Claude Code Memory Compiler
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Cole Medin
commit
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40
.claude/settings.json
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.claude/settings.json
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{
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"hooks": {
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"SessionStart": [
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{
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"matcher": "",
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"hooks": [
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{
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"type": "command",
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"command": "uv run python hooks/session-start.py",
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"timeout": 15
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}
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]
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}
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],
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"PreCompact": [
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{
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"matcher": "",
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"hooks": [
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{
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"type": "command",
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"command": "uv run python hooks/pre-compact.py",
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"timeout": 10
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}
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]
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}
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],
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"SessionEnd": [
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{
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"matcher": "",
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"hooks": [
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{
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"type": "command",
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"command": "uv run python hooks/session-end.py",
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"timeout": 10
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}
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]
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}
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]
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}
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}
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29
.gitignore
vendored
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.gitignore
vendored
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# Python
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__pycache__/
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*.pyc
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.venv/
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# Runtime state (regenerated by scripts)
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scripts/state.json
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scripts/last-flush.json
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scripts/flush.log
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scripts/compile.log
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scripts/session-flush-*
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scripts/flush-context-*
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# Generated content (built up per-user by hooks and scripts)
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daily/
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knowledge/
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# Reports (regenerated by lint)
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reports/
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# uv lock (regenerated by uv sync)
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scripts/uv.lock
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# Claude Code local settings
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.claude/settings.local.json
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# OS files
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.DS_Store
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Thumbs.db
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518
AGENTS.md
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AGENTS.md
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# AGENTS.md - Personal Knowledge Base Schema
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> Adapted from [Andrej Karpathy's LLM Knowledge Base](https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f) architecture.
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> Instead of ingesting external articles, this system compiles knowledge from your own AI conversations.
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## The Compiler Analogy
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```
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daily/ = source code (your conversations - the raw material)
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LLM = compiler (extracts and organizes knowledge)
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knowledge/ = executable (structured, queryable knowledge base)
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lint = test suite (health checks for consistency)
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queries = runtime (using the knowledge)
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```
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You don't manually organize your knowledge. You have conversations, and the LLM handles the synthesis, cross-referencing, and maintenance.
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---
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## Architecture
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### Layer 1: `daily/` - Conversation Logs (Immutable Source)
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Daily logs capture what happened in your AI coding sessions. These are the "raw sources" - append-only, never edited after the fact.
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```
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daily/
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├── 2026-04-01.md
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├── 2026-04-02.md
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├── ...
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```
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Each file follows this format:
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```markdown
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# Daily Log: YYYY-MM-DD
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## Sessions
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### Session (HH:MM) - Brief Title
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**Context:** What the user was working on.
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**Key Exchanges:**
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- User asked about X, assistant explained Y
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- Decided to use Z approach because...
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- Discovered that W doesn't work when...
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**Decisions Made:**
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- Chose library X over Y because...
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- Architecture: went with pattern Z
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**Lessons Learned:**
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- Always do X before Y to avoid...
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- The gotcha with Z is that...
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**Action Items:**
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- [ ] Follow up on X
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- [ ] Refactor Y when time permits
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```
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### Layer 2: `knowledge/` - Compiled Knowledge (LLM-Owned)
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The LLM owns this directory entirely. Humans read it but rarely edit it directly.
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```
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knowledge/
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├── index.md # Master catalog - every article with one-line summary
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├── log.md # Append-only chronological build log
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├── concepts/ # Atomic knowledge articles
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├── connections/ # Cross-cutting insights linking 2+ concepts
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└── qa/ # Filed query answers (compounding knowledge)
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```
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### Layer 3: This File (AGENTS.md)
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The schema that tells the LLM how to compile and maintain the knowledge base. This is the "compiler specification."
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---
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## Structural Files
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### `knowledge/index.md` - Master Catalog
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A table listing every knowledge article. This is the primary retrieval mechanism - the LLM reads this FIRST when answering any query, then selects relevant articles to read in full.
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Format:
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```markdown
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# Knowledge Base Index
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| Article | Summary | Compiled From | Updated |
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|---------|---------|---------------|---------|
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| [[concepts/supabase-auth]] | Row-level security patterns and JWT gotchas | daily/2026-04-02.md | 2026-04-02 |
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| [[connections/auth-and-webhooks]] | Token verification patterns shared across Supabase auth and Stripe webhooks | daily/2026-04-02.md, daily/2026-04-04.md | 2026-04-04 |
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```
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### `knowledge/log.md` - Build Log
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Append-only chronological record of every compile, query, and lint operation.
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Format:
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```markdown
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# Build Log
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## [2026-04-01T14:30:00] compile | Daily Log 2026-04-01
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- Source: daily/2026-04-01.md
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- Articles created: [[concepts/nextjs-project-structure]], [[concepts/tailwind-setup]]
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- Articles updated: (none)
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## [2026-04-02T09:00:00] query | "How do I handle auth redirects?"
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- Consulted: [[concepts/supabase-auth]], [[concepts/nextjs-middleware]]
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- Filed to: [[qa/auth-redirect-handling]]
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```
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---
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## Article Formats
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### Concept Articles (`knowledge/concepts/`)
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One article per atomic piece of knowledge. These are facts, patterns, decisions, preferences, and lessons extracted from your conversations.
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```markdown
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---
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title: "Concept Name"
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aliases: [alternate-name, abbreviation]
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tags: [domain, topic]
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sources:
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- "daily/2026-04-01.md"
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- "daily/2026-04-03.md"
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created: 2026-04-01
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updated: 2026-04-03
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---
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# Concept Name
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[2-4 sentence core explanation]
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## Key Points
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- [Bullet points, each self-contained]
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## Details
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[Deeper explanation, encyclopedia-style paragraphs]
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## Related Concepts
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- [[concepts/related-concept]] - How it connects
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## Sources
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- [[daily/2026-04-01.md]] - Initial discovery during project setup
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- [[daily/2026-04-03.md]] - Updated after debugging session
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```
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### Connection Articles (`knowledge/connections/`)
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Cross-cutting synthesis linking 2+ concepts. Created when a conversation reveals a non-obvious relationship.
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```markdown
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---
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title: "Connection: X and Y"
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connects:
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- "concepts/concept-x"
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- "concepts/concept-y"
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sources:
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- "daily/2026-04-04.md"
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created: 2026-04-04
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updated: 2026-04-04
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---
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# Connection: X and Y
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## The Connection
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[What links these concepts]
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## Key Insight
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[The non-obvious relationship discovered]
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## Evidence
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[Specific examples from conversations]
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## Related Concepts
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- [[concepts/concept-x]]
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- [[concepts/concept-y]]
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```
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### Q&A Articles (`knowledge/qa/`)
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Filed answers from queries. Every complex question answered by the system can be permanently stored, making future queries smarter.
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```markdown
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---
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title: "Q: Original Question"
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question: "The exact question asked"
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consulted:
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- "concepts/article-1"
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- "concepts/article-2"
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filed: 2026-04-05
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---
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# Q: Original Question
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## Answer
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[The synthesized answer with [[wikilinks]] to sources]
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## Sources Consulted
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- [[concepts/article-1]] - Relevant because...
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- [[concepts/article-2]] - Provided context on...
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## Follow-Up Questions
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- What about edge case X?
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- How does this change if Y?
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```
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---
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## Core Operations
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### 1. Compile (daily/ -> knowledge/)
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When processing a daily log:
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1. Read the daily log file
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2. Read `knowledge/index.md` to understand current knowledge state
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3. Read existing articles that may need updating
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4. For each piece of knowledge found in the log:
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- If an existing concept article covers this topic: UPDATE it with new information, add the daily log as a source
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- If it's a new topic: CREATE a new `concepts/` article
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5. If the log reveals a non-obvious connection between 2+ existing concepts: CREATE a `connections/` article
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6. UPDATE `knowledge/index.md` with new/modified entries
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7. APPEND to `knowledge/log.md`
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**Important guidelines:**
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- A single daily log may touch 3-10 knowledge articles
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- Prefer updating existing articles over creating near-duplicates
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- Use Obsidian-style `[[wikilinks]]` with full relative paths from knowledge/
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- Write in encyclopedia style - factual, concise, self-contained
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- Every article must have YAML frontmatter
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- Every article must link back to its source daily logs
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### 2. Query (Ask the Knowledge Base)
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1. Read `knowledge/index.md` (the master catalog)
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2. Based on the question, identify 3-10 relevant articles from the index
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3. Read those articles in full
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4. Synthesize an answer with `[[wikilink]]` citations
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5. If `--file-back` is specified: create a `knowledge/qa/` article and update index.md and log.md
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**Why this works without RAG:** At personal knowledge base scale (50-500 articles), the LLM reading a structured index outperforms cosine similarity. The LLM understands what the question is really asking and selects pages accordingly. Embeddings find similar words; the LLM finds relevant concepts.
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### 3. Lint (Health Checks)
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Seven checks, run periodically:
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1. **Broken links** - `[[wikilinks]]` pointing to non-existent articles
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2. **Orphan pages** - Articles with zero inbound links from other articles
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3. **Orphan sources** - Daily logs that haven't been compiled yet
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4. **Stale articles** - Source daily log changed since article was last compiled
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5. **Contradictions** - Conflicting claims across articles (requires LLM judgment)
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6. **Missing backlinks** - A links to B but B doesn't link back to A
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7. **Sparse articles** - Below 200 words, likely incomplete
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Output: a markdown report with severity levels (error, warning, suggestion).
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---
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## Conventions
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- **Wikilinks:** Use Obsidian-style `[[path/to/article]]` without `.md` extension
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- **Writing style:** Encyclopedia-style, factual, third-person where appropriate
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- **Dates:** ISO 8601 (YYYY-MM-DD for dates, full ISO for timestamps in log.md)
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- **File naming:** lowercase, hyphens for spaces (e.g., `supabase-row-level-security.md`)
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- **Frontmatter:** Every article must have YAML frontmatter with at minimum: title, sources, created, updated
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- **Sources:** Always link back to the daily log(s) that contributed to an article
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---
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## Full Project Structure
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```
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llm-personal-kb/
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|-- .claude/
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| |-- settings.json # Hook configuration (auto-activates in Claude Code)
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|-- .gitignore # Excludes runtime state, temp files, caches
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|-- AGENTS.md # This file - schema + full technical reference
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|-- README.md # Concise overview + quick start
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|-- pyproject.toml # Dependencies (at root so hooks can find it)
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|-- daily/ # "Source code" - conversation logs (immutable)
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|-- knowledge/ # "Executable" - compiled knowledge (LLM-owned)
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| |-- index.md # Master catalog - THE retrieval mechanism
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| |-- log.md # Append-only build log
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| |-- concepts/ # Atomic knowledge articles
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| |-- connections/ # Cross-cutting insights linking 2+ concepts
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| |-- qa/ # Filed query answers (compounding knowledge)
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|-- scripts/ # CLI tools
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| |-- compile.py # Compile daily logs -> knowledge articles
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| |-- query.py # Ask questions (index-guided, no RAG)
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| |-- lint.py # 7 health checks
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| |-- flush.py # Extract memories from conversations (background)
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| |-- config.py # Path constants
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| |-- utils.py # Shared helpers
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|-- hooks/ # Claude Code hooks
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| |-- session-start.py # Injects knowledge into every session
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| |-- session-end.py # Extracts conversation -> daily log
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| |-- pre-compact.py # Safety net: captures context before compaction
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|-- reports/ # Lint reports (gitignored)
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```
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---
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## Hook System (Automatic Capture)
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Hooks are configured in `.claude/settings.json` and fire automatically when you use Claude Code in this project.
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### `.claude/settings.json` Format
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```json
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{
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"hooks": {
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"SessionStart": [{ "matcher": "", "hooks": [{ "type": "command", "command": "uv run python hooks/session-start.py", "timeout": 15 }] }],
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"PreCompact": [{ "matcher": "", "hooks": [{ "type": "command", "command": "uv run python hooks/pre-compact.py", "timeout": 10 }] }],
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"SessionEnd": [{ "matcher": "", "hooks": [{ "type": "command", "command": "uv run python hooks/session-end.py", "timeout": 10 }] }]
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}
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}
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```
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Commands use simple relative paths from the project root. Empty `matcher` catches all events.
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### Hook Details
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**`session-start.py`** (SessionStart)
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- Pure local I/O, no API calls, runs in under 1 second
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- Reads `knowledge/index.md` and the most recent daily log
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- Outputs JSON to stdout: `{"hookSpecificOutput": {"hookEventName": "SessionStart", "additionalContext": "..."}}`
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- Claude sees the knowledge base index at the start of every session
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- Max context: 20,000 characters
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**`session-end.py`** (SessionEnd)
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- Reads hook input from stdin (JSON with `session_id`, `transcript_path`, `cwd`)
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- Copies the raw JSONL transcript to a temp file (no parsing in the hook - keeps it fast)
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- Spawns `flush.py` as a fully detached background process
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- Recursion guard: exits immediately if `CLAUDE_INVOKED_BY` env var is set
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**`pre-compact.py`** (PreCompact)
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- Same architecture as session-end.py
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- Fires before Claude Code auto-compacts the context window
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- Guards against empty `transcript_path` (known Claude Code bug #13668)
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- Critical for long sessions: captures context before summarization discards it
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**Why both PreCompact and SessionEnd?** Long-running sessions may trigger multiple auto-compactions before you close the session. Without PreCompact, intermediate context is lost to summarization before SessionEnd ever fires.
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### Background Flush Process (`flush.py`)
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Spawned by both hooks as a fully detached background process:
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- **Windows:** `CREATE_NEW_PROCESS_GROUP | DETACHED_PROCESS` flags
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- **Mac/Linux:** `start_new_session=True`
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This ensures flush.py survives after Claude Code's hook process exits.
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**What flush.py does:**
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1. Sets `CLAUDE_INVOKED_BY=memory_flush` env var (prevents recursive hook firing)
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2. Reads the pre-extracted conversation context from the temp `.md` file
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3. Skips if context is empty or if same session was flushed within 60 seconds (deduplication)
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4. Calls Claude Agent SDK (`query()` with `allowed_tools=[]`, `max_turns=2`)
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5. Claude decides what's worth saving - returns structured bullet points or `FLUSH_OK`
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6. Appends result to `daily/YYYY-MM-DD.md`
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7. Cleans up temp context file
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8. **End-of-day auto-compilation:** If it's past 6 PM local time (`COMPILE_AFTER_HOUR = 18`) and today's daily log has changed since its last compilation (hash comparison against `state.json`), spawns `compile.py` as another detached background process. This means compilation happens automatically once a day without needing a cron job or manual trigger.
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### JSONL Transcript Format
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Claude Code stores conversations as `.jsonl` files. Messages are nested under a `message` key:
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```python
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entry = json.loads(line)
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msg = entry.get("message", {})
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role = msg.get("role", "") # "user" or "assistant"
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content = msg.get("content", "") # string or list of content blocks
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```
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Content can be a string or a list of blocks (`{"type": "text", "text": "..."}` dicts).
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---
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## Script Details
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||||
### compile.py - The Compiler
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Uses the Claude Agent SDK's async streaming `query()`:
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```python
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async for message in query(
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prompt=compile_prompt,
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options=ClaudeAgentOptions(
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cwd=str(ROOT_DIR),
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system_prompt={"type": "preset", "preset": "claude_code"},
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allowed_tools=["Read", "Write", "Edit", "Glob", "Grep"],
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||||
permission_mode="acceptEdits",
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||||
max_turns=30,
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),
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||||
):
|
||||
```
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||||
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||||
- Builds a prompt with: AGENTS.md schema, current index, all existing articles, and the daily log
|
||||
- Claude reads the daily log, decides what concepts to extract, and writes files directly
|
||||
- `permission_mode="acceptEdits"` auto-approves all file operations
|
||||
- Incremental: tracks SHA-256 hashes of daily logs in `state.json`, skips unchanged files
|
||||
- Cost: ~$0.45-0.65 per daily log (increases as KB grows)
|
||||
|
||||
**CLI:**
|
||||
```bash
|
||||
uv run python scripts/compile.py # compile new/changed only
|
||||
uv run python scripts/compile.py --all # force recompile everything
|
||||
uv run python scripts/compile.py --file daily/2026-04-01.md
|
||||
uv run python scripts/compile.py --dry-run
|
||||
```
|
||||
|
||||
### query.py - Index-Guided Retrieval
|
||||
|
||||
Loads the entire knowledge base into context (index + all articles). No RAG.
|
||||
|
||||
At personal KB scale (50-500 articles), the LLM reading a structured index outperforms vector similarity. The LLM understands what you're really asking; cosine similarity just finds similar words.
|
||||
|
||||
**CLI:**
|
||||
```bash
|
||||
uv run python scripts/query.py "What auth patterns do I use?"
|
||||
uv run python scripts/query.py "What's my error handling strategy?" --file-back
|
||||
```
|
||||
|
||||
With `--file-back`, creates a Q&A article in `knowledge/qa/` and updates the index and log. This is the compounding loop - every question makes the KB smarter.
|
||||
|
||||
### lint.py - Health Checks
|
||||
|
||||
Seven checks:
|
||||
|
||||
| Check | Type | Catches |
|
||||
|-------|------|---------|
|
||||
| Broken links | Structural | `[[wikilinks]]` to non-existent articles |
|
||||
| Orphan pages | Structural | Articles with zero inbound links |
|
||||
| Orphan sources | Structural | Daily logs not yet compiled |
|
||||
| Stale articles | Structural | Source logs changed since compilation |
|
||||
| Missing backlinks | Structural | A links to B but B doesn't link back |
|
||||
| Sparse articles | Structural | Under 200 words |
|
||||
| Contradictions | LLM | Conflicting claims across articles |
|
||||
|
||||
**CLI:**
|
||||
```bash
|
||||
uv run python scripts/lint.py # all checks
|
||||
uv run python scripts/lint.py --structural-only # skip LLM check (free)
|
||||
```
|
||||
|
||||
Reports saved to `reports/lint-YYYY-MM-DD.md`.
|
||||
|
||||
---
|
||||
|
||||
## State Tracking
|
||||
|
||||
`scripts/state.json` tracks:
|
||||
- `ingested` - map of daily log filenames to SHA-256 hashes, compilation timestamps, and costs
|
||||
- `query_count` - total queries run
|
||||
- `last_lint` - timestamp of most recent lint
|
||||
- `total_cost` - cumulative API cost
|
||||
|
||||
`scripts/last-flush.json` tracks flush deduplication (session_id + timestamp).
|
||||
|
||||
Both are gitignored and regenerated automatically.
|
||||
|
||||
---
|
||||
|
||||
## Dependencies
|
||||
|
||||
`pyproject.toml` (at project root):
|
||||
- `claude-agent-sdk>=0.1.29` - Claude Agent SDK for LLM calls with tool use
|
||||
- `python-dotenv>=1.0.0` - Environment variable management
|
||||
- `tzdata>=2024.1` - Timezone data
|
||||
- Python 3.12+, managed by [uv](https://docs.astral.sh/uv/)
|
||||
|
||||
No API key needed - uses Claude Code's built-in credentials at `~/.claude/.credentials.json`.
|
||||
|
||||
---
|
||||
|
||||
## Costs
|
||||
|
||||
| Operation | Cost |
|
||||
|-----------|------|
|
||||
| Compile one daily log | $0.45-0.65 |
|
||||
| Query (no file-back) | ~$0.15-0.25 |
|
||||
| Query (with file-back) | ~$0.25-0.40 |
|
||||
| Full lint (with contradictions) | ~$0.15-0.25 |
|
||||
| Structural lint only | $0.00 |
|
||||
| Memory flush (per session) | ~$0.02-0.05 |
|
||||
|
||||
---
|
||||
|
||||
## Customization
|
||||
|
||||
### Additional Article Types
|
||||
|
||||
Add directories like `people/`, `projects/`, `tools/` to `knowledge/`. Define the article format in this file (AGENTS.md) and update `utils.py`'s `list_wiki_articles()` to include them.
|
||||
|
||||
### Obsidian Integration
|
||||
|
||||
The knowledge base is pure markdown with `[[wikilinks]]` - works natively in Obsidian. Point a vault at `knowledge/` for graph view, backlinks, and search.
|
||||
|
||||
### Scaling Beyond Index-Guided Retrieval
|
||||
|
||||
At ~2,000+ articles / ~2M+ tokens, the index becomes too large for the context window. At that point, add hybrid RAG (keyword + semantic search) as a retrieval layer before the LLM. See Karpathy's recommendation of `qmd` by Tobi Lutke for search at scale.
|
||||
52
README.md
Normal file
52
README.md
Normal file
|
|
@ -0,0 +1,52 @@
|
|||
# LLM Personal Knowledge Base
|
||||
|
||||
**Your AI conversations compile themselves into a searchable knowledge base.**
|
||||
|
||||
Adapted from [Karpathy's LLM Knowledge Base](https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f) architecture, but instead of clipping web articles, the raw data is your own conversations with Claude Code. When a session ends (or auto-compacts mid-session), Claude Code hooks capture the conversation transcript and spawn a background process that uses the [Claude Agent SDK](https://github.com/anthropics/claude-agent-sdk) to extract the important stuff - decisions, lessons learned, patterns, gotchas - and appends it to a daily log. You then compile those daily logs into structured, cross-referenced knowledge articles organized by concept. Retrieval uses a simple index file instead of RAG - no vector database, no embeddings, just markdown.
|
||||
|
||||
Anthropic has clarified that personal use of the Claude Agent SDK is covered under your existing Claude subscription (Max, Team, or Enterprise) - no separate API credits needed. Unlike OpenClaw, which requires API billing for its memory flush, this runs on your subscription.
|
||||
|
||||
## Quick Start
|
||||
|
||||
Tell your AI coding agent:
|
||||
|
||||
> "Clone https://github.com/coleam00/llm-personal-kb into this project. Set up the Claude Code hooks so my conversations automatically get captured into daily logs, compiled into a knowledge base, and injected back into future sessions. Read the AGENTS.md for the full technical reference on how everything works."
|
||||
|
||||
The agent will:
|
||||
1. Clone the repo and run `uv sync` to install dependencies
|
||||
2. Copy `.claude/settings.json` into your project (or merge the hooks into your existing settings)
|
||||
3. The hooks activate automatically next time you open Claude Code
|
||||
|
||||
From there, your conversations start accumulating. After 6 PM local time, the next session flush automatically triggers compilation of that day's logs into knowledge articles. You can also run `uv run python scripts/compile.py` manually at any time.
|
||||
|
||||
## How It Works
|
||||
|
||||
```
|
||||
Conversation -> SessionEnd/PreCompact hooks -> flush.py extracts knowledge
|
||||
-> daily/YYYY-MM-DD.md -> compile.py -> knowledge/concepts/, connections/, qa/
|
||||
-> SessionStart hook injects index into next session -> cycle repeats
|
||||
```
|
||||
|
||||
- **Hooks** capture conversations automatically (session end + pre-compaction safety net)
|
||||
- **flush.py** calls the Claude Agent SDK to decide what's worth saving, and after 6 PM triggers end-of-day compilation automatically
|
||||
- **compile.py** turns daily logs into organized concept articles with cross-references (triggered automatically or run manually)
|
||||
- **query.py** answers questions using index-guided retrieval (no RAG needed at personal scale)
|
||||
- **lint.py** runs 7 health checks (broken links, orphans, contradictions, staleness)
|
||||
|
||||
## Key Commands
|
||||
|
||||
```bash
|
||||
uv run python scripts/compile.py # compile new daily logs
|
||||
uv run python scripts/query.py "question" # ask the knowledge base
|
||||
uv run python scripts/query.py "question" --file-back # ask + save answer back
|
||||
uv run python scripts/lint.py # run health checks
|
||||
uv run python scripts/lint.py --structural-only # free structural checks only
|
||||
```
|
||||
|
||||
## Why No RAG?
|
||||
|
||||
Karpathy's insight: at personal scale (50-500 articles), the LLM reading a structured `index.md` outperforms vector similarity. The LLM understands what you're really asking; cosine similarity just finds similar words. RAG becomes necessary at ~2,000+ articles when the index exceeds the context window.
|
||||
|
||||
## Technical Reference
|
||||
|
||||
See **[AGENTS.md](AGENTS.md)** for the complete technical reference: article formats, hook architecture, script internals, cross-platform details, costs, and customization options. AGENTS.md is designed to give an AI agent everything it needs to understand, modify, or rebuild the system.
|
||||
170
hooks/pre-compact.py
Normal file
170
hooks/pre-compact.py
Normal file
|
|
@ -0,0 +1,170 @@
|
|||
"""
|
||||
PreCompact hook - captures conversation transcript before auto-compaction.
|
||||
|
||||
When Claude Code's context window fills up, it auto-compacts (summarizes and
|
||||
discards detail). This hook fires BEFORE that happens, extracting conversation
|
||||
context and spawning flush.py to extract knowledge that would otherwise
|
||||
be lost to summarization.
|
||||
|
||||
The hook itself does NO API calls - only local file I/O for speed (<10s).
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import logging
|
||||
import os
|
||||
import re
|
||||
import subprocess
|
||||
import sys
|
||||
from datetime import datetime, timezone
|
||||
from pathlib import Path
|
||||
|
||||
# Recursion guard
|
||||
if os.environ.get("CLAUDE_INVOKED_BY"):
|
||||
sys.exit(0)
|
||||
|
||||
ROOT = Path(__file__).resolve().parent.parent
|
||||
SCRIPTS_DIR = ROOT / "scripts"
|
||||
STATE_DIR = SCRIPTS_DIR
|
||||
|
||||
logging.basicConfig(
|
||||
filename=str(SCRIPTS_DIR / "flush.log"),
|
||||
level=logging.INFO,
|
||||
format="%(asctime)s %(levelname)s [pre-compact] %(message)s",
|
||||
datefmt="%Y-%m-%d %H:%M:%S",
|
||||
)
|
||||
|
||||
MAX_TURNS = 30
|
||||
MAX_CONTEXT_CHARS = 15_000
|
||||
MIN_TURNS_TO_FLUSH = 5
|
||||
|
||||
|
||||
def extract_conversation_context(transcript_path: Path) -> tuple[str, int]:
|
||||
"""Read JSONL transcript and extract last ~N conversation turns as markdown."""
|
||||
turns: list[str] = []
|
||||
|
||||
with open(transcript_path, encoding="utf-8") as f:
|
||||
for line in f:
|
||||
line = line.strip()
|
||||
if not line:
|
||||
continue
|
||||
try:
|
||||
entry = json.loads(line)
|
||||
except json.JSONDecodeError:
|
||||
continue
|
||||
|
||||
msg = entry.get("message", {})
|
||||
if isinstance(msg, dict):
|
||||
role = msg.get("role", "")
|
||||
content = msg.get("content", "")
|
||||
else:
|
||||
role = entry.get("role", "")
|
||||
content = entry.get("content", "")
|
||||
|
||||
if role not in ("user", "assistant"):
|
||||
continue
|
||||
|
||||
if isinstance(content, list):
|
||||
text_parts = []
|
||||
for block in content:
|
||||
if isinstance(block, dict) and block.get("type") == "text":
|
||||
text_parts.append(block.get("text", ""))
|
||||
elif isinstance(block, str):
|
||||
text_parts.append(block)
|
||||
content = "\n".join(text_parts)
|
||||
|
||||
if isinstance(content, str) and content.strip():
|
||||
label = "User" if role == "user" else "Assistant"
|
||||
turns.append(f"**{label}:** {content.strip()}\n")
|
||||
|
||||
recent = turns[-MAX_TURNS:]
|
||||
context = "\n".join(recent)
|
||||
|
||||
if len(context) > MAX_CONTEXT_CHARS:
|
||||
context = context[-MAX_CONTEXT_CHARS:]
|
||||
boundary = context.find("\n**")
|
||||
if boundary > 0:
|
||||
context = context[boundary + 1 :]
|
||||
|
||||
return context, len(recent)
|
||||
|
||||
|
||||
def main() -> None:
|
||||
# Read hook input from stdin
|
||||
try:
|
||||
raw_input = sys.stdin.read()
|
||||
try:
|
||||
hook_input: dict = json.loads(raw_input)
|
||||
except json.JSONDecodeError:
|
||||
fixed_input = re.sub(r'(?<!\\)\\(?!["\\])', r'\\\\', raw_input)
|
||||
hook_input = json.loads(fixed_input)
|
||||
except (json.JSONDecodeError, ValueError, EOFError) as e:
|
||||
logging.error("Failed to parse stdin: %s", e)
|
||||
return
|
||||
|
||||
session_id = hook_input.get("session_id", "unknown")
|
||||
transcript_path_str = hook_input.get("transcript_path", "")
|
||||
|
||||
logging.info("PreCompact fired: session=%s", session_id)
|
||||
|
||||
# transcript_path can be empty (known Claude Code bug #13668)
|
||||
if not transcript_path_str or not isinstance(transcript_path_str, str):
|
||||
logging.info("SKIP: no transcript path")
|
||||
return
|
||||
|
||||
transcript_path = Path(transcript_path_str)
|
||||
if not transcript_path.exists():
|
||||
logging.info("SKIP: transcript missing: %s", transcript_path_str)
|
||||
return
|
||||
|
||||
# Extract conversation context in the hook
|
||||
try:
|
||||
context, turn_count = extract_conversation_context(transcript_path)
|
||||
except Exception as e:
|
||||
logging.error("Context extraction failed: %s", e)
|
||||
return
|
||||
|
||||
if not context.strip():
|
||||
logging.info("SKIP: empty context")
|
||||
return
|
||||
|
||||
if turn_count < MIN_TURNS_TO_FLUSH:
|
||||
logging.info("SKIP: only %d turns (min %d)", turn_count, MIN_TURNS_TO_FLUSH)
|
||||
return
|
||||
|
||||
# Write context to a temp file for the background process
|
||||
timestamp = datetime.now(timezone.utc).astimezone().strftime("%Y%m%d-%H%M%S")
|
||||
context_file = STATE_DIR / f"flush-context-{session_id}-{timestamp}.md"
|
||||
context_file.write_text(context, encoding="utf-8")
|
||||
|
||||
# Spawn flush.py as a background process
|
||||
flush_script = SCRIPTS_DIR / "flush.py"
|
||||
|
||||
cmd = [
|
||||
"uv",
|
||||
"run",
|
||||
"--directory",
|
||||
str(ROOT),
|
||||
"python",
|
||||
str(flush_script),
|
||||
str(context_file),
|
||||
session_id,
|
||||
]
|
||||
|
||||
creation_flags = subprocess.CREATE_NO_WINDOW if sys.platform == "win32" else 0
|
||||
|
||||
try:
|
||||
subprocess.Popen(
|
||||
cmd,
|
||||
stdout=subprocess.DEVNULL,
|
||||
stderr=subprocess.DEVNULL,
|
||||
creationflags=creation_flags,
|
||||
)
|
||||
logging.info("Spawned flush.py for session %s (%d turns, %d chars)", session_id, turn_count, len(context))
|
||||
except Exception as e:
|
||||
logging.error("Failed to spawn flush.py: %s", e)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
174
hooks/session-end.py
Normal file
174
hooks/session-end.py
Normal file
|
|
@ -0,0 +1,174 @@
|
|||
"""
|
||||
SessionEnd hook - captures conversation transcript for memory extraction.
|
||||
|
||||
When a Claude Code session ends, this hook reads the transcript path from
|
||||
stdin, extracts conversation context, and spawns flush.py as a background
|
||||
process to extract knowledge into the daily log.
|
||||
|
||||
The hook itself does NO API calls - only local file I/O for speed (<10s).
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import logging
|
||||
import os
|
||||
import re
|
||||
import subprocess
|
||||
import sys
|
||||
from datetime import datetime, timezone
|
||||
from pathlib import Path
|
||||
|
||||
# Recursion guard: if we were spawned by flush.py (which calls Agent SDK,
|
||||
# which runs Claude Code, which would fire this hook again), exit immediately.
|
||||
if os.environ.get("CLAUDE_INVOKED_BY"):
|
||||
sys.exit(0)
|
||||
|
||||
ROOT = Path(__file__).resolve().parent.parent
|
||||
DAILY_DIR = ROOT / "daily"
|
||||
SCRIPTS_DIR = ROOT / "scripts"
|
||||
STATE_DIR = SCRIPTS_DIR
|
||||
|
||||
logging.basicConfig(
|
||||
filename=str(SCRIPTS_DIR / "flush.log"),
|
||||
level=logging.INFO,
|
||||
format="%(asctime)s %(levelname)s [hook] %(message)s",
|
||||
datefmt="%Y-%m-%d %H:%M:%S",
|
||||
)
|
||||
|
||||
MAX_TURNS = 30
|
||||
MAX_CONTEXT_CHARS = 15_000
|
||||
MIN_TURNS_TO_FLUSH = 1
|
||||
|
||||
|
||||
def extract_conversation_context(transcript_path: Path) -> tuple[str, int]:
|
||||
"""Read JSONL transcript and extract last ~N conversation turns as markdown."""
|
||||
turns: list[str] = []
|
||||
|
||||
with open(transcript_path, encoding="utf-8") as f:
|
||||
for line in f:
|
||||
line = line.strip()
|
||||
if not line:
|
||||
continue
|
||||
try:
|
||||
entry = json.loads(line)
|
||||
except json.JSONDecodeError:
|
||||
continue
|
||||
|
||||
msg = entry.get("message", {})
|
||||
if isinstance(msg, dict):
|
||||
role = msg.get("role", "")
|
||||
content = msg.get("content", "")
|
||||
else:
|
||||
role = entry.get("role", "")
|
||||
content = entry.get("content", "")
|
||||
|
||||
if role not in ("user", "assistant"):
|
||||
continue
|
||||
|
||||
if isinstance(content, list):
|
||||
text_parts = []
|
||||
for block in content:
|
||||
if isinstance(block, dict) and block.get("type") == "text":
|
||||
text_parts.append(block.get("text", ""))
|
||||
elif isinstance(block, str):
|
||||
text_parts.append(block)
|
||||
content = "\n".join(text_parts)
|
||||
|
||||
if isinstance(content, str) and content.strip():
|
||||
label = "User" if role == "user" else "Assistant"
|
||||
turns.append(f"**{label}:** {content.strip()}\n")
|
||||
|
||||
recent = turns[-MAX_TURNS:]
|
||||
context = "\n".join(recent)
|
||||
|
||||
if len(context) > MAX_CONTEXT_CHARS:
|
||||
context = context[-MAX_CONTEXT_CHARS:]
|
||||
boundary = context.find("\n**")
|
||||
if boundary > 0:
|
||||
context = context[boundary + 1 :]
|
||||
|
||||
return context, len(recent)
|
||||
|
||||
|
||||
def main() -> None:
|
||||
# Read hook input from stdin
|
||||
# Claude Code on Windows may pass paths with unescaped backslashes
|
||||
try:
|
||||
raw_input = sys.stdin.read()
|
||||
try:
|
||||
hook_input: dict = json.loads(raw_input)
|
||||
except json.JSONDecodeError:
|
||||
fixed_input = re.sub(r'(?<!\\)\\(?!["\\])', r'\\\\', raw_input)
|
||||
hook_input = json.loads(fixed_input)
|
||||
except (json.JSONDecodeError, ValueError, EOFError) as e:
|
||||
logging.error("Failed to parse stdin: %s", e)
|
||||
return
|
||||
|
||||
session_id = hook_input.get("session_id", "unknown")
|
||||
source = hook_input.get("source", "unknown")
|
||||
transcript_path_str = hook_input.get("transcript_path", "")
|
||||
|
||||
logging.info("SessionEnd fired: session=%s source=%s", session_id, source)
|
||||
|
||||
if not transcript_path_str or not isinstance(transcript_path_str, str):
|
||||
logging.info("SKIP: no transcript path")
|
||||
return
|
||||
|
||||
transcript_path = Path(transcript_path_str)
|
||||
if not transcript_path.exists():
|
||||
logging.info("SKIP: transcript missing: %s", transcript_path_str)
|
||||
return
|
||||
|
||||
# Extract conversation context in the hook (fast, no API calls)
|
||||
try:
|
||||
context, turn_count = extract_conversation_context(transcript_path)
|
||||
except Exception as e:
|
||||
logging.error("Context extraction failed: %s", e)
|
||||
return
|
||||
|
||||
if not context.strip():
|
||||
logging.info("SKIP: empty context")
|
||||
return
|
||||
|
||||
if turn_count < MIN_TURNS_TO_FLUSH:
|
||||
logging.info("SKIP: only %d turns (min %d)", turn_count, MIN_TURNS_TO_FLUSH)
|
||||
return
|
||||
|
||||
# Write context to a temp file for the background process
|
||||
timestamp = datetime.now(timezone.utc).astimezone().strftime("%Y%m%d-%H%M%S")
|
||||
context_file = STATE_DIR / f"session-flush-{session_id}-{timestamp}.md"
|
||||
context_file.write_text(context, encoding="utf-8")
|
||||
|
||||
# Spawn flush.py as a background process
|
||||
flush_script = SCRIPTS_DIR / "flush.py"
|
||||
|
||||
cmd = [
|
||||
"uv",
|
||||
"run",
|
||||
"--directory",
|
||||
str(ROOT),
|
||||
"python",
|
||||
str(flush_script),
|
||||
str(context_file),
|
||||
session_id,
|
||||
]
|
||||
|
||||
# On Windows, use CREATE_NO_WINDOW to avoid flash console window.
|
||||
# Do NOT use DETACHED_PROCESS — it breaks the Agent SDK's subprocess I/O.
|
||||
creation_flags = subprocess.CREATE_NO_WINDOW if sys.platform == "win32" else 0
|
||||
|
||||
try:
|
||||
subprocess.Popen(
|
||||
cmd,
|
||||
stdout=subprocess.DEVNULL,
|
||||
stderr=subprocess.DEVNULL,
|
||||
creationflags=creation_flags,
|
||||
)
|
||||
logging.info("Spawned flush.py for session %s (%d turns, %d chars)", session_id, turn_count, len(context))
|
||||
except Exception as e:
|
||||
logging.error("Failed to spawn flush.py: %s", e)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
92
hooks/session-start.py
Normal file
92
hooks/session-start.py
Normal file
|
|
@ -0,0 +1,92 @@
|
|||
"""
|
||||
SessionStart hook - injects knowledge base context into every conversation.
|
||||
|
||||
This is the "context injection" layer. When Claude Code starts a session,
|
||||
this hook reads the knowledge base index and recent daily log, then injects
|
||||
them as additional context so Claude always "remembers" what it has learned.
|
||||
|
||||
Configure in .claude/settings.json:
|
||||
{
|
||||
"hooks": {
|
||||
"SessionStart": [{
|
||||
"matcher": "",
|
||||
"command": "uv run python hooks/session-start.py"
|
||||
}]
|
||||
}
|
||||
}
|
||||
"""
|
||||
|
||||
import json
|
||||
import sys
|
||||
from datetime import datetime, timedelta, timezone
|
||||
from pathlib import Path
|
||||
|
||||
# Paths relative to project root
|
||||
ROOT = Path(__file__).resolve().parent.parent
|
||||
KNOWLEDGE_DIR = ROOT / "knowledge"
|
||||
DAILY_DIR = ROOT / "daily"
|
||||
INDEX_FILE = KNOWLEDGE_DIR / "index.md"
|
||||
|
||||
MAX_CONTEXT_CHARS = 20_000
|
||||
MAX_LOG_LINES = 30
|
||||
|
||||
|
||||
def get_recent_log() -> str:
|
||||
"""Read the most recent daily log (today or yesterday)."""
|
||||
today = datetime.now(timezone.utc).astimezone()
|
||||
|
||||
for offset in range(2):
|
||||
date = today - timedelta(days=offset)
|
||||
log_path = DAILY_DIR / f"{date.strftime('%Y-%m-%d')}.md"
|
||||
if log_path.exists():
|
||||
lines = log_path.read_text(encoding="utf-8").splitlines()
|
||||
# Return last N lines to keep context small
|
||||
recent = lines[-MAX_LOG_LINES:] if len(lines) > MAX_LOG_LINES else lines
|
||||
return "\n".join(recent)
|
||||
|
||||
return "(no recent daily log)"
|
||||
|
||||
|
||||
def build_context() -> str:
|
||||
"""Assemble the context to inject into the conversation."""
|
||||
parts = []
|
||||
|
||||
# Today's date
|
||||
today = datetime.now(timezone.utc).astimezone()
|
||||
parts.append(f"## Today\n{today.strftime('%A, %B %d, %Y')}")
|
||||
|
||||
# Knowledge base index (the core retrieval mechanism)
|
||||
if INDEX_FILE.exists():
|
||||
index_content = INDEX_FILE.read_text(encoding="utf-8")
|
||||
parts.append(f"## Knowledge Base Index\n\n{index_content}")
|
||||
else:
|
||||
parts.append("## Knowledge Base Index\n\n(empty - no articles compiled yet)")
|
||||
|
||||
# Recent daily log
|
||||
recent_log = get_recent_log()
|
||||
parts.append(f"## Recent Daily Log\n\n{recent_log}")
|
||||
|
||||
context = "\n\n---\n\n".join(parts)
|
||||
|
||||
# Truncate if too long
|
||||
if len(context) > MAX_CONTEXT_CHARS:
|
||||
context = context[:MAX_CONTEXT_CHARS] + "\n\n...(truncated)"
|
||||
|
||||
return context
|
||||
|
||||
|
||||
def main():
|
||||
context = build_context()
|
||||
|
||||
output = {
|
||||
"hookSpecificOutput": {
|
||||
"hookEventName": "SessionStart",
|
||||
"additionalContext": context,
|
||||
}
|
||||
}
|
||||
|
||||
print(json.dumps(output))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
13
pyproject.toml
Normal file
13
pyproject.toml
Normal file
|
|
@ -0,0 +1,13 @@
|
|||
[project]
|
||||
name = "llm-personal-kb"
|
||||
version = "0.1.0"
|
||||
description = "Personal knowledge base compiled from AI conversations - inspired by Karpathy's LLM KB architecture"
|
||||
requires-python = ">=3.12"
|
||||
dependencies = [
|
||||
"claude-agent-sdk>=0.1.29",
|
||||
"python-dotenv>=1.0.0",
|
||||
"tzdata>=2024.1",
|
||||
]
|
||||
|
||||
[tool.ruff]
|
||||
line-length = 100
|
||||
224
scripts/compile.py
Normal file
224
scripts/compile.py
Normal file
|
|
@ -0,0 +1,224 @@
|
|||
"""
|
||||
Compile daily conversation logs into structured knowledge articles.
|
||||
|
||||
This is the "LLM compiler" - it reads daily logs (source code) and produces
|
||||
organized knowledge articles (the executable).
|
||||
|
||||
Usage:
|
||||
uv run python compile.py # compile new/changed logs only
|
||||
uv run python compile.py --all # force recompile everything
|
||||
uv run python compile.py --file daily/2026-04-01.md # compile a specific log
|
||||
uv run python compile.py --dry-run # show what would be compiled
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import asyncio
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
from config import AGENTS_FILE, CONCEPTS_DIR, CONNECTIONS_DIR, DAILY_DIR, KNOWLEDGE_DIR, now_iso
|
||||
from utils import (
|
||||
file_hash,
|
||||
list_raw_files,
|
||||
list_wiki_articles,
|
||||
load_state,
|
||||
read_wiki_index,
|
||||
save_state,
|
||||
)
|
||||
|
||||
# ── Paths for the LLM to use ──────────────────────────────────────────
|
||||
ROOT_DIR = Path(__file__).resolve().parent.parent
|
||||
|
||||
|
||||
async def compile_daily_log(log_path: Path, state: dict) -> float:
|
||||
"""Compile a single daily log into knowledge articles.
|
||||
|
||||
Returns the API cost of the compilation.
|
||||
"""
|
||||
from claude_agent_sdk import (
|
||||
AssistantMessage,
|
||||
ClaudeAgentOptions,
|
||||
ResultMessage,
|
||||
TextBlock,
|
||||
query,
|
||||
)
|
||||
|
||||
log_content = log_path.read_text(encoding="utf-8")
|
||||
schema = AGENTS_FILE.read_text(encoding="utf-8")
|
||||
wiki_index = read_wiki_index()
|
||||
|
||||
# Read existing articles for context
|
||||
existing_articles_context = ""
|
||||
existing = {}
|
||||
for article_path in list_wiki_articles():
|
||||
rel = article_path.relative_to(KNOWLEDGE_DIR)
|
||||
existing[str(rel)] = article_path.read_text(encoding="utf-8")
|
||||
|
||||
if existing:
|
||||
parts = []
|
||||
for rel_path, content in existing.items():
|
||||
parts.append(f"### {rel_path}\n```markdown\n{content}\n```")
|
||||
existing_articles_context = "\n\n".join(parts)
|
||||
|
||||
timestamp = now_iso()
|
||||
|
||||
prompt = f"""You are a knowledge compiler. Your job is to read a daily conversation log
|
||||
and extract knowledge into structured wiki articles.
|
||||
|
||||
## Schema (AGENTS.md)
|
||||
|
||||
{schema}
|
||||
|
||||
## Current Wiki Index
|
||||
|
||||
{wiki_index}
|
||||
|
||||
## Existing Wiki Articles
|
||||
|
||||
{existing_articles_context if existing_articles_context else "(No existing articles yet)"}
|
||||
|
||||
## Daily Log to Compile
|
||||
|
||||
**File:** {log_path.name}
|
||||
|
||||
{log_content}
|
||||
|
||||
## Your Task
|
||||
|
||||
Read the daily log above and compile it into wiki articles following the schema exactly.
|
||||
|
||||
### Rules:
|
||||
|
||||
1. **Extract key concepts** - Identify 3-7 distinct concepts worth their own article
|
||||
2. **Create concept articles** in `knowledge/concepts/` - One .md file per concept
|
||||
- Use the exact article format from AGENTS.md (YAML frontmatter + sections)
|
||||
- Include `sources:` in frontmatter pointing to the daily log file
|
||||
- Use `[[concepts/slug]]` wikilinks to link to related concepts
|
||||
- Write in encyclopedia style - neutral, comprehensive
|
||||
3. **Create connection articles** in `knowledge/connections/` if this log reveals non-obvious
|
||||
relationships between 2+ existing concepts
|
||||
4. **Update existing articles** if this log adds new information to concepts already in the wiki
|
||||
- Read the existing article, add the new information, add the source to frontmatter
|
||||
5. **Update knowledge/index.md** - Add new entries to the table
|
||||
- Each entry: `| [[path/slug]] | One-line summary | source-file | {timestamp[:10]} |`
|
||||
6. **Append to knowledge/log.md** - Add a timestamped entry:
|
||||
```
|
||||
## [{timestamp}] compile | {log_path.name}
|
||||
- Source: daily/{log_path.name}
|
||||
- Articles created: [[concepts/x]], [[concepts/y]]
|
||||
- Articles updated: [[concepts/z]] (if any)
|
||||
```
|
||||
|
||||
### File paths:
|
||||
- Write concept articles to: {CONCEPTS_DIR}
|
||||
- Write connection articles to: {CONNECTIONS_DIR}
|
||||
- Update index at: {KNOWLEDGE_DIR / 'index.md'}
|
||||
- Append log at: {KNOWLEDGE_DIR / 'log.md'}
|
||||
|
||||
### Quality standards:
|
||||
- Every article must have complete YAML frontmatter
|
||||
- Every article must link to at least 2 other articles via [[wikilinks]]
|
||||
- Key Points section should have 3-5 bullet points
|
||||
- Details section should have 2+ paragraphs
|
||||
- Related Concepts section should have 2+ entries
|
||||
- Sources section should cite the daily log with specific claims extracted
|
||||
"""
|
||||
|
||||
cost = 0.0
|
||||
|
||||
try:
|
||||
async for message in query(
|
||||
prompt=prompt,
|
||||
options=ClaudeAgentOptions(
|
||||
cwd=str(ROOT_DIR),
|
||||
system_prompt={"type": "preset", "preset": "claude_code"},
|
||||
allowed_tools=["Read", "Write", "Edit", "Glob", "Grep"],
|
||||
permission_mode="acceptEdits",
|
||||
max_turns=30,
|
||||
),
|
||||
):
|
||||
if isinstance(message, AssistantMessage):
|
||||
for block in message.content:
|
||||
if isinstance(block, TextBlock):
|
||||
pass # compilation output - LLM writes files directly
|
||||
elif isinstance(message, ResultMessage):
|
||||
cost = message.total_cost_usd or 0.0
|
||||
print(f" Cost: ${cost:.4f}")
|
||||
except Exception as e:
|
||||
print(f" Error: {e}")
|
||||
return 0.0
|
||||
|
||||
# Update state
|
||||
rel_path = log_path.name
|
||||
state.setdefault("ingested", {})[rel_path] = {
|
||||
"hash": file_hash(log_path),
|
||||
"compiled_at": now_iso(),
|
||||
"cost_usd": cost,
|
||||
}
|
||||
state["total_cost"] = state.get("total_cost", 0.0) + cost
|
||||
save_state(state)
|
||||
|
||||
return cost
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(description="Compile daily logs into knowledge articles")
|
||||
parser.add_argument("--all", action="store_true", help="Force recompile all logs")
|
||||
parser.add_argument("--file", type=str, help="Compile a specific daily log file")
|
||||
parser.add_argument("--dry-run", action="store_true", help="Show what would be compiled")
|
||||
args = parser.parse_args()
|
||||
|
||||
state = load_state()
|
||||
|
||||
# Determine which files to compile
|
||||
if args.file:
|
||||
target = Path(args.file)
|
||||
if not target.is_absolute():
|
||||
target = DAILY_DIR / target.name
|
||||
if not target.exists():
|
||||
# Try resolving relative to project root
|
||||
target = ROOT_DIR / args.file
|
||||
if not target.exists():
|
||||
print(f"Error: {args.file} not found")
|
||||
sys.exit(1)
|
||||
to_compile = [target]
|
||||
else:
|
||||
all_logs = list_raw_files()
|
||||
if args.all:
|
||||
to_compile = all_logs
|
||||
else:
|
||||
to_compile = []
|
||||
for log_path in all_logs:
|
||||
rel = log_path.name
|
||||
prev = state.get("ingested", {}).get(rel, {})
|
||||
if not prev or prev.get("hash") != file_hash(log_path):
|
||||
to_compile.append(log_path)
|
||||
|
||||
if not to_compile:
|
||||
print("Nothing to compile - all daily logs are up to date.")
|
||||
return
|
||||
|
||||
print(f"{'[DRY RUN] ' if args.dry_run else ''}Files to compile ({len(to_compile)}):")
|
||||
for f in to_compile:
|
||||
print(f" - {f.name}")
|
||||
|
||||
if args.dry_run:
|
||||
return
|
||||
|
||||
# Compile each file sequentially
|
||||
total_cost = 0.0
|
||||
for i, log_path in enumerate(to_compile, 1):
|
||||
print(f"\n[{i}/{len(to_compile)}] Compiling {log_path.name}...")
|
||||
cost = asyncio.run(compile_daily_log(log_path, state))
|
||||
total_cost += cost
|
||||
print(f" Done.")
|
||||
|
||||
articles = list_wiki_articles()
|
||||
print(f"\nCompilation complete. Total cost: ${total_cost:.2f}")
|
||||
print(f"Knowledge base: {len(articles)} articles")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
33
scripts/config.py
Normal file
33
scripts/config.py
Normal file
|
|
@ -0,0 +1,33 @@
|
|||
"""Path constants and configuration for the personal knowledge base."""
|
||||
|
||||
from pathlib import Path
|
||||
from datetime import datetime, timezone
|
||||
|
||||
# ── Paths ──────────────────────────────────────────────────────────────
|
||||
ROOT_DIR = Path(__file__).resolve().parent.parent
|
||||
DAILY_DIR = ROOT_DIR / "daily"
|
||||
KNOWLEDGE_DIR = ROOT_DIR / "knowledge"
|
||||
CONCEPTS_DIR = KNOWLEDGE_DIR / "concepts"
|
||||
CONNECTIONS_DIR = KNOWLEDGE_DIR / "connections"
|
||||
QA_DIR = KNOWLEDGE_DIR / "qa"
|
||||
REPORTS_DIR = ROOT_DIR / "reports"
|
||||
SCRIPTS_DIR = ROOT_DIR / "scripts"
|
||||
HOOKS_DIR = ROOT_DIR / "hooks"
|
||||
AGENTS_FILE = ROOT_DIR / "AGENTS.md"
|
||||
|
||||
INDEX_FILE = KNOWLEDGE_DIR / "index.md"
|
||||
LOG_FILE = KNOWLEDGE_DIR / "log.md"
|
||||
STATE_FILE = SCRIPTS_DIR / "state.json"
|
||||
|
||||
# ── Timezone ───────────────────────────────────────────────────────────
|
||||
TIMEZONE = "America/Chicago"
|
||||
|
||||
|
||||
def now_iso() -> str:
|
||||
"""Current time in ISO 8601 format."""
|
||||
return datetime.now(timezone.utc).astimezone().isoformat(timespec="seconds")
|
||||
|
||||
|
||||
def today_iso() -> str:
|
||||
"""Current date in ISO 8601 format."""
|
||||
return datetime.now(timezone.utc).astimezone().strftime("%Y-%m-%d")
|
||||
255
scripts/flush.py
Normal file
255
scripts/flush.py
Normal file
|
|
@ -0,0 +1,255 @@
|
|||
"""
|
||||
Memory flush agent - extracts important knowledge from conversation context.
|
||||
|
||||
Spawned by session-end.py or pre-compact.py as a background process. Reads
|
||||
pre-extracted conversation context from a .md file, uses the Claude Agent SDK
|
||||
to decide what's worth saving, and appends the result to today's daily log.
|
||||
|
||||
Usage:
|
||||
uv run python flush.py <context_file.md> <session_id>
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
# Recursion prevention: set this BEFORE any imports that might trigger Claude
|
||||
import os
|
||||
os.environ["CLAUDE_INVOKED_BY"] = "memory_flush"
|
||||
|
||||
import asyncio
|
||||
import json
|
||||
import logging
|
||||
import sys
|
||||
import time
|
||||
from datetime import datetime, timezone
|
||||
from pathlib import Path
|
||||
|
||||
ROOT = Path(__file__).resolve().parent.parent
|
||||
DAILY_DIR = ROOT / "daily"
|
||||
SCRIPTS_DIR = ROOT / "scripts"
|
||||
STATE_FILE = SCRIPTS_DIR / "last-flush.json"
|
||||
LOG_FILE = SCRIPTS_DIR / "flush.log"
|
||||
|
||||
# Set up file-based logging so we can verify the background process ran.
|
||||
# The parent process sends stdout/stderr to DEVNULL (to avoid the inherited
|
||||
# file handle bug on Windows), so this is our only observability channel.
|
||||
logging.basicConfig(
|
||||
filename=str(LOG_FILE),
|
||||
level=logging.INFO,
|
||||
format="%(asctime)s %(levelname)s %(message)s",
|
||||
datefmt="%Y-%m-%d %H:%M:%S",
|
||||
)
|
||||
|
||||
|
||||
def load_flush_state() -> dict:
|
||||
if STATE_FILE.exists():
|
||||
try:
|
||||
return json.loads(STATE_FILE.read_text(encoding="utf-8"))
|
||||
except (json.JSONDecodeError, OSError):
|
||||
pass
|
||||
return {}
|
||||
|
||||
|
||||
def save_flush_state(state: dict) -> None:
|
||||
STATE_FILE.write_text(json.dumps(state), encoding="utf-8")
|
||||
|
||||
|
||||
def append_to_daily_log(content: str, section: str = "Session") -> None:
|
||||
"""Append content to today's daily log."""
|
||||
today = datetime.now(timezone.utc).astimezone()
|
||||
log_path = DAILY_DIR / f"{today.strftime('%Y-%m-%d')}.md"
|
||||
|
||||
if not log_path.exists():
|
||||
DAILY_DIR.mkdir(parents=True, exist_ok=True)
|
||||
log_path.write_text(
|
||||
f"# Daily Log: {today.strftime('%Y-%m-%d')}\n\n## Sessions\n\n## Memory Maintenance\n\n",
|
||||
encoding="utf-8",
|
||||
)
|
||||
|
||||
time_str = today.strftime("%H:%M")
|
||||
entry = f"### {section} ({time_str})\n\n{content}\n\n"
|
||||
|
||||
with open(log_path, "a", encoding="utf-8") as f:
|
||||
f.write(entry)
|
||||
|
||||
|
||||
async def run_flush(context: str) -> str:
|
||||
"""Use Claude Agent SDK to extract important knowledge from conversation context."""
|
||||
from claude_agent_sdk import (
|
||||
AssistantMessage,
|
||||
ClaudeAgentOptions,
|
||||
ResultMessage,
|
||||
TextBlock,
|
||||
query,
|
||||
)
|
||||
|
||||
prompt = f"""Review the conversation context below and respond with a concise summary
|
||||
of important items that should be preserved in the daily log.
|
||||
Do NOT use any tools — just return plain text.
|
||||
|
||||
Format your response as a structured daily log entry with these sections:
|
||||
|
||||
**Context:** [One line about what the user was working on]
|
||||
|
||||
**Key Exchanges:**
|
||||
- [Important Q&A or discussions]
|
||||
|
||||
**Decisions Made:**
|
||||
- [Any decisions with rationale]
|
||||
|
||||
**Lessons Learned:**
|
||||
- [Gotchas, patterns, or insights discovered]
|
||||
|
||||
**Action Items:**
|
||||
- [Follow-ups or TODOs mentioned]
|
||||
|
||||
Skip anything that is:
|
||||
- Routine tool calls or file reads
|
||||
- Content that's trivial or obvious
|
||||
- Trivial back-and-forth or clarification exchanges
|
||||
|
||||
Only include sections that have actual content. If nothing is worth saving,
|
||||
respond with exactly: FLUSH_OK
|
||||
|
||||
## Conversation Context
|
||||
|
||||
{context}"""
|
||||
|
||||
response = ""
|
||||
|
||||
try:
|
||||
async for message in query(
|
||||
prompt=prompt,
|
||||
options=ClaudeAgentOptions(
|
||||
cwd=str(ROOT),
|
||||
allowed_tools=[],
|
||||
max_turns=2,
|
||||
),
|
||||
):
|
||||
if isinstance(message, AssistantMessage):
|
||||
for block in message.content:
|
||||
if isinstance(block, TextBlock):
|
||||
response += block.text
|
||||
elif isinstance(message, ResultMessage):
|
||||
pass
|
||||
except Exception as e:
|
||||
import traceback
|
||||
logging.error("Agent SDK error: %s\n%s", e, traceback.format_exc())
|
||||
response = f"FLUSH_ERROR: {type(e).__name__}: {e}"
|
||||
|
||||
return response
|
||||
|
||||
|
||||
COMPILE_AFTER_HOUR = 18 # 6 PM local time
|
||||
|
||||
|
||||
def maybe_trigger_compilation() -> None:
|
||||
"""If it's past the compile hour and today's log hasn't been compiled, run compile.py."""
|
||||
import subprocess as _sp
|
||||
|
||||
now = datetime.now(timezone.utc).astimezone()
|
||||
if now.hour < COMPILE_AFTER_HOUR:
|
||||
return
|
||||
|
||||
# Check if today's log has already been compiled
|
||||
today_log = f"{now.strftime('%Y-%m-%d')}.md"
|
||||
compile_state_file = SCRIPTS_DIR / "state.json"
|
||||
if compile_state_file.exists():
|
||||
try:
|
||||
compile_state = json.loads(compile_state_file.read_text(encoding="utf-8"))
|
||||
ingested = compile_state.get("ingested", {})
|
||||
if today_log in ingested:
|
||||
# Already compiled today - check if the log has changed since
|
||||
from hashlib import sha256
|
||||
log_path = DAILY_DIR / today_log
|
||||
if log_path.exists():
|
||||
current_hash = sha256(log_path.read_bytes()).hexdigest()[:16]
|
||||
if ingested[today_log].get("hash") == current_hash:
|
||||
return # log unchanged since last compile
|
||||
except (json.JSONDecodeError, OSError):
|
||||
pass
|
||||
|
||||
compile_script = SCRIPTS_DIR / "compile.py"
|
||||
if not compile_script.exists():
|
||||
return
|
||||
|
||||
logging.info("End-of-day compilation triggered (after %d:00)", COMPILE_AFTER_HOUR)
|
||||
|
||||
cmd = ["uv", "run", "--directory", str(ROOT), "python", str(compile_script)]
|
||||
|
||||
kwargs: dict = {}
|
||||
if sys.platform == "win32":
|
||||
kwargs["creationflags"] = _sp.CREATE_NEW_PROCESS_GROUP | _sp.DETACHED_PROCESS
|
||||
else:
|
||||
kwargs["start_new_session"] = True
|
||||
|
||||
try:
|
||||
log_handle = open(str(SCRIPTS_DIR / "compile.log"), "a")
|
||||
_sp.Popen(cmd, stdout=log_handle, stderr=_sp.STDOUT, cwd=str(ROOT), **kwargs)
|
||||
except Exception as e:
|
||||
logging.error("Failed to spawn compile.py: %s", e)
|
||||
|
||||
|
||||
def main():
|
||||
if len(sys.argv) < 3:
|
||||
logging.error("Usage: %s <context_file.md> <session_id>", sys.argv[0])
|
||||
sys.exit(1)
|
||||
|
||||
context_file = Path(sys.argv[1])
|
||||
session_id = sys.argv[2]
|
||||
|
||||
logging.info("flush.py started for session %s, context: %s", session_id, context_file)
|
||||
|
||||
if not context_file.exists():
|
||||
logging.error("Context file not found: %s", context_file)
|
||||
return
|
||||
|
||||
# Deduplication: skip if same session was flushed within 60 seconds
|
||||
state = load_flush_state()
|
||||
if (
|
||||
state.get("session_id") == session_id
|
||||
and time.time() - state.get("timestamp", 0) < 60
|
||||
):
|
||||
logging.info("Skipping duplicate flush for session %s", session_id)
|
||||
context_file.unlink(missing_ok=True)
|
||||
return
|
||||
|
||||
# Read pre-extracted context
|
||||
context = context_file.read_text(encoding="utf-8").strip()
|
||||
if not context:
|
||||
logging.info("Context file is empty, skipping")
|
||||
context_file.unlink(missing_ok=True)
|
||||
return
|
||||
|
||||
logging.info("Flushing session %s: %d chars", session_id, len(context))
|
||||
|
||||
# Run the LLM extraction
|
||||
response = asyncio.run(run_flush(context))
|
||||
|
||||
# Append to daily log
|
||||
if "FLUSH_OK" in response:
|
||||
logging.info("Result: FLUSH_OK")
|
||||
append_to_daily_log(
|
||||
"FLUSH_OK - Nothing worth saving from this session", "Memory Flush"
|
||||
)
|
||||
elif "FLUSH_ERROR" in response:
|
||||
logging.error("Result: %s", response)
|
||||
append_to_daily_log(response, "Memory Flush")
|
||||
else:
|
||||
logging.info("Result: saved to daily log (%d chars)", len(response))
|
||||
append_to_daily_log(response, "Session")
|
||||
|
||||
# Update dedup state
|
||||
save_flush_state({"session_id": session_id, "timestamp": time.time()})
|
||||
|
||||
# Clean up context file
|
||||
context_file.unlink(missing_ok=True)
|
||||
|
||||
# End-of-day auto-compilation: if it's past the compile hour and today's
|
||||
# log hasn't been compiled yet, trigger compile.py in the background.
|
||||
maybe_trigger_compilation()
|
||||
|
||||
logging.info("Flush complete for session %s", session_id)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
312
scripts/lint.py
Normal file
312
scripts/lint.py
Normal file
|
|
@ -0,0 +1,312 @@
|
|||
"""
|
||||
Lint the knowledge base for structural and semantic health.
|
||||
|
||||
Runs 7 checks: broken links, orphan pages, orphan sources, stale articles,
|
||||
contradictions (LLM), missing backlinks, and sparse articles.
|
||||
|
||||
Usage:
|
||||
uv run python lint.py # all checks
|
||||
uv run python lint.py --structural-only # skip LLM checks (faster, cheaper)
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import asyncio
|
||||
from pathlib import Path
|
||||
|
||||
from config import KNOWLEDGE_DIR, REPORTS_DIR, now_iso, today_iso
|
||||
from utils import (
|
||||
count_inbound_links,
|
||||
extract_wikilinks,
|
||||
file_hash,
|
||||
get_article_word_count,
|
||||
list_raw_files,
|
||||
list_wiki_articles,
|
||||
load_state,
|
||||
read_all_wiki_content,
|
||||
save_state,
|
||||
wiki_article_exists,
|
||||
)
|
||||
|
||||
ROOT_DIR = Path(__file__).resolve().parent.parent
|
||||
|
||||
|
||||
def check_broken_links() -> list[dict]:
|
||||
"""Check for [[wikilinks]] that point to non-existent articles."""
|
||||
issues = []
|
||||
for article in list_wiki_articles():
|
||||
content = article.read_text(encoding="utf-8")
|
||||
rel = article.relative_to(KNOWLEDGE_DIR)
|
||||
for link in extract_wikilinks(content):
|
||||
if link.startswith("daily/"):
|
||||
continue # daily log references are valid
|
||||
if not wiki_article_exists(link):
|
||||
issues.append({
|
||||
"severity": "error",
|
||||
"check": "broken_link",
|
||||
"file": str(rel),
|
||||
"detail": f"Broken link: [[{link}]] - target does not exist",
|
||||
})
|
||||
return issues
|
||||
|
||||
|
||||
def check_orphan_pages() -> list[dict]:
|
||||
"""Check for articles with zero inbound links."""
|
||||
issues = []
|
||||
for article in list_wiki_articles():
|
||||
rel = article.relative_to(KNOWLEDGE_DIR)
|
||||
link_target = str(rel).replace(".md", "").replace("\\", "/")
|
||||
inbound = count_inbound_links(link_target)
|
||||
if inbound == 0:
|
||||
issues.append({
|
||||
"severity": "warning",
|
||||
"check": "orphan_page",
|
||||
"file": str(rel),
|
||||
"detail": f"Orphan page: no other articles link to [[{link_target}]]",
|
||||
})
|
||||
return issues
|
||||
|
||||
|
||||
def check_orphan_sources() -> list[dict]:
|
||||
"""Check for daily logs that haven't been compiled yet."""
|
||||
state = load_state()
|
||||
ingested = state.get("ingested", {})
|
||||
issues = []
|
||||
for log_path in list_raw_files():
|
||||
if log_path.name not in ingested:
|
||||
issues.append({
|
||||
"severity": "warning",
|
||||
"check": "orphan_source",
|
||||
"file": f"daily/{log_path.name}",
|
||||
"detail": f"Uncompiled daily log: {log_path.name} has not been ingested",
|
||||
})
|
||||
return issues
|
||||
|
||||
|
||||
def check_stale_articles() -> list[dict]:
|
||||
"""Check if source daily logs have changed since compilation."""
|
||||
state = load_state()
|
||||
ingested = state.get("ingested", {})
|
||||
issues = []
|
||||
for log_path in list_raw_files():
|
||||
rel = log_path.name
|
||||
if rel in ingested:
|
||||
stored_hash = ingested[rel].get("hash", "")
|
||||
current_hash = file_hash(log_path)
|
||||
if stored_hash != current_hash:
|
||||
issues.append({
|
||||
"severity": "warning",
|
||||
"check": "stale_article",
|
||||
"file": f"daily/{rel}",
|
||||
"detail": f"Stale: {rel} has changed since last compilation",
|
||||
})
|
||||
return issues
|
||||
|
||||
|
||||
def check_missing_backlinks() -> list[dict]:
|
||||
"""Check for asymmetric links: A links to B but B doesn't link to A."""
|
||||
issues = []
|
||||
for article in list_wiki_articles():
|
||||
content = article.read_text(encoding="utf-8")
|
||||
rel = article.relative_to(KNOWLEDGE_DIR)
|
||||
source_link = str(rel).replace(".md", "").replace("\\", "/")
|
||||
|
||||
for link in extract_wikilinks(content):
|
||||
if link.startswith("daily/"):
|
||||
continue
|
||||
target_path = KNOWLEDGE_DIR / f"{link}.md"
|
||||
if target_path.exists():
|
||||
target_content = target_path.read_text(encoding="utf-8")
|
||||
if f"[[{source_link}]]" not in target_content:
|
||||
issues.append({
|
||||
"severity": "suggestion",
|
||||
"check": "missing_backlink",
|
||||
"file": str(rel),
|
||||
"detail": f"[[{source_link}]] links to [[{link}]] but not vice versa",
|
||||
"auto_fixable": True,
|
||||
})
|
||||
return issues
|
||||
|
||||
|
||||
def check_sparse_articles() -> list[dict]:
|
||||
"""Check for articles with fewer than 200 words."""
|
||||
issues = []
|
||||
for article in list_wiki_articles():
|
||||
word_count = get_article_word_count(article)
|
||||
if word_count < 200:
|
||||
rel = article.relative_to(KNOWLEDGE_DIR)
|
||||
issues.append({
|
||||
"severity": "suggestion",
|
||||
"check": "sparse_article",
|
||||
"file": str(rel),
|
||||
"detail": f"Sparse article: {word_count} words (minimum recommended: 200)",
|
||||
})
|
||||
return issues
|
||||
|
||||
|
||||
async def check_contradictions() -> list[dict]:
|
||||
"""Use LLM to detect contradictions across articles."""
|
||||
from claude_agent_sdk import (
|
||||
AssistantMessage,
|
||||
ClaudeAgentOptions,
|
||||
ResultMessage,
|
||||
TextBlock,
|
||||
query,
|
||||
)
|
||||
|
||||
wiki_content = read_all_wiki_content()
|
||||
|
||||
prompt = f"""Review this knowledge base for contradictions, inconsistencies, or
|
||||
conflicting claims across articles.
|
||||
|
||||
## Knowledge Base
|
||||
|
||||
{wiki_content}
|
||||
|
||||
## Instructions
|
||||
|
||||
Look for:
|
||||
- Direct contradictions (article A says X, article B says not-X)
|
||||
- Inconsistent recommendations (different articles recommend conflicting approaches)
|
||||
- Outdated information that conflicts with newer entries
|
||||
|
||||
For each issue found, output EXACTLY one line in this format:
|
||||
CONTRADICTION: [file1] vs [file2] - description of the conflict
|
||||
INCONSISTENCY: [file] - description of the inconsistency
|
||||
|
||||
If no issues found, output exactly: NO_ISSUES
|
||||
|
||||
Do NOT output anything else - no preamble, no explanation, just the formatted lines."""
|
||||
|
||||
response = ""
|
||||
try:
|
||||
async for message in query(
|
||||
prompt=prompt,
|
||||
options=ClaudeAgentOptions(
|
||||
cwd=str(ROOT_DIR),
|
||||
allowed_tools=[],
|
||||
max_turns=2,
|
||||
),
|
||||
):
|
||||
if isinstance(message, AssistantMessage):
|
||||
for block in message.content:
|
||||
if isinstance(block, TextBlock):
|
||||
response += block.text
|
||||
except Exception as e:
|
||||
return [{"severity": "error", "check": "contradiction", "file": "(system)", "detail": f"LLM check failed: {e}"}]
|
||||
|
||||
issues = []
|
||||
if "NO_ISSUES" not in response:
|
||||
for line in response.strip().split("\n"):
|
||||
line = line.strip()
|
||||
if line.startswith("CONTRADICTION:") or line.startswith("INCONSISTENCY:"):
|
||||
issues.append({
|
||||
"severity": "warning",
|
||||
"check": "contradiction",
|
||||
"file": "(cross-article)",
|
||||
"detail": line,
|
||||
})
|
||||
|
||||
return issues
|
||||
|
||||
|
||||
def generate_report(all_issues: list[dict]) -> str:
|
||||
"""Generate a markdown lint report."""
|
||||
errors = [i for i in all_issues if i["severity"] == "error"]
|
||||
warnings = [i for i in all_issues if i["severity"] == "warning"]
|
||||
suggestions = [i for i in all_issues if i["severity"] == "suggestion"]
|
||||
|
||||
lines = [
|
||||
f"# Lint Report - {today_iso()}",
|
||||
"",
|
||||
f"**Total issues:** {len(all_issues)}",
|
||||
f"- Errors: {len(errors)}",
|
||||
f"- Warnings: {len(warnings)}",
|
||||
f"- Suggestions: {len(suggestions)}",
|
||||
"",
|
||||
]
|
||||
|
||||
for severity, issues, marker in [
|
||||
("Errors", errors, "x"),
|
||||
("Warnings", warnings, "!"),
|
||||
("Suggestions", suggestions, "?"),
|
||||
]:
|
||||
if issues:
|
||||
lines.append(f"## {severity}")
|
||||
lines.append("")
|
||||
for issue in issues:
|
||||
fixable = " (auto-fixable)" if issue.get("auto_fixable") else ""
|
||||
lines.append(f"- **[{marker}]** `{issue['file']}` - {issue['detail']}{fixable}")
|
||||
lines.append("")
|
||||
|
||||
if not all_issues:
|
||||
lines.append("All checks passed. Knowledge base is healthy.")
|
||||
lines.append("")
|
||||
|
||||
return "\n".join(lines)
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(description="Lint the knowledge base")
|
||||
parser.add_argument(
|
||||
"--structural-only",
|
||||
action="store_true",
|
||||
help="Skip LLM-based checks (contradictions) - faster and free",
|
||||
)
|
||||
args = parser.parse_args()
|
||||
|
||||
print("Running knowledge base lint checks...")
|
||||
all_issues: list[dict] = []
|
||||
|
||||
# Structural checks (free, instant)
|
||||
checks = [
|
||||
("Broken links", check_broken_links),
|
||||
("Orphan pages", check_orphan_pages),
|
||||
("Orphan sources", check_orphan_sources),
|
||||
("Stale articles", check_stale_articles),
|
||||
("Missing backlinks", check_missing_backlinks),
|
||||
("Sparse articles", check_sparse_articles),
|
||||
]
|
||||
|
||||
for name, check_fn in checks:
|
||||
print(f" Checking: {name}...")
|
||||
issues = check_fn()
|
||||
all_issues.extend(issues)
|
||||
print(f" Found {len(issues)} issue(s)")
|
||||
|
||||
# LLM check (costs money)
|
||||
if not args.structural_only:
|
||||
print(" Checking: Contradictions (LLM)...")
|
||||
issues = asyncio.run(check_contradictions())
|
||||
all_issues.extend(issues)
|
||||
print(f" Found {len(issues)} issue(s)")
|
||||
else:
|
||||
print(" Skipping: Contradictions (--structural-only)")
|
||||
|
||||
# Generate and save report
|
||||
report = generate_report(all_issues)
|
||||
REPORTS_DIR.mkdir(parents=True, exist_ok=True)
|
||||
report_path = REPORTS_DIR / f"lint-{today_iso()}.md"
|
||||
report_path.write_text(report, encoding="utf-8")
|
||||
print(f"\nReport saved to: {report_path}")
|
||||
|
||||
# Update state
|
||||
state = load_state()
|
||||
state["last_lint"] = now_iso()
|
||||
save_state(state)
|
||||
|
||||
# Summary
|
||||
errors = sum(1 for i in all_issues if i["severity"] == "error")
|
||||
warnings = sum(1 for i in all_issues if i["severity"] == "warning")
|
||||
suggestions = sum(1 for i in all_issues if i["severity"] == "suggestion")
|
||||
print(f"\nResults: {errors} errors, {warnings} warnings, {suggestions} suggestions")
|
||||
|
||||
if errors > 0:
|
||||
print("\nErrors found - knowledge base needs attention!")
|
||||
return 1
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
exit(main())
|
||||
138
scripts/query.py
Normal file
138
scripts/query.py
Normal file
|
|
@ -0,0 +1,138 @@
|
|||
"""
|
||||
Query the knowledge base using index-guided retrieval (no RAG).
|
||||
|
||||
The LLM reads the index, picks relevant articles, and synthesizes an answer.
|
||||
No vector database, no embeddings, no chunking - just structured markdown
|
||||
and an index the LLM can reason over.
|
||||
|
||||
Usage:
|
||||
uv run python query.py "How should I handle auth redirects?"
|
||||
uv run python query.py "What patterns do I use for API design?" --file-back
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import asyncio
|
||||
from pathlib import Path
|
||||
|
||||
from config import KNOWLEDGE_DIR, QA_DIR, now_iso
|
||||
from utils import load_state, read_all_wiki_content, save_state
|
||||
|
||||
ROOT_DIR = Path(__file__).resolve().parent.parent
|
||||
|
||||
|
||||
async def run_query(question: str, file_back: bool = False) -> str:
|
||||
"""Query the knowledge base and optionally file the answer back."""
|
||||
from claude_agent_sdk import (
|
||||
AssistantMessage,
|
||||
ClaudeAgentOptions,
|
||||
ResultMessage,
|
||||
TextBlock,
|
||||
query,
|
||||
)
|
||||
|
||||
wiki_content = read_all_wiki_content()
|
||||
|
||||
tools = ["Read", "Glob", "Grep"]
|
||||
if file_back:
|
||||
tools.extend(["Write", "Edit"])
|
||||
|
||||
file_back_instructions = ""
|
||||
if file_back:
|
||||
timestamp = now_iso()
|
||||
file_back_instructions = f"""
|
||||
|
||||
## File Back Instructions
|
||||
|
||||
After answering, do the following:
|
||||
1. Create a Q&A article at {QA_DIR}/ with the filename being a slugified version
|
||||
of the question (e.g., knowledge/qa/how-to-handle-auth-redirects.md)
|
||||
2. Use the Q&A article format from the schema (frontmatter with title, question,
|
||||
consulted articles, filed date)
|
||||
3. Update {KNOWLEDGE_DIR / 'index.md'} with a new row for this Q&A article
|
||||
4. Append to {KNOWLEDGE_DIR / 'log.md'}:
|
||||
## [{timestamp}] query (filed) | question summary
|
||||
- Question: {question}
|
||||
- Consulted: [[list of articles read]]
|
||||
- Filed to: [[qa/article-name]]
|
||||
"""
|
||||
|
||||
prompt = f"""You are a knowledge base query engine. Answer the user's question by
|
||||
consulting the knowledge base below.
|
||||
|
||||
## How to Answer
|
||||
|
||||
1. Read the INDEX section first - it lists every article with a one-line summary
|
||||
2. Identify 3-10 articles that are relevant to the question
|
||||
3. Read those articles carefully (they're included below)
|
||||
4. Synthesize a clear, thorough answer
|
||||
5. Cite your sources using [[wikilinks]] (e.g., [[concepts/supabase-auth]])
|
||||
6. If the knowledge base doesn't contain relevant information, say so honestly
|
||||
|
||||
## Knowledge Base
|
||||
|
||||
{wiki_content}
|
||||
|
||||
## Question
|
||||
|
||||
{question}
|
||||
{file_back_instructions}"""
|
||||
|
||||
answer = ""
|
||||
cost = 0.0
|
||||
|
||||
try:
|
||||
async for message in query(
|
||||
prompt=prompt,
|
||||
options=ClaudeAgentOptions(
|
||||
cwd=str(ROOT_DIR),
|
||||
system_prompt={"type": "preset", "preset": "claude_code"},
|
||||
allowed_tools=tools,
|
||||
permission_mode="acceptEdits",
|
||||
max_turns=15,
|
||||
),
|
||||
):
|
||||
if isinstance(message, AssistantMessage):
|
||||
for block in message.content:
|
||||
if isinstance(block, TextBlock):
|
||||
answer += block.text
|
||||
elif isinstance(message, ResultMessage):
|
||||
cost = message.total_cost_usd or 0.0
|
||||
except Exception as e:
|
||||
answer = f"Error querying knowledge base: {e}"
|
||||
|
||||
# Update state
|
||||
state = load_state()
|
||||
state["query_count"] = state.get("query_count", 0) + 1
|
||||
state["total_cost"] = state.get("total_cost", 0.0) + cost
|
||||
save_state(state)
|
||||
|
||||
return answer
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(description="Query the personal knowledge base")
|
||||
parser.add_argument("question", help="The question to ask")
|
||||
parser.add_argument(
|
||||
"--file-back",
|
||||
action="store_true",
|
||||
help="File the answer back into the knowledge base as a Q&A article",
|
||||
)
|
||||
args = parser.parse_args()
|
||||
|
||||
print(f"Question: {args.question}")
|
||||
print(f"File back: {'yes' if args.file_back else 'no'}")
|
||||
print("-" * 60)
|
||||
|
||||
answer = asyncio.run(run_query(args.question, file_back=args.file_back))
|
||||
print(answer)
|
||||
|
||||
if args.file_back:
|
||||
print("\n" + "-" * 60)
|
||||
qa_count = len(list(QA_DIR.glob("*.md"))) if QA_DIR.exists() else 0
|
||||
print(f"Answer filed to knowledge/qa/ ({qa_count} Q&A articles total)")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
133
scripts/utils.py
Normal file
133
scripts/utils.py
Normal file
|
|
@ -0,0 +1,133 @@
|
|||
"""Shared utilities for the personal knowledge base."""
|
||||
|
||||
import hashlib
|
||||
import json
|
||||
import re
|
||||
from pathlib import Path
|
||||
|
||||
from config import (
|
||||
CONCEPTS_DIR,
|
||||
CONNECTIONS_DIR,
|
||||
DAILY_DIR,
|
||||
INDEX_FILE,
|
||||
KNOWLEDGE_DIR,
|
||||
LOG_FILE,
|
||||
QA_DIR,
|
||||
STATE_FILE,
|
||||
)
|
||||
|
||||
|
||||
# ── State management ──────────────────────────────────────────────────
|
||||
|
||||
def load_state() -> dict:
|
||||
"""Load persistent state from state.json."""
|
||||
if STATE_FILE.exists():
|
||||
return json.loads(STATE_FILE.read_text(encoding="utf-8"))
|
||||
return {"ingested": {}, "query_count": 0, "last_lint": None, "total_cost": 0.0}
|
||||
|
||||
|
||||
def save_state(state: dict) -> None:
|
||||
"""Save state to state.json."""
|
||||
STATE_FILE.write_text(json.dumps(state, indent=2), encoding="utf-8")
|
||||
|
||||
|
||||
# ── File hashing ──────────────────────────────────────────────────────
|
||||
|
||||
def file_hash(path: Path) -> str:
|
||||
"""SHA-256 hash of a file (first 16 hex chars)."""
|
||||
return hashlib.sha256(path.read_bytes()).hexdigest()[:16]
|
||||
|
||||
|
||||
# ── Slug / naming ─────────────────────────────────────────────────────
|
||||
|
||||
def slugify(text: str) -> str:
|
||||
"""Convert text to a filename-safe slug."""
|
||||
text = text.lower().strip()
|
||||
text = re.sub(r"[^\w\s-]", "", text)
|
||||
text = re.sub(r"[\s_]+", "-", text)
|
||||
text = re.sub(r"-+", "-", text)
|
||||
return text.strip("-")
|
||||
|
||||
|
||||
# ── Wikilink helpers ──────────────────────────────────────────────────
|
||||
|
||||
def extract_wikilinks(content: str) -> list[str]:
|
||||
"""Extract all [[wikilinks]] from markdown content."""
|
||||
return re.findall(r"\[\[([^\]]+)\]\]", content)
|
||||
|
||||
|
||||
def wiki_article_exists(link: str) -> bool:
|
||||
"""Check if a wikilinked article exists on disk."""
|
||||
path = KNOWLEDGE_DIR / f"{link}.md"
|
||||
return path.exists()
|
||||
|
||||
|
||||
# ── Wiki content helpers ──────────────────────────────────────────────
|
||||
|
||||
def read_wiki_index() -> str:
|
||||
"""Read the knowledge base index file."""
|
||||
if INDEX_FILE.exists():
|
||||
return INDEX_FILE.read_text(encoding="utf-8")
|
||||
return "# Knowledge Base Index\n\n| Article | Summary | Compiled From | Updated |\n|---------|---------|---------------|---------|"
|
||||
|
||||
|
||||
def read_all_wiki_content() -> str:
|
||||
"""Read index + all wiki articles into a single string for context."""
|
||||
parts = [f"## INDEX\n\n{read_wiki_index()}"]
|
||||
|
||||
for subdir in [CONCEPTS_DIR, CONNECTIONS_DIR, QA_DIR]:
|
||||
if not subdir.exists():
|
||||
continue
|
||||
for md_file in sorted(subdir.glob("*.md")):
|
||||
rel = md_file.relative_to(KNOWLEDGE_DIR)
|
||||
content = md_file.read_text(encoding="utf-8")
|
||||
parts.append(f"## {rel}\n\n{content}")
|
||||
|
||||
return "\n\n---\n\n".join(parts)
|
||||
|
||||
|
||||
def list_wiki_articles() -> list[Path]:
|
||||
"""List all wiki article files."""
|
||||
articles = []
|
||||
for subdir in [CONCEPTS_DIR, CONNECTIONS_DIR, QA_DIR]:
|
||||
if subdir.exists():
|
||||
articles.extend(sorted(subdir.glob("*.md")))
|
||||
return articles
|
||||
|
||||
|
||||
def list_raw_files() -> list[Path]:
|
||||
"""List all daily log files."""
|
||||
if not DAILY_DIR.exists():
|
||||
return []
|
||||
return sorted(DAILY_DIR.glob("*.md"))
|
||||
|
||||
|
||||
# ── Index helpers ─────────────────────────────────────────────────────
|
||||
|
||||
def count_inbound_links(target: str, exclude_file: Path | None = None) -> int:
|
||||
"""Count how many wiki articles link to a given target."""
|
||||
count = 0
|
||||
for article in list_wiki_articles():
|
||||
if article == exclude_file:
|
||||
continue
|
||||
content = article.read_text(encoding="utf-8")
|
||||
if f"[[{target}]]" in content:
|
||||
count += 1
|
||||
return count
|
||||
|
||||
|
||||
def get_article_word_count(path: Path) -> int:
|
||||
"""Count words in an article, excluding YAML frontmatter."""
|
||||
content = path.read_text(encoding="utf-8")
|
||||
# Strip frontmatter
|
||||
if content.startswith("---"):
|
||||
end = content.find("---", 3)
|
||||
if end != -1:
|
||||
content = content[end + 3:]
|
||||
return len(content.split())
|
||||
|
||||
|
||||
def build_index_entry(rel_path: str, summary: str, sources: str, updated: str) -> str:
|
||||
"""Build a single index table row."""
|
||||
link = rel_path.replace(".md", "")
|
||||
return f"| [[{link}]] | {summary} | {sources} | {updated} |"
|
||||
636
uv.lock
generated
Normal file
636
uv.lock
generated
Normal file
|
|
@ -0,0 +1,636 @@
|
|||
version = 1
|
||||
revision = 1
|
||||
requires-python = ">=3.12"
|
||||
|
||||
[[package]]
|
||||
name = "annotated-types"
|
||||
version = "0.7.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/ee/67/531ea369ba64dcff5ec9c3402f9f51bf748cec26dde048a2f973a4eea7f5/annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89", size = 16081 }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53", size = 13643 },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "anyio"
|
||||
version = "4.13.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "idna" },
|
||||
{ name = "typing-extensions", marker = "python_full_version < '3.13'" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/19/14/2c5dd9f512b66549ae92767a9c7b330ae88e1932ca57876909410251fe13/anyio-4.13.0.tar.gz", hash = "sha256:334b70e641fd2221c1505b3890c69882fe4a2df910cba14d97019b90b24439dc", size = 231622 }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/da/42/e921fccf5015463e32a3cf6ee7f980a6ed0f395ceeaa45060b61d86486c2/anyio-4.13.0-py3-none-any.whl", hash = "sha256:08b310f9e24a9594186fd75b4f73f4a4152069e3853f1ed8bfbf58369f4ad708", size = 114353 },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "attrs"
|
||||
version = "26.1.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/9a/8e/82a0fe20a541c03148528be8cac2408564a6c9a0cc7e9171802bc1d26985/attrs-26.1.0.tar.gz", hash = "sha256:d03ceb89cb322a8fd706d4fb91940737b6642aa36998fe130a9bc96c985eff32", size = 952055 }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/64/b4/17d4b0b2a2dc85a6df63d1157e028ed19f90d4cd97c36717afef2bc2f395/attrs-26.1.0-py3-none-any.whl", hash = "sha256:c647aa4a12dfbad9333ca4e71fe62ddc36f4e63b2d260a37a8b83d2f043ac309", size = 67548 },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "certifi"
|
||||
version = "2026.2.25"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/af/2d/7bf41579a8986e348fa033a31cdd0e4121114f6bce2457e8876010b092dd/certifi-2026.2.25.tar.gz", hash = "sha256:e887ab5cee78ea814d3472169153c2d12cd43b14bd03329a39a9c6e2e80bfba7", size = 155029 }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/9a/3c/c17fb3ca2d9c3acff52e30b309f538586f9f5b9c9cf454f3845fc9af4881/certifi-2026.2.25-py3-none-any.whl", hash = "sha256:027692e4402ad994f1c42e52a4997a9763c646b73e4096e4d5d6db8af1d6f0fa", size = 153684 },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "cffi"
|
||||
version = "2.0.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "pycparser", marker = "implementation_name != 'PyPy'" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/eb/56/b1ba7935a17738ae8453301356628e8147c79dbb825bcbc73dc7401f9846/cffi-2.0.0.tar.gz", hash = "sha256:44d1b5909021139fe36001ae048dbdde8214afa20200eda0f64c068cac5d5529", size = 523588 }
|
||||
wheels = [
|
||||
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||||
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||||
{ url = "https://files.pythonhosted.org/packages/ff/df/a4f0fbd47331ceeba3d37c2e51e9dfc9722498becbeec2bd8bc856c9538a/cffi-2.0.0-cp312-cp312-manylinux1_i686.manylinux2014_i686.manylinux_2_17_i686.manylinux_2_5_i686.whl", hash = "sha256:21d1152871b019407d8ac3985f6775c079416c282e431a4da6afe7aefd2bccbe", size = 212529 },
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||||
{ url = "https://files.pythonhosted.org/packages/7b/2b/2b6435f76bfeb6bbf055596976da087377ede68df465419d192acf00c437/cffi-2.0.0-cp312-cp312-win32.whl", hash = "sha256:da902562c3e9c550df360bfa53c035b2f241fed6d9aef119048073680ace4a18", size = 172932 },
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Loading…
Add table
Add a link
Reference in a new issue