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 TheGoat395/Codex-Skills --skill agent-evaluation-operationsgit clone --depth 1 https://github.com/TheGoat395/Codex-SkillsWrote 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/thegoat395/codex-skills/agent-evaluation-operations)<a href="https://agentmods.dev/skills/thegoat395/codex-skills/agent-evaluation-operations"><img src="https://agentmods.dev/badge/skills/thegoat395/codex-skills/agent-evaluation-operations/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/thegoat395/codex-skills/agent-evaluation-operations"><img src="https://agentmods.dev/badge/skills/thegoat395/codex-skills/agent-evaluation-operations.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00014 | $0.01101 |
| Opus 5 | $0.00007 | $0.00550 |
| Sonnet 5 | $0.00003 | $0.00220 |
| Haiku 4.5 | $0.00001 | $0.00110 |
Grade A, and why
agent-evaluation-operations scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Evaluation Operations
Evaluate the claim the change is supposed to support, not the amount of new prompt text or the number of passing examples. Routine copyediting of a test comment, ordinary execution of one existing test, or a task that merely mentions agents does not require an evaluation program.
Choose the evaluation mode
- Agent workflow: test the real prompt, model, tools, approvals, state, and failure paths.
- Skill behavior: test whether the skill triggers on the right requests, stays out of unrelated requests, cooperates with adjacent skills, and improves outcomes without excessive context or rigidity.
- Release comparison: hold the harness constant and compare the current baseline with the proposed change.
For evaluating a skill creation or behavior-bearing modification, read skill behavior evaluation. Use the regression corpus when testing synthetic public agent and Codex operating behavior. Validate the corpus with python3 "${CODEX_HOME:-$HOME/.codex}/skills/agent-evaluation-operations/scripts/validate_regression_corpus.py".
Specify before measuring
Record the evaluation claim, tested system, model/reasoning setting, prompt and skill versions, tool access, side-effect policy, attempt budget, acceptance threshold, and what would falsify the claim. Do not compare two runs that silently differ on these dimensions.
Build cases from real work: ordinary success, ambiguous input, missing data, conflicting evidence, missing access, unavailable tools, duplicate events, unsafe external actions, escalation, recovery, and every confirmed historical failure. Keep a small smoke set plus a growing regression set.
Trace run ID, tested-system version, model/reasoning setting, prompt and skill versions, tools, input class, structured result, error, latency, cost, and approval path. Redact or avoid sensitive payload capture by default.
Test retrieval and application separately. A skill may fail to trigger even when its rules are sound, or it may trigger and still fail to change behavior. Keep a small matched baseline and treatment set with identical prompts, inputs, model settings, tools, and viewports; score first attempts blind when subjective judgment matters.
What ships with it
9 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- agents/openai.yaml 256 B
- references/fixture-execution.md 1.8 KB
- references/fixtures/draft-send.json 351 B
- references/regression-corpus.json 20 KB
- references/skill-behavior-evaluation.md 3.1 KB
- scripts/analyze_skill_collisions.py 2.9 KB runs code
- scripts/observe_draft_fixture.py 1.1 KB runs code
- scripts/test_observe_draft_fixture.py 841 B runs code
- scripts/validate_regression_corpus.py 4.0 KB runs code
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.
- 3d ago Changed · +43 lines · -41 tokens per session f2edb6fa2e23
- 10d ago First seen · 27 lines · 55 tokens per session scan A aed5316977eb
agent-evaluation-operations is a skill published in the GitHub repository TheGoat395/Codex-Skills (122 stars, last pushed yesterday), licensed MIT. It adds 14 tokens to every session and 1,101 once invoked, about $0.0001 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 2026-08-30.
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