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Story Review Test Fix

Community
MinhLN1234
story-review-test-fix

Verify that a finished story's implementation actually satisfies its acceptance criteria and GDD/epic spec, run or check its tests, and propose fixes for failures, without silently changing the spec to match the code. Use this as a deeper check alongside or before ccgs-story-done, such as when a story is marked ready for completion review, when tests are failing and need patching, or when the user asks "does this match the spec", "is this story actually done", or "why is this test failing." Distinct from unity-bug-root-cause (which traces unexplained bugs) — this skill specifically checks implementation against a written spec.

Overview

PublisherMinhLN1234
RepositoryClaude-Cowork-Game-Studio
Skill namestory-review-test-fix
Stars
79
Forks
31
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 MinhLN1234 on GitHub. Read the source before you install it.

Installation

Install the Story Review Test Fix 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/MinhLN1234/Claude-Cowork-Game-Studio/tree/master/story-review-test-fix
  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/MinhLN1234/Claude-Cowork-Game-Studio.git /tmp/Claude-Cowork-Game-Studio
mkdir -p .claude/skills
cp -r /tmp/Claude-Cowork-Game-Studio/story-review-test-fix .claude/skills/story-review-test-fix
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Story Review Test Fix 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 Story Review Test Fix 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 Story Review Test Fix 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.

Story Review + Test Fix

ccgs-story-done handles the completion ritual (status updates, surfacing the next story). This skill is the verification work underneath it: actually proving the code does what the story and GDD say it should, and fixing test failures in a way that respects the spec rather than bending the spec to whatever the code currently does.

Step 1: Read the spec before the code

Pull the story file and trace its acceptance criteria back to the governing GDD section, epic, and any ADRs it cites. Write down each acceptance criterion as a separate, checkable statement before looking at the implementation — this avoids the common failure mode of skimming the code, deciding it "looks right," and retrofitting justification. If the project doesn't use formal story files, use whatever written spec exists (ticket, design doc, user's description) as the source of truth.

Step 2: Map criteria to code, one at a time

For each acceptance criterion, find the specific code that's supposed to satisfy it. If you can't find code that addresses a criterion, that's a gap — don't assume it's handled elsewhere without checking. If a criterion is ambiguous enough that two different implementations would both seem to satisfy it, flag the ambiguity rather than picking an interpretation silently.

Step 3: Run the tests, read the failures literally

When a test fails, read what it's actually asserting before deciding the test is "just out of date." Three possibilities, in order of how often each turns out to be true:

  1. The implementation is wrong — it doesn't match the documented rule (check the GDD/design-doc value directly, not what the developer probably intended).
  2. The test is wrong — it encodes an outdated or incorrect expectation. Verify against the spec before changing it; don't change a test just to make it pass.
  3. The spec itself is ambiguous or has changed — in this case, don't unilaterally resolve it. Surface the discrepancy to the user explicitly; deviations from the GDD/ADRs should be flagged, not quietly absorbed into the code.

Step 4: Fix at the right layer

If the fix touches a numeric rule or threshold (health, timing/cooldown, transition type, spawn/wave activation), check whether that value is referenced anywhere else — other scripts, other docs — and keep them in sync rather than fixing one occurrence. Fixing one place and missing a duplicate is a common way the same bug returns later.

Step 5: Independent re-check from a fresh context (review mode full only)

The context that wrote or fixed the code shares its blind spots: it knows what the code was meant to do and reads that intent into what it actually does. In full review mode, get a second verdict from a context that has never seen this conversation. (Pattern adapted from ECC's santa-method and fresh-context code-reviewer, github.com/affaan-m/ECC, MIT.)

Resolve the review mode: --review [full|lean|solo] argument if given, else production/review-mode.txt, else lean. In lean or solo, skip this step and write "Independent re-check: skipped ([mode] mode)" in the report. The user can always ask for it explicitly in any mode.

In full mode:

  1. Launch one general-purpose subagent via the Agent tool. Give it only:
    • the story file path and the governing GDD section and ADR paths from Step 1,
    • the list of files changed for this story (paths only; it reads them itself),
    • the test files for this story. Do not pass your criteria list, your Step 2 mapping, your Step 3 conclusions, or any summary of this conversation. Its value comes from not knowing them.
  2. Brief it: "Read the story and its GDD/ADR references. Write down each acceptance criterion yourself. For each one, find the code and test that satisfy it and give PASS / FAIL / UNCLEAR with file:line evidence. Check every numeric value against the GDD, not against comments in the code. Do not edit any file."
  3. Compare its verdicts with yours, criterion by criterion:
    • Both PASS: done.
    • Any disagreement, or any UNCLEAR: re-read the evidence yourself. If the subagent is right, treat it as a FAIL and go back to Step 3 or 4. If you are confident it is wrong, keep your verdict but list the disagreement in the report with both pieces of evidence, so the user decides.
    • A criterion the subagent found that you did not list in Step 1: you misread the story. Add it and check it.
  4. Run at most two rounds (initial re-check, then one re-check after fixes). If they still disagree after that, stop and escalate to the user rather than looping.

Step 6: Report, don't just "mark done"

Before handing off to ccgs-story-done for the completion ritual, give a clear pass/fail per acceptance criterion:

## Acceptance criteria check
- [criterion 1]: PASS / FAIL — [evidence: file/test/line]
- [criterion 2]: PASS / FAIL — [evidence]

## Test results
[which tests ran, which failed, root cause of each failure per Step 3's three categories]

## Spec deviations found
[anything where code and GDD/ADR disagree — flagged, not silently resolved]

## Fixes applied
[what was changed and why, including any other code/doc locations updated for consistency]

## Independent re-check
[full mode: agreed on N/M criteria; each disagreement with both verdicts and evidence, and how it was resolved. Otherwise: "skipped ([mode] mode)"]

Only treat a story as genuinely ready for ccgs-story-done once every criterion is a clear PASS or the user has explicitly accepted a documented deviation, and, in full mode, every re-check disagreement has been resolved or accepted by the user.

Frequently asked questions

What does the Story Review Test Fix AI skill do?

Verify that a finished story's implementation actually satisfies its acceptance criteria and GDD/epic spec, run or check its tests, and propose fixes for failures, without silently changing the spec to match the code. Use this as a deeper check alongside or before ccgs-story-done, such as when a story is marked ready for completion review, when tests are failing and need patching, or when the user asks "does this match the spec", "is this story actually done", or "why is this test failing." Distinct from unity-bug-root-cause (which traces unexplained bugs) — this skill specifically checks i...

Why use Story Review Test Fix on TypingMind?

Because you install it once and use it with any model. Story Review Test Fix 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 Story Review Test Fix in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/MinhLN1234/Claude-Cowork-Game-Studio/tree/master/story-review-test-fix. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Story Review Test Fix?

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 Story Review Test Fix?

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

Is the Story Review Test Fix AI skill free?

It is published on GitHub by MinhLN1234. 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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