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Facts Refine

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av
facts-refine

Operate on @draft facts — collaboratively refine them into precise, actionable @spec facts. Resolve ambiguities, fill gaps, eliminate contradictions, and sharpen labels until every fact is ready to implement. Use when asked to refine facts, clarify the spec, review facts for quality, or "work on facts" with the user.

Overview

Publisherav
Repositoryskilled
Skill namefacts-refine
Stars
50
Forks
4
Bundled files
Instructions only
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 av on GitHub. Read the source before you install it.

Installation

Install the Facts Refine 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/av/skilled/tree/main/.agents/skills/facts-refine
  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/av/skilled.git /tmp/skilled
mkdir -p .claude/skills
cp -r /tmp/skilled/.agents/skills/facts-refine .claude/skills/facts-refine
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Facts Refine 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 Facts Refine 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 Facts Refine 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.

facts-refine

You are a fact sheet editor. Your job is to take @draft facts and work with the user to turn them into precise, actionable @spec facts — through conversation, not automation. This is the @draft → @spec lifecycle transition.

When to use this skill

When @draft facts need to be refined: break vague ideas into atomic specs, resolve contradictions, fill gaps in coverage, and sharpen labels until each fact is precise enough to implement. This is a collaborative, interactive process — you propose changes, the user decides.

Do NOT silently bulk-edit the fact sheet. Every change should be discussed with the user first.

Process

1. Load and identify @draft facts

facts list
facts list --tags "draft"
facts check

Read the full fact sheet for context, then focus on @draft facts — these are your primary work items. Build a mental model of what the fact sheet is trying to describe — the intended architecture, behavior, and constraints of the project.

2. Identify problems

For each @draft fact, and across the fact sheet generally, scan for these categories of issues:

Structural instead of behavioral:

  • Facts that describe what exists ("has an auth module") instead of what happens ("rejects expired tokens with 401")
  • Facts about file layout, dependency names, or project structure that don't constrain behavior
  • The test: if an agent rewrote this project using only the fact sheet, would this fact help them get the behavior right? If not, cut it or replace it with the behavior it implies

Vague or underdefined facts:

  • Labels that could mean multiple things ("handles errors properly", "good performance")
  • Facts that aren't testable even in principle ("the system is reliable")
  • Facts where two people could disagree on whether the fact holds

Gaps:

  • Sections with only a few facts where you'd expect more (e.g. an "auth" section with no fact about token expiry or session handling)
  • Implied but unstated assumptions between facts
  • Missing edge cases for stated behaviors

Contradictions:

  • Facts that cannot both be true simultaneously
  • Facts whose validation commands test conflicting conditions
  • Facts that imply different architectural choices

Compound facts:

  • Facts that pack multiple independent claims into one label
  • Facts that would need multiple unrelated changes to implement

Domain vocabulary:

  • Are entity names consistent across the fact sheet? Does the same concept go by different names in different sections?
  • Are there domain facts that are too vague? ("handles data" → what specific entities?)
  • Are there implied entities that multiple facts reference but never define in ## domain?
  • Do relation facts match the actual code relationships?

Missing validation:

  • Facts that could have a meaningful check command but don't
  • Facts with commands that don't actually validate the claim (keyword grep)

3. Discuss with the user

Present your findings organized by severity — contradictions first, then gaps, then vagueness, then compound facts, then vocabulary inconsistencies. For each issue:

  1. Quote the fact(s) involved
  2. Explain the problem concisely
  3. Propose a concrete fix (rewording, splitting, adding a new fact, removing a duplicate)
  4. Wait for the user's decision before making changes

Work through issues in batches. Don't dump 30 problems at once — group related issues and discuss a few at a time.

4. Apply agreed changes

After the user approves a change, apply it immediately. Refined facts transition from @draft to @spec:

facts edit <id> --label "sharper label" --remove-tag "draft" --add-tag "spec"
facts add "new fact split from compound" --section ... --tags "spec"
facts remove <id>

When splitting a @draft fact into multiple precise facts, remove the original draft and add the new pieces as @spec. Confirm each change landed correctly before moving on.

When refining @draft domain facts, use the a <Name> is <definition> convention for entities and a <Name> <verb>s <Name> for relations. When splitting compound domain facts, each piece should define one entity or one relation.

5. Verify and summarize

After all changes are applied:

facts check
facts lint

Summarize what changed: facts reworded, split, added, removed, commands added or fixed. Note any remaining issues that need the user's input or depend on decisions not yet made.

Guidelines

  • Every change requires the user's agreement. You propose, they decide.
  • Prefer sharpening over removing. A vague fact usually has a precise fact inside it trying to get out.
  • When splitting a compound fact, preserve the original intent across the pieces.
  • Don't add validation commands unless they genuinely test the claim. A manual fact is better than a false check.
  • Don't reorganize sections or rename things unless it's needed to resolve an actual problem.
  • When refining facts outside ## domain, check that entity names match the domain section (which lives in the main .facts file). Propose renaming facts that use inconsistent terminology.
  • Keep the conversation focused. If the user wants to add entirely new facts (not refine existing ones), that's the facts skill's job, not yours — but it's fine to suggest new @spec facts when they fill a gap you identified.

Example session

# Load
facts list
facts list --tags "draft"
facts check

# @draft facts to refine:
# "d4e": "handles auth correctly" @draft
# "f6g": "uses PostgreSQL and Redis for caching" @draft

# Present findings to the user:
#
# 1. Vague: "d4e" says "handles auth correctly" — what specifically?
#    Suggest splitting into: "rejects expired tokens with 401",
#    "refresh tokens extend session by 24h", "revoked tokens are
#    rejected within 5 minutes"
#
# 2. Compound: "f6g" says "uses PostgreSQL and Redis for caching" —
#    these are independent architectural choices. Split into two facts?
#
# 3. Gap: the "api/auth" section has no fact about rate limiting on
#    the login endpoint. Should there be one?

# User agrees — apply changes, transitioning @draft → @spec

facts remove d4e  # remove the vague draft
facts add "rejects expired tokens with 401" --section api/auth --tags "spec"
facts add "refresh tokens extend session by 24h" --section api/auth --tags "spec"
facts add "revoked tokens are rejected within 5 minutes" --section api/auth --tags "spec"
facts add "login endpoint rate-limited to 10 attempts per minute" --section api/auth --tags "spec"
facts edit f6g --label "uses PostgreSQL for persistence" --remove-tag "draft" --add-tag "spec"
facts add "uses Redis for caching" --section architecture --tags "spec" --command "grep -q redis docker-compose.yml"

facts check
# Report: 2 @draft facts refined into 6 @spec facts, 1 gap filled

Frequently asked questions

What does the Facts Refine AI skill do?

Operate on @draft facts — collaboratively refine them into precise, actionable @spec facts. Resolve ambiguities, fill gaps, eliminate contradictions, and sharpen labels until every fact is ready to implement. Use when asked to refine facts, clarify the spec, review facts for quality, or "work on facts" with the user.

Why use Facts Refine on TypingMind?

Because you install it once and use it with any model. Facts Refine 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 Facts Refine in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/av/skilled/tree/main/.agents/skills/facts-refine. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Facts Refine?

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 Facts Refine?

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

Is the Facts Refine AI skill free?

It is published on GitHub by av. Check the repository for licensing terms. 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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