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-evaluation-reportinggit 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-reporting)<a href="https://agentmods.dev/skills/lingxling/awesome-skills-cn/agent-evaluation-reporting"><img src="https://agentmods.dev/badge/skills/lingxling/awesome-skills-cn/agent-evaluation-reporting/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-reporting"><img src="https://agentmods.dev/badge/skills/lingxling/awesome-skills-cn/agent-evaluation-reporting.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 | $0.00031 | $0.01732 |
| Opus 5.5 | $0.00012 | $0.00693 |
| Sonnet 5.5 | $0.00006 | $0.00346 |
| Haiku 4.5 | $0.00003 | $0.00173 |
Grade A, and why
agent-evaluation-reporting 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 10d 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-reporting — 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 — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Evaluation Reporting
Overview
Turn raw agent evaluation runs into a decision-ready report without hiding failures or overstating capability. Keep outcome populations, denominators, latency populations, and experiment conditions explicit so readers can reproduce every headline number.
When to Use This Skill
- Use when reporting benchmark, regression, pilot, or production evaluation runs for an AI agent.
- Use when autonomous and human-assisted completions appear in the same result set.
- Use when failures, timeouts, infrastructure-invalid runs, retries, or partial results affect the denominator.
- Use when comparing two agents, prompts, harnesses, or releases and deciding whether the comparison is valid.
How It Works
Step 1: Freeze the comparison contract
Record the task set and sampling, model and provider, prompt or policy version, tool and harness versions, evaluator rubric, timeout and retry policy, token or cost budget, environment, and human-intervention policy. Assign the configuration a stable label or digest.
If a material condition differs between runs, mark the comparison as non-equivalent. Report a directional observation only; do not claim that the changed agent caused the difference.
Step 2: Build a mutually exclusive outcome ledger
Classify every scheduled attempt exactly once:
| Outcome | Meaning |
|---|---|
autonomous_success |
The agent satisfied the evaluator without human intervention. |
assisted_success |
The task succeeded only after a human intervened. |
failure |
The run reached a terminal, evaluable failure. |
timeout |
The run exhausted its declared time or step budget. |
invalid |
The agent never received a valid evaluation because the harness, environment, or input failed. |
Preserve attempt ID, task ID or seed, retry index, parent attempt ID, configuration label, outcome, intervention count, duration, cost, evaluator evidence, and invalid reason when available. Never silently drop invalid or retried runs.
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.
- 10d ago First seen · 137 lines · 31 tokens per session scan A 800b27a3d99d
agent-evaluation-reporting is a skill published in the GitHub repository lingxling/awesome-skills-cn (312 stars, last pushed 10d ago), licensed MIT. It adds 31 tokens to every session and 1,732 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-reporting, differing in 0 lines, and is treated as a copy.
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