| name | skill-forge |
| description | Discover repeated workflow patterns from session history and generate new skill scaffolds. Also monitors skill health — success rates, retries, corrections, and trends. Use when looking for automation opportunities, reviewing skill effectiveness, or generating new skills from observed patterns. |
Skill Forge & Dojo
Two systems for skill self-improvement, mostly automated:
- Forge — mines session history for repeated multi-step workflows and proposes new skills
- Dojo — tracks skill health (success rate, retries, user corrections) and flags weak skills
Automation (runs without you asking)
- On session start: surfaces critical skill health issues as notifications, shows pending proposals in status bar
- Before each prompt: if your prompt involves a skill with known issues, injects health warnings into the system prompt so the agent is aware
- On session shutdown: runs incremental analysis on new sessions (batched — waits for 5+ new sessions), updates health metrics, auto-generates proposals for high-frequency patterns
- On tool_call: tracks which skills are loaded (SKILL.md reads) for health tracking
You only need to intervene to:
- Review and accept/reject proposals (
skill_forge_proposals, skill_forge_accept)
- Investigate flagged skills (
skill_dojo_report)
- Force a full re-analysis (
skill_forge_analyze incremental=false)
Manual Tools
# Force-run workflow mining (normally runs on shutdown)
→ skill_forge_analyze
# Review proposed skills
→ skill_forge_proposals
# Accept a proposal and generate the skill scaffold
→ skill_forge_accept index=0
# Check skill health dashboard
→ skill_dojo_health
# Deep dive on a specific skill
→ skill_dojo_report skill="cr-workflow"
Improving a Weak Skill
When the dojo flags a skill:
- Check
skill_dojo_report for the specific issues
- Review recent sessions where the skill failed (use session_search)
- Check
memory_lessons for corrections related to that skill
- Update the SKILL.md with clearer instructions, better examples, or edge case handling
- Health metrics update automatically as new sessions accumulate
How It Works
Pattern Mining
- Reads parsed session data from the session-search index
- Classifies tool calls into high-level actions (build, edit_java, git_commit, cr_upload, etc.)
- Deduplicates consecutive identical actions
- Extracts n-gram subsequences (3-7 steps), filters generic exploration patterns
- Counts occurrences across sessions, requires 8+ (configurable)
Health Tracking
- Detects skill invocations by SKILL.md reads in session history
- Tracks the "segment" from skill load to next skill load or topic change
- Measures: success (no retries/corrections), retry count, user corrections, duration
- Computes trends by comparing recent vs older invocations
- Flags issues: high_failure_rate, excessive_retries, frequent_corrections, slow, unused
State
- Persisted to
~/.pi/skill-evolution/state.json
- Incremental by default — only processes new sessions
/skills command for quick status check
