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Create Prove

Organization
aeonfun
create-prove

Run a changed Aeon skill for real and attach SHA-bound behavioral evidence to its PR

Overview

Publisheraeonfun
Repositoryaeon
Skill namecreate-prove
Stars
771
Forks
280
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 aeonfun on GitHub. Read the source before you install it.

Installation

Install the Create Prove 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/aeonfun/aeon/tree/main/skills/create-prove
  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/aeonfun/aeon.git /tmp/aeon
mkdir -p .claude/skills
cp -r /tmp/aeon/skills/create-prove .claude/skills/create-prove
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Create Prove 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 Create Prove 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 Create Prove 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.

${var} - Required immutable target in the form owner/repo#pr@40-character-lowercase-sha.

Today is ${today}. Prove the behavior of one Aeon-shaped change by running the changed skill through the target repository's real aeon.yml workflow. A green diff review is not proof. A successful, correlated Actions run is proof.

Scope

This first implementation supports Aeon-shaped pull requests that change exactly one runnable skills/<slug>/SKILL.md. It does not launch conventional applications and it does not guess which skill represents a workflow-only or configuration-only change. Unsupported target shapes must fail closed without posting a proof receipt.

Never prove create-prove by recursively dispatching itself. Exit PROVE_UNSUPPORTED instead.

Steps

  1. Parse ${var} into target=owner/repo#pr and expected_sha. Reject any value outside the exact grammar above with PROVE_INVALID_TARGET.
  2. Read the PR through gh api. Require all of the following:
    • the PR is open;
    • its current head.sha equals expected_sha;
    • its head branch belongs to the same repository, because gh workflow run --ref cannot execute an untrusted fork branch in the base repository;
    • exactly one changed path matches skills/<slug>/SKILL.md;
    • the slug is not create-prove. Any mismatch exits PROVE_UNSUPPORTED or PROVE_STALE without a receipt.
  3. Inspect the changed skill's frontmatter and instructions. Choose the smallest real, non-destructive variable that exercises the changed behavior. If no safe real invocation exists, exit PROVE_UNSAFE rather than inventing evidence. Do not use synthetic credentials or a dry-run mode.
  4. Dispatch the target branch's workflow by filename, with a unique correlation ID whose dispatch_id must start with the literal prefix prove- — .github/workflows/aeon.yml's commit-skip guard only recognizes that exact prefix to know this run is being proved, not a normal dispatch, and must not commit or push to the branch it's proving. Getting this prefix wrong silently defeats the immutable-head guarantee this whole skill exists to provide:
    bash
    dispatch_id="prove-${pr_number}-$(date -u +%Y%m%dT%H%M%SZ)-${RANDOM}"
    gh workflow run aeon.yml --repo "$repo" --ref "$head_branch" \
      -f skill="$skill" -f var="$proof_var" -f dispatch_id="$dispatch_id"
    Discover the run only by the exact correlated run title, using the same rule as chain-runner.yml. Never select merely the newest run for that skill.
  5. Wait up to 30 minutes. Require status=completed and conclusion=success. Fetch the run log and the captured skill output. Confirm the output is non-empty and does not contain _No output captured._. A successful Actions wrapper with no captured behavior is PROVE_MISSING_EVIDENCE.
  6. Re-read the PR and require its head SHA still equals expected_sha.
  7. Post one PR comment containing a concise description of the exercised path, the run URL, a short output excerpt, and exactly one final machine receipt:
    text
    <!-- aeon-proof:{"schema":1,"target":"owner/repo#N","sha":"<sha>","kind":"aeon-skill","skill":"<slug>","evidence_run_id":123,"evidence_url":"https://github.com/owner/repo/actions/runs/123","verdict":"proven"} -->
    Construct the JSON with jq -cn, then render it on one line. Do not post the receipt until every gate above passes.
  8. End with the target, skill, run ID, run URL, and PROVE_VERDICT=proven in the captured output.

Constraints

  • The proof run must execute the PR head branch, not main.
  • Never treat CI checks, source inspection, or the prior review receipt as behavioral evidence.
  • Never post a proven receipt for a failed, cancelled, timed-out, stale, empty-output, unsupported, or unsafe run.
  • Do not merge, close, approve, or modify the target PR.
  • Do not commit repository files.

Network note

Use gh for every GitHub read, dispatch, log fetch, and PR comment. Authentication is provided by the workflow. Never print tokens or place secret values on a command line.

Log

Append the result to memory/logs/${today}.md under ### create-prove, including the target, SHA, selected skill, evidence run ID, and terminal verdict. The workflow may persist the captured output on your behalf.

Frequently asked questions

What does the Create Prove AI skill do?

Run a changed Aeon skill for real and attach SHA-bound behavioral evidence to its PR

Why use Create Prove on TypingMind?

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

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

Which AI models can use Create Prove?

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 Create Prove?

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

Is the Create Prove AI skill free?

Yes. It is published on GitHub by aeonfun 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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