Individual skill
omh-agent-evaluation
[omh] Choosing between coding agents on evidence: compare executor or agent choices on reproducible tasks using quality, cost, time, tool, and evidence metrics. Use when the user says: agent-evaluation, agent evaluation, agent eval, agent benchmark, executor evaluation, executor benchmark, compare agents, compare codex claude.
skills/omh-agent-evaluation/SKILL.md
Source commit 98284f305f6a9dce6e36a1580c9f333105f1a950
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- Matches the observed repository HEAD
- Yes
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- Yes
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- Skill scan complete
- No
- Observed behavior
- Network or broad shell
- Last checked
- Nov 9, 2026
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Repository context
- Repository stars (not skill ratings)
- 5,473
- Repository last updated
- Nov 11, 2026
- License
- MIT
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- agent Repository topic
- ai Repository topic
- ai-agent Repository topic
- ai-tools Repository topic
- claude-code Repository topic
- codex Repository topic
- hermes-agent Repository topic
- hermes-skill Repository topic
- skills Repository topic
- engineering Derived category
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SKILL.mdcore · 9,515 bytesSKILL.mdcore · 9,515 bytesreferences/self-evaluation-loops.mddoc · 6,372 bytes
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# Agent Evaluation
This is a Hermes-native `agent-evaluation` workflow skill.
## Why This Exists
`agent-evaluation` gives OMH a way to improve executor choice empirically, not by vibes, while preserving executor-neutral product language across Codex, Claude Code, Hermes, and generic runtimes.
## Do Not Use When
- The user needs current runtime readiness only; use `omh-executor-runtime-readiness`.
- The user already selected an executor and wants implementation; use the coding handoff or delivery workflow.
- The user asks for workflow learning from a single failed route; use `omh-workflow-learning`.
- The ask is to find and fix runtime, memory, cost, or rendering hotspots rather than score executor or model output quality; use `ulw-perf`.