plan2code/.agents-docs/AGENTS-smarsh2code-metrics.md

Plan2Code Metrics

Part of AGENTS.md — project guidance for AI coding agents.

A recursive self-improvement toolchain for plan2code contributors. Collects run metrics, aggregates by prompt generation, diagnoses weak steps via AI, and proposes surgical prompt edits.

Metrics Data Flow

Collect → Aggregate → Analyze → Improve → Apply

1. Collector reads project artifacts (specs/<feature>/) → writes RunMetrics JSON per run
2. Aggregator groups runs by prompt SHA fingerprint (cohorts) → aggregated.json
3. Analyzer invokes AI with aggregated metrics + prompt contents → diagnosis markdown
4. Improver invokes AI with diagnosis → validated PromptEdit[] proposals (char limit + verbatim checks)
5. Applier shows interactive diffs → patches src/plan2code-*.md files

Metrics Commands

cd plan2code-metrics && npm run build  # Build the CLI
plan2code-metrics                      # Run (fully interactive, no flags)

Metrics CLI Menu

Option	Action
Collect	Read spec artifacts → run JSON
Import	Copy run JSON from another project
View	Display cohort metrics with health indicators
Analyze	AI diagnosis of weak metrics
Propose	AI improvement proposals with validation
Apply	Interactive diff review + file patching

Key Source Files

File	Purpose
types.ts	All interfaces (RunMetrics, UserFeedback, CohortMetrics, etc.) + METRIC_TARGETS
collector.ts	Reads project artifacts → run JSON (parses plan drafts, overview.md, loop logs)
aggregator.ts	Merges runs by prompt generation (SHA cohort) → aggregated.json
analyzer.ts	AI diagnosis via prompts/analyze.md template
improver.ts	AI proposals via prompts/improve.md + validation (char count, old_text match)
applier.ts	Interactive diff review + file patching
cli.ts	Menu-driven interactive CLI (100% prompts, no flags)
invoke-llm.ts	Unified LLM interface (Claude Code or Copilot CLI)

User Feedback

The collector parses an optional ## User Feedback table from overview.md:

## User Feedback
| Field | Value |
|-------|-------|
| Rating | 8 |
| Reason | Smooth workflow |
| Went Well | Planning was thorough |
| Went Poorly | Some tasks unclear |

Feedback is collected during finalize (Step 5) or retroactively via the CLI. Pipe characters in values are escaped as \|. The aggregator computes avg_user_rating and feedback_count per cohort.

Supported AI Agents

• Claude Code (recommended): claude CLI with --inputFile for prompt delivery
• GitHub Copilot CLI: copilot CLI with stdin prompt delivery

Metric Targets

Metric	Target	Direction
avg_confidence	≥ 90	higher is better
avg_task_completion_rate	≥ 0.95	higher is better
avg_blocker_count	≤ 1.5	lower is better
avg_completion_marker_success_rate	≥ 0.95	higher is better
avg_verification_failures_found	≤ 1.0	lower is better
archival_success_rate	≥ 0.99	higher is better
avg_user_rating	≥ 7.0	higher is better

Data stored in .plan2code-metrics/ (runs/, aggregated.json, proposals/).