agent-fitness

# /agent-fitness Three modes: **`/agent-fitness extract`** — runs `python3 daemons/agent-fitness-extract.py` to scan the latest JSONL transcript and append new dispatch rows to `memory/AGENT_FITNESS.md`. Idempotent (dedup by session_id + tool_use_id). **`/agent-fitness`** (no args) — runs `python3 daemons/agent-fitness-report.py`. Surfaces: - Total dispatches (all-time, last 30d, last 7d) - Per-agent calibration table (Total / Clean / Blocked / Errored / Partial / Ratio) - Per-agent recent outcome trend (last 10 dispatches encoded as `c7 b1 e0 p0`) - ⚠ Repeat-blocker callouts (any agent with >=2 blocks in window) - Top 3 failing (agent, task) pairs **`/agent-fitness --days 7`** — same as above but windowed to last N days. Useful for "is this agent failing more lately?" ## Computation Calibration ratio per agent: `clean / (total − errored)`. The denominator excludes hard errors (infrastructure/tool failures) and only counts agent-level outcomes. Trend signal: any agent with >=2 blocks in the window surfaces a repeat-blocker callout. The classifier was hardened in v0.5.1 to catch real Lyra-style block patterns ("BLOCKED:", "are denied", "blocker-found" etc). ## When to run - After major sessions where multiple sub-agents fired (today S30d had ~7 dispatches) - Periodically (weekly?) to spot trends - Before relying on a sub-agent for something heavy ("am I about to dispatch an agent that's been failing?") ## What it does NOT do - Does not auto-reroute dispatches based ...