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 lingxling/awesome-skills-cn --skill agent-evaluationgit clone --depth 1 https://github.com/lingxling/awesome-skills-cnWrote 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/lingxling/awesome-skills-cn/agent-evaluation)<a href="https://agentmods.dev/skills/lingxling/awesome-skills-cn/agent-evaluation"><img src="https://agentmods.dev/badge/skills/lingxling/awesome-skills-cn/agent-evaluation/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/lingxling/awesome-skills-cn/agent-evaluation"><img src="https://agentmods.dev/badge/skills/lingxling/awesome-skills-cn/agent-evaluation.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 | $0.00034 | $0.01551 |
| Opus 5.5 | $0.00014 | $0.00620 |
| Sonnet 5.5 | $0.00007 | $0.00310 |
| Haiku 4.5 | $0.00003 | $0.00155 |
Grade A, and why
agent-evaluation 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 19d 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.
This is a copy
100% identical to agent-evaluation — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Evaluation
Evaluate observable agent behavior against task-specific cases. Modified by AAS maintainers on 2026-09-05 to remove unsupported benchmark claims, correct uncertainty/error reporting and separate optional architecture sketches from the operating procedure.
When to Use
Use when comparing a changed agent, prompt or tool configuration, reproducing an observed failure, or estimating reliability on a declared task distribution. Do not infer product readiness from a public benchmark percentage or a generic score threshold.
Prerequisites
- A versioned case set with expected observable outcomes and permission boundaries.
- A known baseline and candidate revision, including model, prompt, tools, configuration and runtime versions.
- Authorized synthetic or redacted inputs, isolated targets and a bounded token, time and cost budget.
- A verifier that distinguishes wrong outcomes, expected safe rejections, evaluator failures and infrastructure outages. Provider access is needed only if the declared evaluation calls that provider.
Evaluation procedure
- Freeze the contract. Record case IDs and dataset revision, baseline/candidate identities, target environment, repeat plan, budgets, stopping rule and decision criteria before execution. Keep critical safety and authorization failures separate from average quality; they cannot be compensated by a higher score.
- Validate the harness. Run a known-pass case, a known-fail case and a deliberate verifier/infrastructure failure. Confirm that each is classified correctly and that trace retention excludes credentials and private input bodies. If classification is wrong, fix the harness and repeat these checks before measuring the agent.
- Run the frozen cases. Use the same case definitions and budgets for baseline and candidate, with independent fixture state and recorded execution order. Retain every attempt and its run ID, outcome, reason, latency and resource totals. An exception is not evidence that an unsafe request was safely rejected.
- Investigate variation. Preserve the original failure. Classify disagreement as agent behavior, shared-state contamination, verifier ambiguity or an outage. Use only the predeclared repeat budget; do not retry until green, silently drop failures or change the expected outcome to fit the candidate. An unresolved harness fault makes the affected result inconclusive.
- Compare and decide. Report per-case results and uncertainty, regressions, critical failures and incomplete cases. Repeated runs of one case are not independent samples of the task distribution. A changed expectation needs a separately reviewed contract revision and reruns of both baseline and candidate; keep the old results.
- Fix and verify. Make a bounded fix, rerun the failing case to verify the mechanism, then rerun the applicable frozen regression suite from clean state. Stop at the declared budget if disagreement persists. Record pass, fail or inconclusive with the exact evidence; follow the project publication/deployment approval boundary separately.
What ships with it
1 file 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.
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.
- 19d ago Changed · -1,049 lines · -4 tokens per session scan B → A 35a81463e739
- 1mo ago First seen · 1,136 lines · 38 tokens per session scan B c7a2bca261ed
agent-evaluation is a skill published in the GitHub repository lingxling/awesome-skills-cn (321 stars, last pushed 18d ago), licensed MIT. It adds 34 tokens to every session and 1,551 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to agent-evaluation, differing in 0 lines, and is treated as a copy.
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