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Facts

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facts

Manage .facts files — atomic, validatable truth statements about a project. Install, check, list, add, edit, remove, and lint facts via the CLI. ALWAYS read this skill when the user mentions facts in any capacity.

Overview

Publisherav
Repositoryskilled
Skill namefacts
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 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
  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 .claude/skills/facts
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Facts 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 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 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

A CLI for fact-driven development. You use it to specify what must be true about a project, then validate that reality matches.

Project: https://github.com/av/facts

Installing

If facts is not installed, install it with one of:

bash
curl -fsSL https://raw.githubusercontent.com/av/facts/main/install.sh | sh
bash
npm install -g @avcodes/facts
bash
pipx install facts-cli

Verify with facts --version. The command is always facts — never npx facts.

Core idea

A .facts file is a flat list of atomic truth statements about a project. Each fact can optionally have a shell command that verifies it. The fact sheet serves as both specification (what should be true) and documentation (what is true) — the difference is just which direction you're working from.

- the API returns JSON
- label: project builds
  command: cargo build
- label: tests pass
  command: cargo test
  tags: [ci, core]

That's the entire format. Plain strings for simple facts, mappings when you need a command, tags, or explicit ID. Allowed mapping keys: id, label, command, tags — nothing else.

Essential commands

See everything:

facts list
facts list --tags "not implemented"
facts list --has-command
facts list --light                      # markdown-like output with headings and dimmed IDs

Validate:

facts check
facts check --tags "ci"

check is your primary feedback loop. It lints the files first (aborting on structural errors), then runs every command-fact and reports pass/fail/manual. Run it often. Exit 0 means all command-facts pass; manual facts don't affect the exit code.

Manual facts (? in output) are your responsibility. They have no command — you verify them by reading the relevant code. For each ? fact: read what it claims, check the code, then report PASS or FAIL with a one-line reason. Reporting "N manual" without checking each one is not acceptable — those facts exist because they describe behavior that matters.

Add facts:

facts add "users can sign up" --section features/auth
facts add "signup returns 201" --command "curl -s -o /dev/null -w '%{http_code}' localhost:3000/signup | grep 201" --section features/auth

Edit facts:

facts edit <id> --add-tag "implemented"
facts edit <id> --remove-tag "blocked"
facts edit <id> --label "corrected statement"
facts edit <id> --command "new check command"

Prefer --add-tag / --remove-tag over --tags. The latter replaces all tags silently — use it only when you intend a full replacement.

Remove facts:

facts remove <id>

Scaffold a new project:

facts init

Run facts <command> --help for the full flag reference.

How facts work

Files: .facts is the default. Additional sheets use semantic names (cli.facts, api.facts). All *.facts files in the project root are discovered automatically.

Sections: Markdown headings (#, ##, etc.) create hierarchical sections addressable by path (e.g. cli/subcommands). Sections are created when you add to them and removed when their last fact is deleted.

Tags: @word tokens for filtering. Inline for plain strings (- some fact @mvp), tags: key for mappings. Stripped from the label before display and ID hashing. Filter with boolean expressions: --tags "mvp and not blocked".

Lifecycle tags: Three well-known tags drive the agent workflow:

  • @draft — rough idea, needs refinement and atomization
  • @spec — precise and actionable, ready to implement
  • @implemented — true and backed by code
  • Untagged facts are ground truth — verified against the codebase

The lifecycle flows @draft → @spec → @implemented. Each companion skill owns one transition.

IDs: Every fact gets a short ID (3+ chars) derived from its label hash. Stable as long as the label doesn't change. Use --id or --new-id to override.

Validation: Commands run via $SHELL (fallback sh) in the project root. Exit 0 = fact holds. Write commands that are fast and idempotent — they run on every check.

Writing good facts

Facts should describe what the project does — its behaviors, features, and contracts — not what files it has or what libraries it uses. The test: if an agent reimplemented this project using only the fact sheet, would the result behave correctly?

  • Behavioral — describe what happens from the user's perspective ("expired tokens are rejected with 401") not what exists in the code ("there is an auth module")
  • Atomic — one truth per fact, independently verifiable
  • Declarative — state what is true, not what to do ("uses PostgreSQL" not "set up PostgreSQL")
  • Stable — shouldn't change with every commit ("tests pass" not "there are 47 tests")
  • Falsifiable — you could imagine a broken implementation where this fact would not hold

Structural facts (dependencies, file layout) are supporting detail, not the main content. A fact sheet full of "uses X library" and "has Y directory" tells an agent nothing about how the project actually works.

Good validation commands are fast, idempotent, and test one thing. Prefer test -f, grep -q, and short script checks over running full builds.

Domain vocabulary

The ## domain section in the main .facts file defines the project's key entities and their relationships. These are facts too — atomic truth statements — but they describe the conceptual model rather than specific behaviors.

Entity facts name and define a concept — the common pattern is a <Name> is <definition>. Relation facts state how entities connect. Use the defined entity names in natural declarative statements — there is no rigid grammar, but the connection should be specific.

## domain
- a FactSheet is a parsed *.facts file containing sections and facts
- a Section is a heading-delimited group of facts, nestable via heading depth
- a Fact is an atomic truth statement: a label, optional command, optional tags
- a FactSheet contains a preamble and nested Sections
- check validates command-bearing Facts by running them in the project root
- Tags filter Facts via boolean expressions

Use these names consistently across the fact sheet. The domain section lives in the main .facts file. It is built by facts-discover, refined by facts-refine, and extended by facts-implement when new concepts emerge during implementation.

Agent workflows

Start of work — always do this first:

facts list                              # read the full spec
facts check                             # see what holds and what doesn't

Use the fact sheet to orient before writing code. It is the source of truth for what the project should look like and what is already validated.

Define the spec (most common user intent): When the user says "work on facts", "add facts", or "define the spec", they want to collaboratively define what should be true — not audit what already is.

facts add "users can sign up" --section features/auth --tags "draft"
facts add "signup returns 201" --command "curl -s ..." --section features/auth --tags "spec"
  • Discuss with the user what the project should look like
  • Add rough ideas as @draft — they'll be refined into precise specs later
  • Add precise, actionable facts as @spec — they're ready to implement
  • Do NOT remove @draft or @spec facts — they represent intended future work
  • Do NOT run facts-discover unless the user explicitly asks to sync with reality

Track lifecycle progress:

facts list --tags "draft"               # rough ideas to refine
facts list --tags "spec"                # ready to implement
facts list --tags "implemented"         # done
facts list --tags "not implemented"     # all remaining work
facts check                             # verify

Maintain accuracy during coding work: When you add a feature, add corresponding facts. When you fix a bug, verify related facts still hold. When you remove code, remove obsolete facts.

facts check                             # find failing facts
facts edit <id> --label "corrected"     # fix inaccurate facts
facts remove <id>                       # remove obsolete facts
facts add "new truth" --section foo     # add discovered truths

Companion skills

Each skill owns one lifecycle transition:

  • facts-discover — scan the codebase and classify every fact by lifecycle stage (@draft, @spec, @implemented). Use to triage or bootstrap.
  • facts-refine — pick up @draft facts and refine them into precise @spec facts with the user. Use when drafts need sharpening.
  • facts-implement — pick up @spec facts and implement them in code, then tag @implemented. Use when specs are ready to build.

Frequently asked questions

What does the Facts AI skill do?

Manage .facts files — atomic, validatable truth statements about a project. Install, check, list, add, edit, remove, and lint facts via the CLI. ALWAYS read this skill when the user mentions facts in any capacity.

Why use Facts on TypingMind?

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

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

Which AI models can use Facts?

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?

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

Is the Facts 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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