a5c-ai/retrospect

retrospect

Load and use the installed babysit skill.

Resolve the request in retrospect mode:

• treat everything after $retrospect as the run selector(s) to summarize
• focus on the run history, outcomes, lessons, and gaps
• do not create a separate command surface here; this skill only forwards into babysit

Phase 1: Resolve Target Run(s)

• If --all or "all" is present in args: list all runs via ls -lt .a5c/runs/ and collect all completed/failed run IDs
• If multiple run IDs are provided: use all of them
• Otherwise: existing behavior (resolve the latest single run)
• Use interactive prompts to confirm run selection when possible

Phase 2: Load Run Data

For each selected run, load:

• run.json metadata
• Journal events
• Task definitions and results
• State snapshots

Phase 3: Analysis

Perform standard per-run analysis (outcomes, process effectiveness, suggestions).

Cross-Run Pattern Analysis (multi-run mode)

When analyzing multiple runs, additionally cover:

• Common failure modes across runs
• Velocity trends (tasks/time across runs)
• Process evolution (how processes changed over time)
• Repeated breakpoint patterns

Phase 4: Suggestions

Provide actionable suggestions for process improvements, optimizations, and fixes.

Phase 5: Implementation

If the user agrees, implement improvements to processes, skills, or configuration.

Phase 6: Cleanup Suggestion

After analysis, suggest: "Consider running babysitter cleanup (or /babysitter:cleanup) to clean up old run data and reclaim disk space."