How do I install the Ingest Docs skill?

Run npx skillmds@latest add oimiragieo/ingest-docs in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Ingest Docs safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Ingest Docs?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Ingest Docs free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Ingest Docs?

oimiragieo (@oimiragieo) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Ingest Docs by oimiragieo — Agent Skill

Name: Ingest Docs
Author: oimiragieo

Ingest Docs

Query-guided compression of a documentation site, docs folder, README, wiki, or specification for Q&A. Use this skill whenever the user points you at multi-page reference material and wants to find something in it. Trigger phrases include "help me find X in these docs", "what does the Y docs say about Z", "search the Stripe/AWS/Anthropic docs for", "summarize this reference", "check the docs for". Passes the user's question as a query parameter so gotcontext weights relevant sections heavier and compresses off-topic sections more aggressively — keeps answers grounded without loading every page.

oimiragieo e9f4277 2.4 KB Updated Sep 17, 2026 0 repo stars

File contents

ingest-docs

When this skill is the preferred path

A docs tree is the perfect target for query-guided compression: sprawling, repetitive, and mostly irrelevant to any single question. Loading the full tree exhausts context; loading one page misses cross-references. Query-guided compression solves both by keeping what's relevant to the user's question and shrinking what isn't.

How to use it

Identify the source — local path (./docs), a docs URL, or a list of URLs. Ask the user if it's unclear.
Identify the question. If the user hasn't asked a specific question yet, ask before ingesting — query-guided mode needs a query. If they want a general overview, use fidelity=balanced without a query.
Call gotcontext's ingest_context MCP tool with:
- source — path or URL(s)
- query — the user's question (enables relevance-weighted compression; highly recommended)
- fidelity=balanced — good Q&A accuracy, ~50% savings
Read the compressed blob. The response includes a one-line header with source, page count, and compression ratio.
Answer the user, citing sections/pages from the compressed output so they can verify.

When the raw read is actually fine

Single short README — just Read it.
You're writing or editing documentation — you need the raw markdown.

Good candidates for this skill

Sphinx / MkDocs / Docusaurus trees
GitBook, Notion, or Confluence exports
Swagger / OpenAPI HTML renderings
Any /docs directory in a repo
Man page archives

Cache behavior

Within one session, cache the compressed result of a tree after the first call and query against the cached blob for follow-ups. Each re-ingestion is a paid compression call — don't waste the user's credits on the same tree twice.

---name: ingest-docsdescription: Query-guided compression of a documentation site, docs folder, README, wiki, or specification for Q&A. Use this skill whenever the user points you at multi-page reference material and wants to find something in it. Trigger phrases include "help me find X in these docs", "what does the Y docs say about Z", "search the Stripe/AWS/Anthropic docs for", "summarize this reference", "check the docs for". Passes the user's question as a query parameter so gotcontext weights relevant sections heavier and compresses off-topic sections more aggressively — keeps answers grounded without loading every page.---# ingest-docs## When this skill is the preferred pathA docs tree is the perfect target for query-guided compression:sprawling, repetitive, and mostly irrelevant to any single question.Loading the full tree exhausts context; loading one page missescross-references. Query-guided compression solves both by keepingwhat's relevant to the user's question and shrinking what isn't.## How to use it1. Identify the source — local path (`./docs`), a docs URL, or a list of URLs. Ask the user if it's unclear.2. Identify the question. If the user hasn't asked a specific question yet, ask before ingesting — query-guided mode needs a query. If they want a general overview, use `fidelity=balanced` without a query.3. Call gotcontext's `ingest_context` MCP tool with: - `source` — path or URL(s) - `query` — the user's question (enables relevance-weighted compression; highly recommended) - `fidelity=balanced` — good Q&A accuracy, ~50% savings4. Read the compressed blob. The response includes a one-line header with source, page count, and compression ratio.5. Answer the user, citing sections/pages from the compressed output so they can verify.## When the raw read is actually fine- Single short README — just `Read` it.- You're writing or editing documentation — you need the raw markdown.## Good candidates for this skill- Sphinx / MkDocs / Docusaurus trees- GitBook, Notion, or Confluence exports- Swagger / OpenAPI HTML renderings- Any `/docs` directory in a repo- Man page archives## Cache behaviorWithin one session, cache the compressed result of a tree after thefirst call and query against the cached blob for follow-ups. Eachre-ingestion is a paid compression call — don't waste the user'scredits on the same tree twice.

Frequently asked questions