pi-skill-evolution

Meta-skill and self-improvement loop for pi. Mines your session history for repeated workflows, proposes new skills, and tracks skill health — automatically.

What it does

Two systems:

Skill Forge — discovers repeated multi-step workflow patterns across your session history and proposes them as new skills. Patterns like "build → fix → build" or "commit → upload CR → update notes" that you do over and over get surfaced as candidates for formalization.

Skill Dojo — tracks how well each skill performs by analyzing session data: success rate, retry count, user correction rate, and trends over time. Flags skills that are degrading or have high failure rates so you can fix them.

Install

pi install git:github.com/samfoy/pi-skill-evolution
# or
pi install npm:pi-skill-evolution

Automation

Most of the work happens automatically:

• On session start: surfaces critical skill health issues and pending proposals
• Before each prompt: injects health warnings into the system prompt when you're about to use a skill with known issues
• On session shutdown: runs incremental pattern mining and health analysis (batched every 5+ new sessions)

You only intervene to:

• Review and accept/reject proposals
• Investigate flagged skills
• Force a full re-analysis

Tools

| Tool | Description | |------|-------------| | skill_forge_analyze | Mine session history for workflow patterns | | skill_forge_proposals | List pending skill proposals | | skill_forge_accept | Accept a proposal and generate a SKILL.md scaffold | | skill_dojo_health | Skill health dashboard — success rates, retries, trends | | skill_dojo_report | Detailed report for a specific skill |

Commands

| Command | Description | |---------|-------------| | /skills | Quick status summary |

Example

> skill_dojo_health

Skill Health Dashboard (20 skills tracked)

Issues: 0 critical, 1 warnings

Skill                    | Uses | Success | Retries | Corrections | Trend
-------------------------|------|---------|---------|-------------|------
ticketing                |   45 |    71%  |     1.0 |          2% | →  🟡
internal-reader          |  117 |    91%  |     0.4 |          2% | →
cr-workflow              |   73 |    96%  |     0.2 |          4% | →
code-review              |   49 |    96%  |     0.1 |          0% | →
...

Requirements

• pi with the session-search extension (for session index data)
• Sessions accumulate over time — the more history, the better the pattern detection

How it works

Pattern Mining

• Reads parsed session data from the session-search index (~/.pi/session-search/index/)
• Classifies tool calls into high-level actions (build, git_commit, cr_upload, mcp_call, etc.)
• Deduplicates consecutive identical actions
• Extracts n-gram subsequences (3-7 steps), filters generic exploration patterns
• Counts occurrences across sessions, requires 8+ by default

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: high_failure_rate, excessive_retries, frequent_corrections, slow, unused

State

• Persisted to ~/.pi/skill-evolution/state.json
• Incremental by default — only processes new sessions since last analysis

License

MIT