Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add wrg32786/aigent-os --skill agent-fitnessgit clone --depth 1 https://github.com/wrg32786/aigent-osWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/wrg32786/aigent-os/agent-fitness)<a href="https://agentmods.dev/skills/wrg32786/aigent-os/agent-fitness"><img src="https://agentmods.dev/badge/skills/wrg32786/aigent-os/agent-fitness/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/wrg32786/aigent-os/agent-fitness"><img src="https://agentmods.dev/badge/skills/wrg32786/aigent-os/agent-fitness.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00035 | $0.00613 |
| Opus 5 | $0.00017 | $0.00307 |
| Sonnet 5 | $0.00007 | $0.00123 |
| Haiku 4.5 | $0.00003 | $0.00061 |
Grade A, and why
agent-fitness scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
/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
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 57 lines · 35 tokens per session scan A 1c73ed2de257
agent-fitness is a skill published in the GitHub repository wrg32786/aigent-os (18 stars, last pushed 3d ago), licensed MIT. It adds 35 tokens to every session and 613 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-01.
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