agent-fitness

Track and surface sub-agent dispatch calibration ratios.

IndexedMIT★ 23

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Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.

npx agentmarket install skill/wrg32786-agent-fitness

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About

Track and surface sub-agent dispatch calibration ratios. Extract from JSONL transcript or read existing ledger. Per-agent clean/blocked/errored/partial trends.

Use cases: agent-building

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Permissions

What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.

Tools: No tools found
Secrets: None asked for
Install scripts: None found

Network hosts it talks to 1

codeload.github.com · found in install command

Files it touches

~/.claude/skills/agent-fitness

Tools it pre-approves 2

From allowed-tools in SKILL.md: the agent may use these without asking you each time.

Bash,
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What's inside

Skill name: agent-fitness
Description: Track and surface sub-agent dispatch calibration ratios. Extract from JSONL transcript or read existing ledger. Per-agent clean/blocked/errored/partial trends.
License: MIT
Allowed tools: Bash,, Read
Instructions: 42 lines in SKILL.md · view on GitHub

Before installing

Pre-approves some shell commands

Files (1)

SKILL.md

SKILL.md (first lines)

# /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

From wrg32786/aigent-os, MIT.

Signals

Stars: 23
Forks: 7
Open issues: 14
Last commit (this folder): Jul 7, 2026 (4 months ago)
Last release: Jul 25, 2026 (3 months ago)

From GitHub (fetched Oct 24, 2026).

Details

License: MIT
Repository: github.com/wrg32786/aigent-os
Website: tools.theaigent.xyz/os
First seen: Oct 24, 2026
Last checked: Oct 24, 2026
Source: GitHub github-graphql

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agent-fitness skill. Agent Mart. https://agentmart.dev/skills/wrg32786-agent-fitness (last updated 2026-10-24)