ai-training/.cursor/skills/retrospective/SKILL.md

name, description, category, tags, disable-model-invocation

name	description	category	tags	disable-model-invocation
retrospective	Collect metrics from implementation batch reports and code review findings, analyze trends across cycles, and produce improvement reports with actionable recommendations. 3-step workflow: collect metrics, analyze trends, produce report. Outputs to _docs/06_metrics/. Trigger phrases: - "retrospective", "retro", "run retro" - "metrics review", "feedback loop" - "implementation metrics", "analyze trends"	evolve	retrospective metrics trends improvement feedback-loop	true

Retrospective

Collect metrics from implementation artifacts, analyze trends across development cycles, and produce actionable improvement reports.

Core Principles

• Data-driven: conclusions come from metrics, not impressions
• Actionable: every finding must have a concrete improvement suggestion
• Cumulative: each retrospective compares against previous ones to track progress
• Save immediately: write artifacts to disk after each step
• Non-judgmental: focus on process improvement, not blame

Context Resolution

Fixed paths:

• IMPL_DIR: _docs/03_implementation/
• METRICS_DIR: _docs/06_metrics/
• TASKS_DIR: _docs/02_tasks/ (scan all subfolders: todo/, backlog/, done/)

Announce the resolved paths to the user before proceeding.

Prerequisite Checks (BLOCKING)

1. IMPL_DIR exists and contains at least one batch_*_report.md — STOP if missing (nothing to analyze)
2. Create METRICS_DIR if it does not exist
3. Check for previous retrospective reports in METRICS_DIR to enable trend comparison

Artifact Management

Directory Structure

METRICS_DIR/
├── retro_[YYYY-MM-DD].md
├── retro_[YYYY-MM-DD].md
└── ...

Progress Tracking

At the start of execution, create a TodoWrite with all steps (1 through 3). Update status as each step completes.

Workflow

Step 1: Collect Metrics

Role: Data analyst Goal: Parse all implementation artifacts and extract quantitative metrics Constraints: Collection only — no interpretation yet

Sources

Source	Metrics Extracted
batch_*_report.md	Tasks per batch, batch count, task statuses (Done/Blocked/Partial)
Code review sections in batch reports	PASS/FAIL/PASS_WITH_WARNINGS ratios, finding counts by severity and category
Task spec files in TASKS_DIR	Complexity points per task, dependency count
FINAL_implementation_report.md	Total tasks, total batches, overall duration
Git log (if available)	Commits per batch, files changed per batch

Metrics to Compute

Implementation Metrics:

• Total tasks implemented
• Total batches executed
• Average tasks per batch
• Average complexity points per batch
• Total complexity points delivered

Quality Metrics:

• Code review pass rate (PASS / total reviews)
• Code review findings by severity: Critical, High, Medium, Low counts
• Code review findings by category: Bug, Spec-Gap, Security, Performance, Maintainability, Style, Scope
• FAIL count (batches that required user intervention)

Efficiency Metrics:

• Blocked task count and reasons
• Tasks completed on first attempt vs requiring fixes
• Batch with most findings (identify problem areas)

Self-verification:

• All batch reports parsed
• All metric categories computed
• No batch reports missed

---

Step 2: Analyze Trends

Role: Process improvement analyst Goal: Identify patterns, recurring issues, and improvement opportunities Constraints: Analysis must be grounded in the metrics from Step 1

1. If previous retrospective reports exist in METRICS_DIR, load the most recent one for comparison
2. Identify patterns:
   • Recurring findings: which code review categories appear most frequently?
   • Problem components: which components/files generate the most findings?
   • Complexity accuracy: do high-complexity tasks actually produce more issues?
   • Blocker patterns: what types of blockers occur and can they be prevented?
3. Compare against previous retrospective (if exists):
   • Which metrics improved?
   • Which metrics degraded?
   • Were previous improvement actions effective?
4. Identify top 3 improvement actions ranked by impact

Self-verification:

• Patterns are grounded in specific metrics
• Comparison with previous retro included (if exists)
• Top 3 actions are concrete and actionable

---

Step 3: Produce Report

Role: Technical writer Goal: Write a structured retrospective report with metrics, trends, and recommendations Constraints: Concise, data-driven, actionable

Write METRICS_DIR/retro_[YYYY-MM-DD].md using templates/retrospective-report.md as structure.

Self-verification:

• All metrics from Step 1 included
• Trend analysis from Step 2 included
• Top 3 improvement actions clearly stated
• Suggested rule/skill updates are specific

Save action: Write retro_[YYYY-MM-DD].md

Present the report summary to the user.

---

Escalation Rules

Situation	Action
No batch reports exist	STOP — nothing to analyze
Batch reports have inconsistent format	WARN user, extract what is available
No previous retrospective for comparison	PROCEED — report baseline metrics only
Metrics suggest systemic issue (>50% FAIL rate)	WARN user — suggest immediate process review

Methodology Quick Reference

┌────────────────────────────────────────────────────────────────┐
│              Retrospective (3-Step Method)                     │
├────────────────────────────────────────────────────────────────┤
│ PREREQ: batch reports exist in _docs/03_implementation/        │
│                                                                │
│ 1. Collect Metrics  → parse batch reports, compute metrics     │
│ 2. Analyze Trends   → patterns, comparison, improvement areas  │
│ 3. Produce Report   → _docs/06_metrics/retro_[date].md         │
├────────────────────────────────────────────────────────────────┤
│ Principles: Data-driven · Actionable · Cumulative              │
│             Non-judgmental · Save immediately                  │
└────────────────────────────────────────────────────────────────┘