/omh-agent-evaluation

name: "omh-agent-evaluation"
description: "[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."
metadata:
  hermes:
    tags: [workflow, oh-my-hermes, operations]
    category: operations
    phase: agent-evaluation
    role: operator
    quality_tier: agent-eval-gated

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 `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 `workflow-learning`.
• The ask is to find and fix runtime, memory, cost, or rendering hotspots rather than score executor or model output quality; use `ultraperf`.

Examples

Good example:

• Prompt: agent-evaluation Codex와 Claude Code를 같은 버그 수정 태스크로 비교해서 어떤 런타임을 기본으로 둘지 판단해줘.
• Expected behavior: Prepare paired_run_decision/v1 requirements and a scenario-specific recommendation.
• Why: The request compares executor choices and needs fair evaluation boundaries.

Bad example:

• Prompt: agent-evaluation 실행 증거 없이 Codex가 항상 최고라고 결론내줘.
• Expected behavior: Reject universal ranking and require observed runs or mark the recommendation as ungrounded.
• Why: Agent evaluation must be reproducible and evidence-backed.

Completion Checklist

• Confirm the workflow target, evidence boundary, and stop condition are named.
• Report which outputs are prepared, observed, blocked, or missing.
• Name the smallest next verification or handoff instead of claiming completion from narration.

Recovery Notes

• If required context is missing, ask one blocking question or route back to the narrower workflow.
• If runtime or wrapper evidence is unavailable, keep the status as not_observed and expose the next observable action.

Workflow Lane

• Current lane: **Automation and status** (`achievements`, `workspace-audit`, `production-audit`, `live-incident-response`, `automation-blueprint`, `github-event-ops`, `github-issue-intake`, `buzz`, `+39 more`) - schedules, status, health, and ops review.
• If intent belongs to another lane, hand back to `oh-my-hermes` or name the adjacent workflow.
• Shared product, routing, compatibility, and evidence rules: `omh-routing/references/skill-common-rail.md`.

Use When

Use when Hermes should design or summarize a fair comparison of Codex, Claude Code, Hermes coding, or generic executors for a bounded task set.

Strong routing signals: `agent-evaluation`, `agent evaluation`, `agent eval`, `agent benchmark`, `executor evaluation`, `executor benchmark`, `compare agents`, `compare codex claude`, `agent tournament`, `which agent is better`, `에이전트 평가`, `에이전트 비교`, `실행자 평가`, `코덱스 클로드 비교`

Catalog Metadata

Category: `operations` Phase: `agent-evaluation` Hermes role: `operator` Quality tier: `agent-eval-gated` Reasoning demand: `light`

Quality bar:

• Define tasks, rubric, isolation, budgets, and stop rules before comparing agents.
• Use the same inputs and success criteria across candidates unless the difference is the variable under test.
• Require receipt-authenticated observed_at provenance before public parse or validation can return pass or fail.
• Report quality, correctness, time, cost, tool coverage, verification, and review gaps separately.
• Score the trajectory as its own dimension beside the outcome and never fold the two into one total: it asks whether the run searched before it asserted, took approval before an irreversible side effect, used an available tool instead of guessing, and verified before claiming done. A run that reached the right answer with one of those skipped or out of order scores lower on trajectory than one that did not, and the report carries both dimensions.
• When the question is an agent judging and improving its own output rather than comparing executors, load `omh-agent-evaluation/references/self-evaluation-loops.md` and pick the loop shape from it - reflection, evaluator-optimizer, or test-driven refinement - remembering that an executable check outranks a judge whenever one exists.
• Declare all three stop rules before the loop runs - a maximum iteration count, a score threshold chosen in advance, and a no-improvement break - and report the iteration count, the final score, and which of the three ended the run. A loop whose only stop is that the output looks good now is a defect.
• Write criteria before generation and score a rubric dimension by dimension beside its total: criteria derived from an output describe it instead of testing it, and a single number hides which dimension failed.
• Recommend executor choice per scenario and confidence, not as a universal ranking.

Handoff policy:

Keep evaluation design and scoring in Hermes. Actual executor runs, costs, timings, tool calls, code edits, and review results must come from observed runtime or supplied artifacts.

Required inputs:

• candidate executors or agents
• task set and fixtures
• success criteria and scoring rubric
• allowed tools, budget, timebox, and isolation policy
• observed run artifacts when comparing completed attempts

Expected outputs:

• paired_run_decision/v1
• not-evidence boundary

Artifact expectations:

• paired_run_decision/v1 with per-task input digests, explicit criteria, baseline and variant exposure, attempted-run and per-dispatch time budgets, signed observed_at receipt provenance, and a scoped Pareto outcome

Safety ru