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Extract Knowledge

Community
lexler
extract-knowledge

Extracts what the session learned into prose that stands alone, or reviews existing text through the same distortion lenses.

Overview

Publisherlexler
Repositoryskill-factory
Skill nameextract-knowledge
Stars
239
Forks
63
Bundled files
Instructions only
LicenseApache-2.0
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 lexler on GitHub. Read the source before you install it.

Installation

Install the Extract Knowledge 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/lexler/skill-factory/tree/main/output_skills/ai/extract-knowledge
  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/lexler/skill-factory.git /tmp/skill-factory
mkdir -p .claude/skills
cp -r /tmp/skill-factory/output_skills/ai/extract-knowledge .claude/skills/extract-knowledge
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Extract Knowledge 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 Extract Knowledge 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 Extract Knowledge 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.

STARTER_CHARACTER = 📝

Extract Knowledge

Turn what a session knows into prose that stands alone. The reader is a stranger: they were not in the session and they see only the text. Each lens below defends the stranger against one distortion.

Seats

Decide which seat you are in — it changes what you can verify.

  • Author seat: you did the work, so the knowledge is in your context. You can verify faithfulness directly, but you are half-blind to standalone failures because you cannot un-know the session. Compensate deliberately: reread the draft as the stranger, asking of every referent "do I know this if I only have the text?"
  • Reviewer seat: the argument points at text someone else wrote. You are the stranger, so standalone and prose failures are visible to you directly. For faithfulness, check claims against the source session or transcript if one is reachable; mark the claims you cannot verify instead of guessing.

Steps

  1. Read the argument. A topic means extract that topic. No argument means extract everything important. Existing text or a file path means take the reviewer seat and go straight to the lens gate.

  2. Draft the extraction. Capture what the session learned: decisions with their why, corrections and preferences the user stated, insights discovered, dead ends that cost time, and questions still open. Done when every insight in scope is either in the draft or consciously judged not worth the reader's time.

  3. Run the lens gate below and keep a visible verdict list: a bulleted list, one lens per line — its name, then "clean" or the defect found and fixed. A verdict names a defect caught in the draft; a lens that shaped the draft from the start still reports "clean". A lens without a verdict line was skipped, not passed. Done when the verdict list covers every lens in all three groups.

  4. Output the text in chat, or return it if another agent invoked you. Persistence belongs to the caller: hand over prose, and let the caller decide if and where it becomes a file. The extracted text is the only artifact — the verdict list and any questions to the user are chat output, and stay out of the text however it is saved or passed on.

Lens gate

Look through one lens at a time.

Standalone — the stranger was not there

  • Fourth-wall break — the text addresses the asker or narrates the process ("as requested", "I've now added"). → Rewrite from the reader's point of view, or delete the sentence.
  • Context leakage — a session-private referent appears as if the reader knows it: a scratch folder, an experiment label, "the audit". → Name it in terms the reader has, or drop the referent.
  • Change-relative framing — the text describes the delta ("now uses X", "the new approach") instead of the resulting state, so it is stale on arrival. → Write the timeless state.
  • Orphaned claim — a fact was lifted without the context that made it true: a dangling "it", an unstated precondition, a rule that only held in one setup. → Restore the condition or cut the claim.
  • Salience mismatch — content is ranked by how much session attention it got, not by what the reader needs; trivia sits level with the one thing that matters. → Reorder by reader importance and cut what does not serve the reader.

Faithfulness — the record must match what happened

  • Fabrication — a claim that never happened in the session; where the source was silent, priors filled the gap. → Trace every claim to the session; delete what you cannot trace.
  • Commitment upgrade — "we discussed X" became "we decided X"; a maybe became an action item. → Restore the real speech act.
  • Confidence inflation — hedges, scope limits, and caveats were stripped, so one instance reads as a general rule. → Put the hedge and the scope back.
  • Success theater — failures, dead ends, objections, and open risks vanished; "done" claims exceed what was verified. → Record what failed and what is still open.
  • Detail corruption — a true claim carries a wrong actor, number, date, or sequence. → Check each specific against the source.
  • False causality — independent facts joined by an invented "because"; real causal structure flattened into a story. → Link only what was actually linked.
  • Sycophantic record — the text captures what the user asserted, not what was established; the framing beat the evidence. → Record the conclusion the evidence supports.

Prose — plain writing

  • Padding — the text can be compressed without losing information; detail exceeds the reader's curiosity. → Compress until every word earns its weight.
  • Diff narration — the text restates what its subject already shows: a commit narrating the diff, a comment narrating the code. → Keep only what the subject cannot say itself, usually the why.
  • Significance inflation — small things wear grandiose framing: "comprehensive", "robust", "production-ready". → State plainly what it is.
  • Slop style — slogan fragments ("No X, no Y."), snappy triads, "It's not X, it's Y", walls of bullets, bold everywhere. → Write simple English sentences.
  • Tooling residue — prompt text, raw diffs, or harness machinery leaked verbatim into the text. → Delete it.

Frequently asked questions

What does the Extract Knowledge AI skill do?

Extracts what the session learned into prose that stands alone, or reviews existing text through the same distortion lenses.

Why use Extract Knowledge on TypingMind?

Because you install it once and use it with any model. Extract Knowledge 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 Extract Knowledge in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/lexler/skill-factory/tree/main/output_skills/ai/extract-knowledge. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Extract Knowledge?

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 Extract Knowledge?

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

Is the Extract Knowledge AI skill free?

Yes. It is published on GitHub by lexler under the Apache-2.0 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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