
human-review
by dhofheinz
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SKILL.md
name: human-review description: Present open questions to human for resolution using batched AskUserQuestion prompts, then update the specification with answers. context: fork allowed-tools: Read, Edit, AskUserQuestion user-invocable: false
Human Review - Interactive Question Resolution
You are facilitating human review of the specification's open questions.
Input: $ARGUMENTS contains path to spec document
Step 1: Read and Analyze Open Questions
Read the specification and extract all Open Questions.
Categorize each question by:
- Complexity: Simple (yes/no, pick one) vs Complex (requires explanation)
- Topic: Group related questions together
- Dependencies: Some questions may depend on answers to others
Step 2: Plan Question Batches
Create batches following these rules:
Batch Composition
- Maximum 4 questions per AskUserQuestion call
- Group by topic when possible
- Put dependent questions in later batches
- Complex questions may be asked alone for focus
Batch Types
Simple Batch: 3-4 related simple questions
Topic: Authentication
Q1: Should sessions expire after inactivity?
Q2: Support "remember me" functionality?
Q3: Require re-auth for sensitive operations?
Complex Batch: 1-2 complex questions
Topic: Data Architecture
Q1: How should user preferences be stored?
- Option A: In user table (simple, coupled)
- Option B: Separate preferences table (flexible, more complex)
- Option C: JSON column (schemaless, harder to query)
Mixed Batch: 1 complex + 2 simple
Topic: Error Handling
Q1 (complex): What should happen when external API fails?
Q2 (simple): Show technical error details to users?
Q3 (simple): Log all errors to monitoring service?
Step 3: Execute Question Batches
For each batch, use AskUserQuestion:
AskUserQuestion:
questions:
- question: "{full question text}"
header: "{Topic}" # max 12 chars
options:
- label: "{Option A}"
description: "{What this means}"
- label: "{Option B}"
description: "{What this means}"
multiSelect: {true if multiple valid}
Option Design Guidelines
- 2-4 options per question
- First option can be recommended: "Option A (Recommended)"
- Include trade-offs in descriptions
- User can always select "Other" for custom input
Step 4: Process Answers
For each answered question:
Clear Answer → High Confidence
If user gave definitive answer:
- Add to High Confidence section
- Format:
- {statement based on answer} (per human review) - Remove from Open Questions
Partial Answer → Medium Confidence
If user gave tentative or conditional answer:
- Add to Medium Confidence section
- Note the condition or uncertainty
- May keep related follow-up in Open Questions
Deferred Answer
If user says "decide later" or "not sure yet":
- Keep in Open Questions
- Add note:
[DEFERRED: {reason}]
New Questions Raised
If answer raises new questions:
- Add to Open Questions
- Tag as:
[FROM REVIEW]
Step 5: Update Specification
After all batches complete:
- Edit spec to reflect all answers
- Update Open Questions section (remove answered, add new)
- Update convergence metrics
- Add to Iteration Log:
### Human Review ({date})
- **Questions Presented**: {n}
- **Resolved**: {n}
- **Deferred**: {n}
- **New Questions**: {n} raised during review
- **Remaining Open**: {n}
Step 6: Check Completion
After human review:
- If Open Questions ≤ 0: Phase complete
- If Open Questions > 0 but all marked [DEFERRED]: Phase complete
- If new questions added: May need another review pass
Output Summary
HUMAN REVIEW COMPLETE
Spec: {path}
Phase: {what-human|how-human}
## Questions Processed
- Total presented: {n}
- Resolved to High Confidence: {n}
- Resolved to Medium Confidence: {n}
- Deferred: {n}
- New questions raised: {n}
## Remaining Open Questions
{list any remaining}
## Recommendation
{COMPLETE: ready for next phase | CONTINUE: more review needed}
Interaction Guidelines
- Be respectful of human's time - batch efficiently
- Provide enough context for informed decisions
- Don't ask questions that were already answered
- If human seems frustrated, offer to defer remaining questions
- Acknowledge when questions are difficult or uncertain
Score
Total Score
Based on repository quality metrics
SKILL.mdファイルが含まれている
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GitHub Stars 100以上
3ヶ月以内に更新がある
10回以上フォークされている
オープンIssueが50未満
プログラミング言語が設定されている
1つ以上のタグが設定されている
Reviews
Reviews coming soon