test-evidence-review▌
Donchitos/Claude-Code-Game-Studios · updated Apr 16, 2026
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### Test Evidence Review
- ›name: test-evidence-review
- ›description: "Quality review of test files and manual evidence documents. Goes beyond existence checks — evaluates assertion coverage, edge case handling, naming conventions, and evidence completeness
- ›argument-hint: "[story-path | sprint | system-name]"
| name | test-evidence-review |
| description | "Quality review of test files and manual evidence documents. Goes beyond existence checks — evaluates assertion coverage, edge case handling, naming conventions, and evidence completeness. Produces ADEQUATE/INCOMPLETE/MISSING verdict per story. Run before QA sign-off or on demand." |
| argument-hint | "[story-path | sprint | system-name]" |
| user-invocable | true |
| allowed-tools | Read, Glob, Grep, Write |
Test Evidence Review
/smoke-check verifies that test files exist and pass. This skill
goes further — it reviews the quality of those tests and evidence documents.
A test file that exists and passes may still leave critical behaviour uncovered.
A manual evidence doc that exists may lack the sign-offs required for closure.
Output: Summary report (in conversation) + optional production/qa/evidence-review-[date].md
When to run:
- Before QA hand-off sign-off (
/team-qaPhase 5) - On any story where test quality is in question
- As part of milestone review for Logic and Integration story quality audit
1. Parse Arguments
Modes:
/test-evidence-review [story-path]— review a single story's evidence/test-evidence-review sprint— review all stories in the current sprint/test-evidence-review [system-name]— review all stories in an epic/system- No argument — ask which scope: "Single story", "Current sprint", "A system"
2. Load Stories in Scope
Based on the argument:
Single story: Read the story file directly. Extract: Story Type, Test Evidence section, story slug, system name.
Sprint: Read the most recently modified file in production/sprints/.
Extract the list of story file paths from the sprint plan. Read each story file.
System: Glob production/epics/[system-name]/story-*.md. Read each.
For each story, collect:
Type:field (Logic / Integration / Visual/Feel / UI / Config/Data)## Test Evidencesection — the stated expected test file path or evidence doc- Story slug (from file name)
- System name (from directory path)
- Acceptance Criteria list (all checkbox items)
3. Locate Evidence Files
For each story, find the evidence:
Logic stories: Glob tests/unit/[system]/[story-slug]_test.*
- If not found, also try: Grep in
tests/unit/[system]/for files containing the story slug
Integration stories: Glob tests/integration/[system]/[story-slug]_test.*
- Also check
production/session-logs/for playtest records mentioning the story
Visual/Feel and UI stories: Glob production/qa/evidence/[story-slug]-evidence.*
Config/Data stories: Glob production/qa/smoke-*.md (any smoke check report)
Note what was found (path) or not found (gap) for each story.
4. Review Automated Test Quality (Logic / Integration)
For each test file found, read it and evaluate:
Assertion coverage
Count the number of distinct assertions (lines containing assert, expect, check, verify, or engine-specific assertion patterns). Low assertion count is a quality signal — a test that makes only 1 assertion per test function may not cover the range of expected behaviour.
Thresholds:
- 3+ assertions per test function → normal
- 1-2 assertions per test function → note as potentially thin
- 0 assertions (test exists but no asserts) → flag as BLOCKING — the test passes vacuously and proves nothing
Edge case coverage
For each acceptance criterion in the story that contains a number, threshold, or "when X happens" conditional: check whether a test function name or test body references that specific case.
Heuristics:
- Grep test file for "zero", "max", "null", "empty", "min", "invalid", "boundary", "edge" — presence of any is a positive signal
- If the story has a Formulas section with specific bounds: check whether tests exercise at minimum/maximum values
Naming quality
Test function names should describe: the scenario + the expected result.
Pattern: test_[scenario]_[expected_outcome]
Flag functions named generically (test_1, test_run, testBasic) as
naming issues — they make failures harder to diagnose.
Formula traceability
For Logic stories where the GDD has a Formulas section: check that the test file contains at least one test whose name or comment references the formula name or a formula value. A test that exercises a formula without mentioning it by name is harder to maintain when the formula changes.
5. Review Manual Evidence Quality (Visual/Feel / UI)
For each evidence document found, read it and evaluate:
Criterion linkage
The evidence doc should reference each acceptance criterion from the story. Check: does the evidence doc contain each criterion (or a clear rephrasing)? Missing criteria mean a criterion was never verified.
Sign-off completeness
Check for three sign-off lines (or equivalent fields):
- Developer sign-off
- Designer / art-lead sign-off (for Visual/Feel)
- QA lead sign-off
If any are missing or blank: flag as INCOMPLETE — the story cannot be fully closed without all required sign-offs.
Screenshot / artefact completeness
For Visual/Feel stories: check whether screenshot file paths are referenced in the evidence doc. If referenced, Glob for them to confirm they exist.
For UI stories: check whether a walkthrough sequence (step-by-step interaction log) is present.
Date coverage
Evidence doc should have a date. If the date is earlier than the story's last major change (heuristic: compare against sprint start date from the sprint plan), flag as POTENTIALLY STALE — the evidence may not cover the final implementation.
6. Build the Review Report
For each story, assign a verdict:
| Verdict | Meaning |
|---|---|
| ADEQUATE | Test/evidence exists, passes quality checks, all criteria covered |
| INCOMPLETE | Test/evidence exists but has quality gaps (thin assertions, missing sign-offs) |
| MISSING | No test or evidence found for a story type that requires it |
The overall sprint/system verdict is the worst story verdict present.
## Test Evidence Review
> **Date**: [date]
> **Scope**: [single story path | Sprint [N] | [system name]]
> **Stories reviewed**: [N]
> **Overall verdict**: ADEQUATE / INCOMPLETE / MISSING
---
### Story-by-Story Results
#### [Story Title] — [Type] — [ADEQUATE/INCOMPLETE/MISSING]
**Test/evidence path**: `[path]` (found) / (not found)
**Automated test quality** *(Logic/Integration only)*:
- Assertion coverage: [N per function on average] — [adequate / thin / none]
- Edge cases: [covered / partial / not found]
- Naming: [consistent / [N] generic names flagged]
- Formula traceability: [yes / no — formula names not referenced in tests]
**Manual evidence quality** *(Visual/Feel/UI only)*:
- Criterion linkage: [N/M criteria referenced]
- Sign-offs: [Developer ✓ | Designer ✗ | QA Lead ✗]
- Artefacts: [screenshots present / missing / N/A]
- Freshness: [dated [date] — current / potentially stale]
**Issues**:
- BLOCKING: [description] *(prevents story-done)*
- ADVISORY: [description] *(should fix before release)*
---
### Summary
| Story | Type | Verdict | Issues |
|-------|------|---------|--------|
| [title] | Logic | ADEQUATE | None |
| [title] | Integration | INCOMPLETE | Thin assertions (avg 1.2/function) |
| [title] | Visual/Feel | INCOMPLETE | QA lead sign-off missing |
| [title] | Logic | MISSING | No test file found |
**BLOCKING items** (must resolve before story can be closed): [N]
**ADVISORY items** (should address before release): [N]
7. Write Output (Optional)
Present the report in conversation.
Ask: "May I write this test evidence review to
production/qa/evidence-review-[date].md?"
This is optional — the report is useful standalone. Write only if the user wants a persistent record.
After the report:
- For BLOCKING items: "These must be resolved before
/story-donecan mark the story Complete. Would you like to address any of them now?" - For thin assertions: "Consider running
/test-helpers [system]to see scaffolded assertion patterns for common cases." - For missing sign-offs: "Manual sign-off is required from [role]. Share
[evidence-path]with them to complete sign-off."
Verdict: COMPLETE — evidence review finished. Use CONCERNS if BLOCKING items were found.
Collaborative Protocol
- Report quality issues, do not fix them — this skill reads and evaluates; it does not modify test files or evidence documents
- ADEQUATE means adequate for shipping, not perfect — avoid nitpicking tests that are functioning and comprehensive enough to give confidence
- BLOCKING vs. ADVISORY distinction is important — only flag BLOCKING when the gap leaves a story criterion genuinely unverified
- Ask before writing — the report file is optional; always confirm before writing
How to use test-evidence-review on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add test-evidence-review
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches test-evidence-review from GitHub repository Donchitos/Claude-Code-Game-Studios and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate test-evidence-review. Access the skill through slash commands (e.g., /test-evidence-review) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices▌
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This▌
✓ Use When
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid When
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.8★★★★★71 reviews- ★★★★★Noah Srinivasan· Dec 28, 2024
Useful defaults in test-evidence-review — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Anaya Abbas· Dec 24, 2024
test-evidence-review reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Anaya Farah· Dec 24, 2024
Registry listing for test-evidence-review matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Anaya Wang· Dec 24, 2024
I recommend test-evidence-review for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Kofi Desai· Dec 20, 2024
test-evidence-review fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Olivia Torres· Dec 8, 2024
test-evidence-review is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Chaitanya Patil· Dec 4, 2024
We added test-evidence-review from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Li Abebe· Dec 4, 2024
test-evidence-review has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Advait Brown· Nov 27, 2024
Solid pick for teams standardizing on skills: test-evidence-review is focused, and the summary matches what you get after install.
- ★★★★★Piyush G· Nov 23, 2024
test-evidence-review fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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