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Verify Before Done

Community
andreaswasita
verify-before-done

Proves work with tests, diffs, and logs before sign-off.

Overview

Publisherandreaswasita
Repositorycopilot-agents-dojo
Skill nameverify-before-done
Stars
54
Forks
15
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 andreaswasita on GitHub. Read the source before you install it.

Installation

Install the Verify Before 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/andreaswasita/copilot-agents-dojo/tree/main/skills/verify-before-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/andreaswasita/copilot-agents-dojo.git /tmp/copilot-agents-dojo
mkdir -p .claude/skills
cp -r /tmp/copilot-agents-dojo/skills/verify-before-done .claude/skills/verify-before-done
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Verify Before 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 Before 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 Before 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 Before Done Skill

Refuses to mark a task complete without concrete evidence from both automated checks and the real artifact or user-facing surface. Produces an explicit VERIFIED, NOT VERIFIED, or INCONCLUSIVE verdict. Does NOT replace scripts/verify.sh — it tells the agent when to run it and what evidence the gate cannot supply.

When to Use

  • Before flipping any step in tasks/todo.md from - [ ] to - [x].
  • Before opening a pull request.
  • After any fix or feature implementation.
  • After performance work, migrations, or changes whose success depends on runtime behavior.
  • Any time the agent is about to say "done" or "ready" without evidence.

Prerequisites

  • A test runner installed for the stack (pytest, vitest, go test, etc.).
  • scripts/verify.sh (or scripts/run-checks.ps1 on Windows) executable.
  • The powershell tool to invoke runners and git.
  • git available so diffs can be inspected.

How to Run

text
1. State the success predicate and capture a baseline when behavior is changing.
2. Run the project's tests and the dojo gate via the `powershell` tool.
3. Exercise the real artifact or user-facing surface.
4. Inspect the diff and working tree.
5. Record evidence and assign a verdict.
6. Mark the step complete only when the verdict is `VERIFIED`.

Quick Reference

CheckCommand (run via powershell)Pass criterion
Tests passpytest / npm test / go test ./... / dotnet test / mvn testexit 0, no failures reported
Dojo gatebash scripts/verify.sh --checkexit 0
Real artifactMatching browser, CLI, API, package, migration, or runtime checkobserved result satisfies the stated predicate
Baseline comparisonSame measurement before and after the changeresult moves from failing/old state to expected state
Diff summarygit diff main --statmatches plan in tasks/todo.md
No regressionsgit diff main -- <touched paths>only intended changes
Clean treegit status --porcelainempty output (or expected untracked)
Evidence capturededit tasks/todo.mdVerification Results block added
VerdictMeaningCompletion allowed?
VERIFIEDEvidence proves the success predicate on the real artifact.Yes
NOT VERIFIEDEvidence proves the result is wrong or regressed.No
INCONCLUSIVEThe check was unavailable, used the wrong surface, or could not distinguish success from failure.No

Procedure

Step 1: State the Predicate and Baseline

Write one falsifiable sentence describing success before running checks. For a bug, capture the failing behavior first. For performance work, record the original measurement. For a migration, record the pre-change state and the expected post-change invariant.

Examples:

  • "Submitting an expired token returns HTTP 401."
  • "The p95 latency for this fixture is below 200 ms."
  • "Running the migration twice leaves the schema and data unchanged."

If no meaningful baseline exists, state why. Do not invent one after seeing the result.

Step 2: Run the Full Test Suite

Run via the powershell tool — not "it compiles", not "the unit test I wrote". The full suite catches regressions in adjacent code.

If the area you changed has no tests, write one as part of the task. An untested change is unverified by definition.

Step 3: Run the Dojo Gate

bash
bash scripts/verify.sh --check

This wraps the spec/plan/actions/tests checks in CI parity mode. If it fails locally, it will fail in CI.

Step 4: Exercise the Real Artifact

Use the matching surface:

ChangeRequired runtime evidence
Browser or desktop UIDrive the affected flow and capture the observed state or screenshot.
CLI or TUIRun the command with representative inputs and inspect exit code and output.
APISend a representative request and inspect status, headers, and response body.
Package or libraryExercise the public entry point from a consumer-shaped test or example.
MigrationRun forward, validate invariants, and test the supported retry or rollback path.
PerformanceRepeat the same measurement method used for the baseline.

Tests prove code paths. The real-surface check proves the reported problem or requested outcome. A check on the wrong surface is INCONCLUSIVE, not a pass.

Step 5: Diff Against Main

bash
git diff main --stat
git diff main

The change set must match the plan in tasks/todo.md. Unexpected files in the diff are a red flag — either the plan is stale or you've leaked scope.

Step 6: Clean Tree Check

bash
git status --porcelain

Output must be empty (or contain only deliberately-untracked files). Debug prints, leftover scratch files, and stray .tmp artifacts all surface here.

Step 7: Record Evidence and Verdict

Append to tasks/todo.md under the current task:

markdown
### Verification Results
- Predicate: Expired tokens return HTTP 401.
- Verdict: VERIFIED
- Baseline: expired token returned HTTP 500 before the fix
- [x] Tests: 47 passed, 0 failed (`pytest tests/`)
- [x] Dojo gate: `verify.sh --check` PASS
- [x] Real artifact: API request returned HTTP 401 with expected body
- [x] Diff: 3 files changed, +84/-12 (matches plan)
- [x] Clean tree
- [x] Evidence: <paste relevant test output, log snippet, or screenshot ref>

Only a VERIFIED block permits flipping the step to - [x]. Record NOT VERIFIED and INCONCLUSIVE honestly, then keep working or report the blocker.

Step 8: Staff-Engineer Sniff Test

Final question: "Would a senior reviewer approve this on the evidence alone?" If no, add the missing evidence before claiming done.

Pitfalls

  • DO NOT say "done" without a Verification Results block. Verbal claims don't count.
  • DO NOT skip tests because "it's a small change." Small changes cause big outages.
  • DO NOT test only the happy path. Edge cases are where bugs live.
  • DO NOT ignore flaky tests. A flaky test is a test that sometimes catches bugs — fix it, don't ignore it.
  • DO NOT trust "it compiles." Compilation is the lowest possible bar.
  • DO NOT treat unit tests as proof that a user-visible or runtime problem is gone.
  • DO NOT convert an unavailable or wrong-surface check into a pass. Use INCONCLUSIVE.
  • DO NOT change the measurement method between baseline and result.
  • DO NOT mark a step done before scripts/verify.sh --check exits 0.

Verification

  • Test suite ran to completion with exit 0.
  • scripts/verify.sh --check (or run-checks.ps1 -Check) passed.
  • The real artifact or user-facing surface satisfies the stated predicate.
  • Baseline and result used the same measurement method when comparison applies.
  • git diff main --stat matches the plan.
  • git status --porcelain is empty.
  • tasks/todo.md has a Verification Results block with an explicit verdict.

Frequently asked questions

What does the Verify Before Done AI skill do?

Proves work with tests, diffs, and logs before sign-off.

Why use Verify Before Done on TypingMind?

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

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

Which AI models can use Verify Before 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 Before Done?

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

Is the Verify Before Done AI skill free?

Yes. It is published on GitHub by andreaswasita 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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