tc-review
Review QA test case documents (Markdown) against source code to find coverage gaps. Use when reviewing test cases, analyzing test coverage, suggesting missing scenarios, or learning codebase to extract testable scenarios. Supports project-based learning from multiple GitHub repos with dual memory (local + Dify RAG).
Best use case
tc-review is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Review QA test case documents (Markdown) against source code to find coverage gaps. Use when reviewing test cases, analyzing test coverage, suggesting missing scenarios, or learning codebase to extract testable scenarios. Supports project-based learning from multiple GitHub repos with dual memory (local + Dify RAG).
Teams using tc-review should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/tc-review/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How tc-review Compares
| Feature / Agent | tc-review | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Review QA test case documents (Markdown) against source code to find coverage gaps. Use when reviewing test cases, analyzing test coverage, suggesting missing scenarios, or learning codebase to extract testable scenarios. Supports project-based learning from multiple GitHub repos with dual memory (local + Dify RAG).
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
Related Guides
SKILL.md Source
# Test Case Review
Review test case documents against source code knowledge. Learn codebases, identify coverage gaps, suggest missing test cases.
## Quick Start
```bash
# 1. Create project and add repos
python scripts/setup_project.py myproject https://github.com/org/repo.git
# 2. Review test cases
python scripts/review.py tests/checkout.md --project myproject
```
## Decision Tree
```
User task → What do they need?
│
├─ Review test cases → Is project set up?
│ ├─ No → Run: python scripts/setup_project.py <name> <repo-url>
│ └─ Yes → Run: python scripts/review.py <testcase.md> --project <name>
│
├─ Learn new codebase → Run: python scripts/learn.py <project>
│
├─ Get suggestions → Run: python scripts/suggest.py <project>
│
└─ Search knowledge → Run: python scripts/search.py <project> "<query>"
```
## Available Scripts
**Always run with `--help` first** to see usage. Scripts handle complex workflows reliably.
| Script | Purpose |
|--------|---------|
| `scripts/setup_project.py` | Create project + add repo + learn (all-in-one) |
| `scripts/review.py` | Review test case file against knowledge |
| `scripts/learn.py` | Learn/re-learn source code from repos |
| `scripts/suggest.py` | Generate test case suggestions |
| `scripts/search.py` | Search project knowledge |
| `scripts/batch_review.py` | Review multiple files at once |
## Test Case Format
Test cases must be Markdown. See [references/testcase-format.md](references/testcase-format.md) for complete guide.
```markdown
# Test Suite: Payment Flow
## TC-001: Successful Payment
**Preconditions:**
- User logged in
- Cart has items
**Steps:**
1. Navigate to checkout
2. Enter valid card
3. Click Pay
**Expected Result:**
Payment success, order created
```
## Example Workflows
### Review Single File
```bash
python scripts/review.py tests/login.md --project auth-service
```
Output:
```
📊 Score: 65/100 Coverage: 65%
✅ Covered: Successful login, Invalid password
⚠️ Missing: Account locked, Rate limiting, Session timeout
💡 Suggested: TC-005 Account Lockout, TC-006 Rate Limit
```
### Batch Review
```bash
python scripts/batch_review.py "tests/**/*.md" --project myapp --min-score 70
```
### Quick Review (No Project Setup)
```bash
python scripts/setup_project.py temp-project https://github.com/org/repo.git
python scripts/review.py tests/feature.md --project temp-project
```
## Environment Variables
| Variable | Required | Description |
|----------|----------|-------------|
| `ANTHROPIC_API_KEY` | Yes* | For Claude LLM |
| `OPENAI_API_KEY` | Yes* | For OpenAI/Codex |
| `LLM_PROVIDER` | No | `anthropic` or `openai` (default: anthropic) |
| `LLM_MODEL` | No | Model name |
| `DIFY_API_KEY` | No | Enable Dify RAG |
| `DIFY_DATASET_ID` | No | Dify dataset ID |
*One of ANTHROPIC_API_KEY or OPENAI_API_KEY required.
## Reference Files
- [testcase-format.md](references/testcase-format.md) - Complete test case writing guide
- [scenarios.md](references/scenarios.md) - Test scenario types and examples
- [troubleshooting.md](references/troubleshooting.md) - Common issues and solutionsRelated Skills
tla-review
Comprehensive TLA+ specification review with checklist and automated validation
term-sheet-review
Review VC term sheets for pre-seed, seed, and Series A rounds. Analyzes each clause from investor or startup perspective, identifies investor-friendly vs. founder-friendly terms, flags deviations from market standards, and provides negotiation guidance. Use when reviewing any VC term sheet, evaluating investment terms, or preparing for funding negotiations. Triggers on term sheet, VC financing, Series A, seed round, liquidation preference, anti-dilution, protective provisions.
self-review
Local code review comparing branches/diffs without GitHub integration. Use when the user wants to review their code, self-review changes, check for bugs before committing, or mentions code review, diff review, or branch comparison.
review-pr
Review-only GitHub pull request analysis with the gh CLI. Use when asked to review a PR, provide structured feedback, or assess readiness to land. Do not merge, push, or make code changes you intend to keep.
review-implementation
Use after hyperpowers:executing-plans completes all tasks - verifies implementation against bd spec, all success criteria met, anti-patterns avoided
review-github-actions
Reviews GitHub Actions workflow files for best practices, ensuring workflows delegate commands to Makefile targets for maintainability and DRY principles. Enforces clean CI/CD patterns including proper caching, matrix strategies, and security practices.
priority-review
Evidence-based code review for diffs, PRs, and commits using P0-P3 severity. Finds actionable defects in changed code (security, correctness, reliability) and avoids style-only feedback.
paw-review-understanding
Analyzes PR changes to create ReviewContext.md and DerivedSpec.md artifacts. Handles both initial analysis and resumption after baseline research.
gitlab-mr-review-flow
标准化“需求描述 → 规范检索 (QMD) → 实现改动 → GitLab MCP 创建 MR → 代码评审报告”的流程技能。
github-pr-review-comments
Comprehensive workflow for managing GitHub PR review comments using gh CLI and GraphQL API. Use when asked to address review comments, find unreplied comments, reply to review threads, or resolve/unresolve review conversations. Supports finding ALL comments across pagination boundaries, replying to threads, and resolving conversations.
differential-review
Perform security-focused review of code diffs and pull requests, identifying newly introduced vulnerabilities, security regressions, and unsafe patterns in changed code.
codex-code-review
Perform comprehensive code reviews using OpenAI Codex CLI. This skill should be used when users request code reviews, want to analyze diffs/PRs, need security audits, performance analysis, or want automated code quality feedback. Supports reviewing staged changes, specific files, entire directories, or git diffs.