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document-extract

Enumerate every requested item in long text documents or transcripts, with fields and source evidence. Use for exhaustive inventories such as every exercise, action item, or finding; not ordinary summaries, general knowledge lists, or code symbols.

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MIT
Last commit
Oct 13, 2026

hub/agents/gaia/python/gaia_agent/skills/document-extract/SKILL.md

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Exhaustive document extraction

  1. Identify each source text file and the requested item type and fields. Use the user's current files; remembered summaries can help locate them but do not establish current contents or completeness.
  2. Call extract_document_items once per source. It processes bounded chunks sequentially and retains source-backed entries. Do not replace this with a summary or manually calculated page offsets. When specifying fields in the tool call, pass fields: ["name", "owner", "deadline"] (the actual requested names).
  3. If an extraction fails, report it as incomplete. Do not silently switch to a shorter list, a guessed count, or a script that has not been run.
  4. If the user requested a saved inventory, use save_extracted_items for the exact destination. The exporter preserves all entries and provenance as JSON, CSV, or readable text, and the framework reads the file back and checks it; don't edit or regenerate it by hand.
  5. The framework appends the full retained inventory to your final answer. Briefly state limitations or requested analysis; do not repeat or re-summarize the list.

The existing memory system records the action, source fingerprint, coverage, count and final inventory unless the session is private. Use conversation search for follow-ups about previous work; re-extract if the source or requested fields change. Never treat a remembered summary as proof that all current items were extracted, and don't store a whole private transcript as a durable personal fact. A compact summary may supplement the inventory, not replace it.

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