/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 on calibration. Surfaces data; principal decides.
• Does not LLM-classify failures — heuristic only (keyword matching on result content).
• Does not pool across sessions for cluster analysis. Future v0.5.x.
• Does not include task-type clustering. All tasks pooled per agent.

Cross-links

• [[concepts/Somatic v0.5.0 Agent Fitness]] — spec
• [[memory/AGENT_FITNESS]] — the data
• [[concepts/Somatic Roadmap]]
• [[memory/TRUST_DECAY]] — sibling claim → outcome ledger