/cmetrics — Workflow Metrics Dashboard

Aggregate all workflow data into a project health and ROI dashboard showing bugs caught, token cost, and trends.

When to Use

  • Monthly, to track how the workflow is performing over time.
  • Before presenting to stakeholders, to justify the workflow investment with data.
  • When deciding whether to increase workflow intensity.
  • When something feels off — metrics reveal which phases are pulling weight and which are not.
  • Not for: Single-feature summaries (use /csummary), or checking current workflow state (use /cstatus).

How It Fits in the Workflow

This skill is standalone and project-wide. It reads accumulated data from every feature that has gone through the Correctless workflow — QA findings, verification reports, antipatterns, drift debt, audit history, token logs, and git history. It produces a comprehensive dashboard with actionable health analysis. Run it periodically to spot trends.

What It Does

  • Counts features completed, total issues caught (deduplicated across phases), and bug escape rate.
  • Breaks down issues by phase (Review, Test Audit, QA, Verify, Audit) to show where value concentrates.
  • Tracks antipattern growth by category, flagging systemic issues that may need architectural fixes.
  • Monitors drift debt health: open items, staleness, accumulation vs. resolution rate.
  • Token ROI Analysis: Reads .correctless/artifacts/token-log-*.json to compute cost per bug caught, tokens per feature by phase, tokens per LOC, and estimated production fix cost avoided. Shows whether token spend is efficient (e.g., “65% of tokens go to TDD, which catches 60% of bugs”).
  • Session Analytics: Reads Claude Code session-meta and facets data from ~/.claude/usage-data/, filtered to the current project. Reports exact token cost (ground truth), tool distribution, friction rate, user engagement, and outcome rate.
  • Correctless vs Freeform comparison: Identifies which sessions used Correctless (by checking artifact timestamps and tool patterns) vs. freeform coding, then compares outcome rate, friction, duration, and token usage between the two groups.
  • Cross-metric correlation analysis: flags patterns like “specs revised mid-TDD frequently AND antipattern growth accelerating in the same category.”

Example 

User: /cmetrics

# Correctless Metrics — my-project
# Generated: 2026-03-29

## Overview
- Features completed: 8
- Total issues caught: 47
- Bug escape rate: 2 escaped / 47 caught (4.1%)
- Workflow active since: 2026-01-15

## Issues by Phase
| Phase        | Issues | % of Total | Notes                              |
|--------------|--------|------------|------------------------------------|
| Review       | 12     | 25%        | Security checklist added 40% of these |
| QA           | 22     | 47%        | 5 class fixes added structural tests  |
| Verify       | 8      | 17%        |                                    |
| Audit        | 5      | 11%        |                                    |

## Token ROI Analysis
- Total tokens tracked: 2.4M across 8 features
- Cost per bug caught: ~51k tokens (47 bugs)
- Estimated production fix cost avoided: 47 bugs x 2-10 hrs = 94-470 hrs

## Session Analytics
- Correctless sessions: 62% fully achieved outcome
- Freeform sessions: 41% fully achieved outcome

## Health Analysis
- QA rounds trending down (3.1 -> 2.3 avg) — workflow getting more effective.
- Error handling antipatterns growing fastest (+4 in 3 months). Consider architectural pattern.
- Spec revision rate: 25% of features — acceptable.

What It Reads / Writes

Reads Writes
.correctless/artifacts/qa-findings-*.json .correctless/artifacts/metrics-{date}.md
docs/verification/*-verification.md  
.correctless/meta/workflow-effectiveness.json  
.correctless/antipatterns.md  
.correctless/meta/drift-debt.json  
.correctless/artifacts/findings/audit-*-history.md  
.correctless/specs/*.md  
.correctless/artifacts/summary-*.md  
.correctless/artifacts/token-log-*.json  
.correctless/artifacts/audit-trail-*.jsonl  
~/.claude/usage-data/session-meta/*.json  
~/.claude/usage-data/facets/*.json  
docs/decisions/*.md  
Git log  

Intensity Levels

  • Standard intensity: Omits Olympics convergence analysis and Olympics history table (those features require high+ intensity).
  • High/critical intensity: Adds Olympics history, convergence speed analysis, and audit-specific metrics.

Common Issues

  • Sparse data on new projects: This is expected. Data accumulates with each feature. The dashboard reports what exists and notes what will appear after more features run.
  • Session-meta not found: Claude Code session data may not exist for the project yet. Session analytics will appear after a few sessions.
  • Token logs vs. session-meta divergence: If the numbers differ significantly, the dashboard notes it: the gap is orchestrator overhead not captured by subagent tracking.