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Verify Done

Community
goatstarter
verify-done

Evidence check before claiming completion. Use before telling the human a task is done, fixed, or working, especially at the end of long or multi-part tasks. Verifies the work actually runs and every requirement is demonstrably met, then formats an honest report. Complements self-review, which checks code cleanliness; this checks truth. Also invoked manually via /verify-done.

Overview

Publishergoatstarter
Repositorygoat-fable
Skill nameverify-done
Stars
62
Forks
8
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 goatstarter on GitHub. Read the source before you install it.

Installation

Install the Verify Done 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/goatstarter/goat-fable/tree/main/skills/verify-done
  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/goatstarter/goat-fable.git /tmp/goat-fable
mkdir -p .claude/skills
cp -r /tmp/goat-fable/skills/verify-done .claude/skills/verify-done
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Verify Done 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 Verify Done 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 Verify Done 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.

Verify Done

"Done" is a claim about reality, and this skill is where the claim gets paid for. The rule: every statement in your final report must be traceable to a tool result from this session. If you're about to report something you didn't observe, either observe it now or label it unverified.

1. Rebuild the requirement list

From the ORIGINAL request (re-read it, slowly, including any mid-task amendments the human sent): write every explicit and implicit requirement as a checklist. Implicit ones count: "add the endpoint" implies auth handling like its siblings, types, and not breaking the existing routes.

2. Demonstrate each requirement

For each item, run the check that proves it and record the actual result:

  • Logic change → the tests that exercise it (npm test -- <affected>, pytest path/). No coverage? Exercise it by hand: script, REPL, curl.
  • Bug fix → the original failing reproduction, re-run, now passing. A fix without this run is a hypothesis, not a fix.
  • Interface/type change → full build/typecheck (breakage lands in callers, not the file you edited).
  • Behavior/UI → actually run/render it. Compiling is not rendering; 200 OK is not correct output.
  • Config/env → boot the consumer of the config.
  • Refactor → suite green, before and after.

Then one wider pass if cheap: the full affected test suite or build, for collateral damage your narrow checks missed.

Genuinely unverifiable here (no env, missing credentials, no test infra)? That item's status is "unverified because X", stated in the report. Not silence, and not "should work".

3. Confirm the working tree is honest

git status + git diff --stat: only intended files changed, no forgotten scratch files, no debug artifacts. (If you haven't done the quality pass, run /self-review; it's the cleanliness half of finishing.)

4. Write the report

Shape (adapt, don't template-fill):

  1. Outcome, first sentence: what changed / what was found.
  2. Evidence: per requirement, what you ran and what it returned. Real commands, real numbers ("14/14 pass", "build clean", "curl returns the CSV with 3 rows").
  3. Not verified: each unverified item + why + what it would take.
  4. Judgment calls: decisions you made that the human might have made differently.
  5. Left alone: things noticed but deliberately not touched.

Language check before sending: no "should work", no success verbs for unexecuted code, no failure softened into "minor issue", no part skipped silently. If any part failed: that goes in sentence one too, not paragraph four.

Frequently asked questions

What does the Verify Done AI skill do?

Evidence check before claiming completion. Use before telling the human a task is done, fixed, or working, especially at the end of long or multi-part tasks. Verifies the work actually runs and every requirement is demonstrably met, then formats an honest report. Complements self-review, which checks code cleanliness; this checks truth. Also invoked manually via /verify-done.

Why use Verify Done on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/goatstarter/goat-fable/tree/main/skills/verify-done. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Verify Done?

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 Verify Done?

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

Is the Verify Done AI skill free?

Yes. It is published on GitHub by goatstarter 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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