
Pdf Brain
- 7 installs
- 61 repo stars
- Updated August 4, 2026
- joelhooks/joelclaw
Retrieve and synthesize cited evidence from a 600+ book docs corpus into operational architecture and ops guidance via joelclaw docs search.
About
Synthesizes evidence-backed answers from a large PDF/docs corpus using semantic search and progressive context expansion. A developer uses it to derive practical architecture and ops guidance grounded in cited chunks rather than web summaries.
- Semantic retrieval over a 600+ book corpus via joelclaw docs search plus staged context expansion
- Builds a cited evidence ledger and converts claims into operational principles and concrete actions
Pdf Brain by the numbers
- 7 all-time installs (skills.sh)
- Ranked #12,545 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
- Data as of Aug 5, 2026 (Skillselion catalog sync)
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| Installs | 7 |
|---|---|
| repo stars | ★ 61 |
| Last updated | August 4, 2026 |
| Repository | joelhooks/joelclaw ↗ |
What it does
Retrieve and synthesize cited evidence from a 600+ book docs corpus into operational architecture and ops guidance via joelclaw docs search.
Files
PDF Brain — Research → Practical System Moves
Use this skill when the user wants evidence-backed synthesis from the docs library (600+ books, PDFs, long-form references), not generic web summarization.
Pipeline v2 (ADR-0234)
The docs pipeline uses a staged artifact chain:
- Extraction: opendataloader-pdf → structured markdown with headings, tables, reading order
- Chunking: markdown-native heading detection, no overlap, hierarchical section + snippet chunks
- Embeddings: nomic-embed-text via ollama GPU (768-dim, retrieval-tuned, pre-computed at ingest) in
docs_chunks_v2collection - Artifacts: durable on NAS at
/Volumes/three-body/docs-artifacts/{docId}/—.md,.meta.json,.chunks.jsonl - Summaries: LLM-generated per-document summaries in
.meta.json
When to Use
Trigger cues (explicit or implied):
- "research this" / "from the library" / "from the books"
- "pdf brain" / "correlate this to our system"
- "what does the research say" / "what do the books say"
- "expand this into practical ideas"
Retrieval Workflow
CLI path (preferred for interactive sessions)
# Search across all books — semantic by default (nomic 768-dim)
joelclaw docs search "distributed consensus" --limit 8
# Search within a specific book
joelclaw docs search "consensus" --doc designing-dataintensive-applications-39cc0d1842a5
# Expand a chunk into surrounding context
joelclaw docs context <chunk-id> --mode snippet-window --before 2 --after 2
# Get the full parent section
joelclaw docs context <chunk-id> --mode parent-section
# Get neighboring sections for broad context
joelclaw docs context <chunk-id> --mode section-neighborhood --neighbors 2
# Read the full structured markdown of a book
joelclaw docs markdown <doc-id>
# Get document summary + taxonomy metadata
joelclaw docs summary <doc-id>API path (for programmatic access or docs-api consumers)
GET /search?q=distributed+consensus&semantic=true&expand=true&assemble=true
GET /docs/:docId/toc
GET /docs/:docId/markdown
GET /docs/:docId/summary
GET /chunks/:chunkIdThe docs-api runs on k8s at docs-api:3838 (Bearer auth required).
Context expansion strategy
The library supports progressive context expansion:
1. Search → chunk-level hits with heading_path and snippet 2. snippet-window → 2 chunks before/after for local context 3. parent-section → the full section containing the snippet 4. section-neighborhood → adjacent sections for broader flow 5. markdown → the complete structured book text
Start narrow, expand only when needed. Don't dump full books into context.
Evidence Synthesis
Build an evidence ledger
While reading, keep a compact ledger:
doc(title)chunk-idclaim(one sentence)relevance(why it matters to this problem)
Never output synthesis without traceable evidence.
Convert evidence into principles
Turn each claim into an operational principle in imperative form:
- "Treat partial failure as normal."
- "Fail fast at dependency boundaries."
- "Prefer idempotent replay-safe remediation loops."
Avoid vague advice. Each principle must imply a technical behavior.
Correlate to joelclaw philosophy
Map principles to existing joelclaw operating rules:
- single source of truth
- silent failures are bugs
- Inngest durability + retries
- CLI-first agent interface
- observability required at every step
- skill/doc updates when reality changes
Translate into action
For each principle, produce: 1. Concrete change (file/service/config path) 2. Validation gate (exact command) 3. Failure signal (what proves it did not work) 4. Rollback or containment move
Taxonomy
The library is classified via SKOS taxonomy:
jc:docs:programming(systems, languages, architecture)jc:docs:business(creator economy)jc:docs:education(learning science, pedagogy)jc:docs:design(game, systems, product)jc:docs:marketing,jc:docs:strategy,jc:docs:ai,jc:docs:operations
Use --concept jc:docs:programming:systems to narrow by domain. Use joelclaw docs status to see facet counts per concept.
Rules
- Do not fabricate quotes or claims.
- Always cite chunk IDs for non-obvious assertions.
- Do not output "book report" fluff. Translate to operations.
- If infra changes are proposed, include verification commands.
- If work implies architectural policy change, tie it to an ADR path.
- Start with search, expand only as needed. Don't waste context on full book dumps.