feat: add user feedback collection to plan2code-metrics

- Add UserFeedback type (rating 1-10, reason, went well, went poorly)
- Collector parses ## User Feedback table from overview.md
- Aggregator computes avg_user_rating and feedback_count per cohort
- CLI offers interactive feedback collection with pipe-safe escaping
- Finalize prompt collects optional feedback as Step 5 (before archival)
- Analysis/improvement prompts reference avg_user_rating metric target

plan2code-metrics/src/prompts/analyze.md

@@ -38,6 +38,7 @@ The following are the current contents of the plan2code workflow prompt files be
 | avg_completion_marker_success_rate (Step 3) | ≥ 0.95 | higher is better |
 | avg_verification_failures_found (Step 4) | ≤ 1.0 | lower is better |
 | archival_success_rate (Step 4) | ≥ 0.99 | higher is better |
+| avg_user_rating (Feedback) | ≥ 7.0 | higher is better (1-10 scale, null if no feedback) |
 
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plan2code-metrics/src/prompts/improve.md

@@ -45,6 +45,7 @@ The following diagnosis was produced by the analysis step:
 - For high `avg_blocker_count`: Add blocker-recovery guidance or prerequisite check instructions.
 - For low `avg_confidence`: Strengthen the confidence calculation instructions with clearer rubrics.
 - For low `avg_parallel_groups`: Add explicit guidance for identifying parallel tasks in Step 2.
+- For low `avg_user_rating`: Review user feedback themes (what_went_well, what_went_poorly) for systemic issues.
 - Keep each `new_text` as short as possible while still addressing the root cause.
 
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