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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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Listed on Skillselion
Installs7
repo stars61
Last updatedAugust 4, 2026
Repositoryjoelhooks/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

SKILL.mdMarkdownGitHub ↗

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_v2 collection
  • 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/:chunkId

The 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-id
  • claim (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.

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