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Final quality review producing a human-readable report
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Basierend auf der SOC-Berufsklassifikation
| name | final-review |
| description | Final quality review producing a human-readable report |
Produce a final human-readable review report after all fixes have been applied. This report is for a human reviewer — concise, clear, and focused on whether the story is ready for merge.
$ARGUMENTS is the path to a story file.
Derive story-slug from the story filename. Read:
$ARGUMENTS — for acceptance criteriadocs/reviews/{story-slug}/02-review.md — previous review findingsdocs/reviews/{story-slug}/03-fix.md — what was fixed (if it exists)Discover the project's test commands by exploring project configuration files. Look at existing source files to understand the code style and conventions.
For each Critical and Warning finding from the previous review, check the specific file and line referenced. Confirm the fix was applied correctly.
Run git diff --name-only HEAD and git status --short to find all changed files. Read every changed file.
Do a holistic review focused on:
Run the project's test command. Confirm all tests pass.
Write to docs/reviews/{story-slug}/04-final-review.md:
# Final Review
**Story:** {story file path}
**Date:** {ISO date}
**Verdict:** {READY FOR MERGE | NEEDS ATTENTION}
## What Was Built
{2-3 sentence summary suitable for a PR description}
## Acceptance Criteria
| Criterion | Status | Test |
| ---------------- | --------- | ---------------- |
| {criterion text} | PASS/FAIL | {test reference} |
## Previous Review
- **Critical findings:** {N} found, {N} resolved
- **Warnings:** {N} found, {N} resolved
## Remaining Concerns
{List any issues that still need attention, or "None"}
## Files Changed
- `path/to/file` — {one-line description}
## Test Results
{Summary: N tests passed, 0 failed}
After writing the report, output:
Review an Equal Experts Gen AI Engineer take-home test submission (typically a RAG pipeline exercise) against the EE assessment rubric. Use when reviewing a candidate's Gen AI / LLM / RAG take-home test, assessing a retrieval-augmented-generation or prompt-engineering exercise, or scoring a THT for a Gen AI Engineer role.
Review an Equal Experts Backend Software Engineer take-home test submission against the EE assessment rubric. Use when reviewing a candidate's backend / general software engineering take-home test (non Gen AI), assessing a coding exercise submission, or scoring a THT for a backend role.
Generate a single-line commit message from conversation context and git diff, then optionally commit
Shared code and test patterns for the story pipeline skills
Write tests and implementation together for a user story
Read a review report and apply fixes to the codebase