dvcrn claude-review — Self-Review Quality Gate
Uses Claude CLI (claude --print) as an independent reviewer to catch errors, missed requirements, and quality issues in your work before delivering to the user.
How It Works
- You complete your task and save output to file(s)
review-worksends your work to a separate Claude instance for independent review- If a skill was used, the reviewer checks against the skill's specific requirements
- If LESSONS.md exists, the reviewer checks for repeat mistakes
- Issues are returned with severity ratings (critical / major / minor) and a PASS/FAIL verdict
- You fix issues and re-review until clean
The reviewer is a separate Claude instance — it has no context of your conversation, so it reviews purely on merit.
Auto-learning: When a review fails, critical and major issues are automatically logged to LESSONS.md. This file is auto-included in future reviews so the reviewer checks for repeat mistakes.
Prerequisites
claudeCLI must be installed and available in PATH (npm install -g @anthropic-ai/claude-code)- Valid API key configured for Claude CLI
Command
review-work "<task_summary>" --context <file_or_folder> [--skill <file_or_folder>]
| Argument | Required | Description |
|---|---|---|
task_summary |
Yes | What the work was supposed to accomplish |
--context <path> |
Yes | File or folder containing the work to review. Can also include reference material, test output, or anything relevant. |
--skill <path> |
No | SKILL.md or skill folder used for this task. The reviewer uses its requirements as a definition of done. |
Auto-included (no flag needed):
LESSONS.md— if it exists, always included so the reviewer checks for repeat mistakes
All paths accept both files and folders. Claude reads all file types natively (text, images, PDFs, code).
Workflow
When instructed to review your work:
- Identify every file you created or modified
- Run
review-workwith the task summary,--contextpointing to your output, and--skillif a skill was used - Read the review output — look for VERDICT: PASS or FAIL
- Fix any critical or major issues
- Re-run
review-workafter fixing (up to 3 cycles) - Report the review summary in your final output
Examples
Review a single file:
review-work "Write a Python email validator" --context /tmp/email.py
Review with skill context (reviewer verifies against skill requirements):
review-work "Write an SEO blog about class action lawsuits" --context /tmp/blog.md --skill ~/.openclaw/workspace/skills/seo-content-writer/SKILL.md
Review an entire project folder:
review-work "Build a todo app with React" --context /tmp/todo-app/ --skill ~/skills/fullstack/SKILL.md
Review with extra context (reference articles, test output, etc.):
# Put your output + reference material in one folder
review-work "Write a blog matching MoneyPilot tone" --context /tmp/blog-project/
Rules
- Review every file you created or modified — not just the main one
- If a skill was used for the task, always pass
--skill - If the review reports critical or major issues → fix them → re-review (up to 3 cycles)
- Only finish after the verdict is PASS (zero critical/major issues)
- Include the review summary in your final output
- After 3 failed cycles, finish but attach the full review report
What NOT to Do
- Do NOT ask the user for arguments — you already know what you created and which skill you used
- Do NOT say "review passed" without actually running the command
- Do NOT fabricate review results — the command produces real output
- Do NOT forget
--skillwhen a skill was involved in the task
LESSONS.md
Failed reviews are auto-logged to LESSONS.md (default: ~/.openclaw/workspace/LESSONS.md). Override the path with the LESSONS_FILE environment variable.
This file is also auto-read on every review, so the reviewer checks: "are any past mistakes being repeated?"