pdf2gep

Convert a PDF document into GEP (Genome Evolution Protocol) assets suitable for retrieval inside the EvoMap network.

pdf2gep fetches a PDF (local path or URL), splits the text into chunks, and writes one GEP bundle per chunk:

• A Gene of category knowledge_reference -- a compact retrieval pointer.
• A KnowledgeCapsule with source_type = "pdf_knowledge" -- the chunk text itself, carried as reference material.

Honest scope note (please read before using)

pdf2gep is a retrieval-oriented protocol adapter. It does not produce the kind of Capsule that proves a Gene works.

• A standard GEP Capsule is an auditable record of one real execution of a Gene (execution_trace with exit codes, non-zero blast_radius, etc.). PDFs contain knowledge, not executions, so pdf2gep deliberately emits a different variant: source_type = "pdf_knowledge", outcome.status = "knowledge_reference", and an empty execution_trace. Treating these as proof-of-validation is a misuse.
• The paper that motivates GEP -- Wang, Ren, Zhang, "From Procedural Skills to Strategy Genes: Towards Experience-Driven Test-Time Evolution" (arXiv:2604.15097) -- validates Gene-as-control-interface on 45 scientific code-solving tasks with Gemini 3.1 Pro and Flash Lite. That result does not carry over automatically to retrieval-style knowledge Genes. The Gene emitted by this tool is explicitly a retrieval pointer, not a control interface.
• Chunk quality is naive: fixed-width ~4000-char slices. This is fine for retrieval-by-topic, but it is not a structured extraction. Do not expect the output to replace a proper RAG ingestion pipeline.

Downstream consumers (EvoMap hub, local agents) should filter on source_type and treat pdf_knowledge Capsules as reference material only.

Install

git clone https://github.com/EvoMap/pdf2gep.git
cd pdf2gep
npm install

Requires Node.js 18+ (for built-in fetch).

Usage

# From a URL (arXiv, etc.)
node index.js "https://arxiv.org/pdf/2604.15097.pdf"

# From a local file
node index.js "./manual.pdf"

Bundles are written to temp/evomap_assets/batch_<timestamp>.json.

Each entry in the batch is { gene, capsule }.

Output schema (GEP 1.6.0)

Gene

{
  "type": "Gene",
  "id": "gene_pdf2gep_<slug>_chunk<N>_<sha8>",
  "category": "knowledge_reference",
  "summary": "Retrieval pointer for <slug> chunk #<N>",
  "signals_match": ["knowledge_lookup", "pdf_reference", "<slug>"],
  "preconditions": ["Agent needs to consult the source document to answer or plan."],
  "strategy": [
    "Retrieve the backing KnowledgeCapsule (source_type=pdf_knowledge) to read the chunk verbatim.",
    "Do NOT treat the chunk as a validated procedure. It is reference material only."
  ],
  "constraints": { "max_files": 0, "forbidden_paths": [".git", "node_modules"] },
  "validation": [],
  "schema_version": "1.6.0",
  "_source": {
    "kind": "pdf2gep",
    "source_type": "pdf_knowledge",
    "source_ref": "<url or absolute path>",
    "source_sha256": "<sha256 of the whole pdf>",
    "chunk_index": 0,
    "chunk_sha256": "<sha256 of this chunk>",
    "claims_outside_scope": "knowledge_extraction",
    "paper_scope_note": "Gene-as-control-interface was validated by arXiv:2604.15097 on code-science tasks. A knowledge_reference Gene is NOT a control interface; it is a retrieval pointer."
  }
}

KnowledgeCapsule

{
  "type": "Capsule",
  "id": "cap_pdf2gep_<chunk_sha12>_<idkey>",
  "gene": "<gene.id>",
  "source_type": "pdf_knowledge",
  "trigger": ["knowledge_lookup", "pdf_reference", "<slug>"],
  "summary": "PDF chunk #<N> from <name>",
  "confidence": null,
  "blast_radius": { "files": 0, "lines": 0, "chunk_chars": 4000 },
  "outcome": { "status": "knowledge_reference", "score": null },
  "env_fingerprint": { "platform": "...", "node": "..." },
  "content": "<chunk text verbatim>",
  "execution_trace": [],
  "schema_version": "1.6.0",
  "_source": {
    "source_ref": "<url or path>",
    "source_sha256": "<sha256 of the whole pdf>",
    "chunk_index": 0,
    "chunk_sha256": "<sha256 of this chunk>",
    "claims_outside_scope": "knowledge_extraction"
  }
}

Key invariants validators can rely on:

• outcome.status === "knowledge_reference" -- never "success" / "failed".
• execution_trace is empty.
• blast_radius.files === 0 && lines === 0.
• source_type === "pdf_knowledge" on both the Gene _source and the Capsule.

Publishing to EvoMap

Use evolver (the GEP reference runtime) to publish a bundle:

evolver publish --bundle temp/evomap_assets/batch_<ts>.json

The EvoMap hub routes pdf_knowledge Capsules to the retrieval index, separately from execution Capsules. Installation and consumption is done via the usual evolver run / gep_install_gene flow; agents that match a knowledge_lookup signal will pick the retrieval Gene and fetch the backing Capsule for citation.

See also:

• Protocol reference: https://evomap.ai/wiki/16-gep-protocol
• Skill store (where the Gene shows up): https://evomap.ai/wiki/31-skill-store

Relationship to other tools

• skill2gep -- protocol adapter that converts SKILL.md into Gene+ExecutionCapsule bundles. That tool is for procedural knowledge where the Capsule's execution_trace comes from real runs. pdf2gep is complementary: it covers reference knowledge and deliberately does not fabricate execution evidence.
• kitchen-engineer42/pdf2skills -- prior art that inspired this tool. pdf2skills targets Claude Code's SKILL.md format; pdf2gep targets the GEP protocol and is explicit about being retrieval-only.

License

MIT.