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Research And Ingest

Community
llopresto87
research-and-ingest

Find authoritative sources on the web (or via documentation MCP servers like Context7/DeepWiki), download or snapshot them when allowed, normalize them into clean Markdown, and stage them for the library-wiki. Use whenever a new library/spec/API/standard is being added, a wiki page is stale, an ADR needs current evidence, or a behavior best practice needs verification. Always prefer official upstream sources and version-pinned content over training data.

Overview

Publisherllopresto87
RepositoryCypress
Skill nameresearch-and-ingest
Stars
113
Forks
6
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by llopresto87 on GitHub. Read the source before you install it.

Installation

Install the Research And Ingest AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/llopresto87/Cypress/tree/main/skills/research-and-ingest
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/llopresto87/Cypress.git /tmp/Cypress
mkdir -p .claude/skills
cp -r /tmp/Cypress/skills/research-and-ingest .claude/skills/research-and-ingest
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Research And Ingest in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Research And Ingest on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Research And Ingest is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

research-and-ingest

This skill is invoked by research-scout to fetch external content in a disciplined way. The output of this skill is two artifacts: raw snapshots in docs/graph/sources/raw/ (when license allows) and normalized summaries in docs/graph/sources/normalized/, plus a row in docs/graph/sources/index.md. The wiki page (created by library-wiki) draws from these.

When to apply this skill

  • A new dependency is being evaluated or added.
  • A wiki page's "Last reviewed" date is older than the project's review cadence, or its pin no longer matches the lockfile.
  • An ADR is being written and needs current evidence.
  • An LLM/VLM feature is being designed and needs the provider's current behavior documentation.
  • A spec is being authored and refers to a standard (RFC, schema, protocol) the project hasn't read recently.

Source ranking

When two sources disagree, prefer in this order:

  1. Official upstream documentation for the exact version in use.
  2. Official upstream source code (especially public API surface, examples directory, CHANGELOG).
  3. Official upstream blog posts and migration guides.
  4. Security advisories from trusted bodies (CVE, CISA, OWASP, official upstream advisories).
  5. Well-maintained community resources with current dates.
  6. Recent blog posts from credible authors.
  7. Anything else, marked clearly with reliability community or mirror.

Verify a forum answer older than a year against current docs before citing it for a fast-moving library, and test any forum answer before it enters the wiki.

Workflow (per source)

1. Identify

For each source you intend to ingest, note:

  • Authority (who maintains it).
  • Version coverage (which versions of the library/spec it covers). A documentation URL with no version in it serves whatever the site publishes now (usually the newest release, on some hosts the development branch), so a value read there (a managed version, a default) belongs to whichever build was current on the day it was read. Read version-bound values from a version-pinned URL or from the artifact's own metadata, and record the version, not a date, as the coverage.
  • Date (when it was last updated upstream).
  • License (whether snapshotting is allowed).
  • Slug (the filename you'll use locally).

2. Fetch

Use the host tool's web-fetch capability. Ingest paywalled or login-walled content only with the user's explicit OK, because its access terms bind the project. If a documentation MCP server is configured (Context7, DeepWiki, llms.txt provider, similar), prefer it for fast, version-aware retrieval.

3. Snapshot (when allowed)

When the license permits, write the raw content to docs/graph/sources/raw/<slug>-<retrieved-date>.<ext>. Acceptable extensions: .md, .html, .pdf, .txt, .json. Keep the raw file whole, to preserve provenance. When the license does not permit a snapshot, link to the source and store no copy; when the host retrieved through an MCP summary and holds no page to keep, there is none to store. Either way, say so in the raw: line of the normalized metadata block. A snapshot with neither the raw file nor the reason is UNJUSTIFIED at the coverage gate: the reason recorded is what makes the omission a decision instead of a habit.

4. Normalize

Produce docs/graph/sources/normalized/<slug>.md:

  • Clean Markdown holding the upstream headings and the decision-relevant content only (navigation chrome, ads, tracking pixels and boilerplate footers go).
  • At the top, the metadata block:
markdown
---
source-title: <title>
source-url: <url>
source-maintainer: <name>
source-version-coverage: <e.g. "1.4 - 1.6">
retrieved: YYYY-MM-DD
license: <SPDX or "see source">
raw: raw/<slug>-<retrieved-date>.<ext> | withheld — <why: the license, a host without fetch, an MCP summary with no page behind it>
reliability: official | community-trusted | community | mirror
---

5. Register

Add a row to docs/graph/sources/index.md:

| <title> | <url> | <maintainer> | YYYY-MM-DD | <version> | <reliability> | <relevance> | <one-line notes> |

6. Draft, then hand back

Draft the wiki page from the normalized sources (skill.library-wiki says what each section owes) and end the turn with the handback payload naming the page, the sources index rows, and tester as recommended_next for the smoke test. The scout is a leaf; the authoring-class docs-librarian finalizes the page in the close-out, the phase order being ingest-library.flow's.

Documentation MCP servers (when available)

If the project has any of these configured, prefer them for fetching upstream content:

  • Context7 (@upstash/context7-mcp): current docs for many libraries, addressable by library ID and version. Use the library-ID form for precision.
  • DeepWiki: open-source repository summaries.
  • llms.txt providers: projects that publish a machine-readable docs index.

When you use one, cite the source in the normalized file's metadata with the MCP server name and the date.

The local wiki is still authoritative for the project. The MCP server gets you upstream content faster; the wiki page is your distillation of what this project actually does with that content.

Disagreement handling

If two sources disagree:

  1. Note the version coverage of each.
  2. Prefer the more recent official source.
  3. If a security advisory disagrees with the docs, the advisory wins.
  4. If the disagreement persists, record both with their versions in the wiki page, and open a question in grill.md §12.

For a non-trivial topic, cross-check against the upstream source code as well, because one source per topic leaves a disagreement nobody can see.

Source reconciliation (lightweight drift check)

Between full research passes, run a cheap periodic reconciliation: diff the currently-resolved dependency versions and manifests/locks against the versions recorded in the library wiki, using only what already resolves locally. Classify each line:

  • no mismatch: the wiki pin still matches what resolves; nothing to do.
  • refresh before the next API-affecting change: the pin has drifted but no work is about to touch that surface; flag it, and re-ingest when work next touches that surface.
  • superseded — treat as historical: the recorded version is gone from the resolved set; mark the wiki content as historical.

This catches silent pin drift that accumulates between full passes, at a fraction of the cost. It is distinct from a full research pass (which re-fetches and re-normalizes upstream content) and from validate-knowledge (which tests whether the wiki prose is navigable and correct, not whether its pins are still current).

Reference files

  • docs/graph/templates/library-page.template.md : where this skill's output ultimately lands.
  • docs/graph/protocols/ingest-library.md : the parent protocol.
  • docs/graph/agents/10-research-scout.md : the agent that runs this.
  • docs/graph/skills/library-wiki.md : the wiki-maintenance skill.

Frequently asked questions

What does the Research And Ingest AI skill do?

Find authoritative sources on the web (or via documentation MCP servers like Context7/DeepWiki), download or snapshot them when allowed, normalize them into clean Markdown, and stage them for the library-wiki. Use whenever a new library/spec/API/standard is being added, a wiki page is stale, an ADR needs current evidence, or a behavior best practice needs verification. Always prefer official upstream sources and version-pinned content over training data.

Why use Research And Ingest on TypingMind?

Because you install it once and use it with any model. Research And Ingest is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Research And Ingest in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/llopresto87/Cypress/tree/main/skills/research-and-ingest. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Research And Ingest?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Research And Ingest?

As many as you like. As long as a model supports skills, you can use Research And Ingest with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Research And Ingest AI skill free?

Yes. It is published on GitHub by llopresto87 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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