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 charlieviettq/awesome-agent-skill --skill agent-evaluationgit clone --depth 1 https://github.com/charlieviettq/awesome-agent-skillWrote 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/charlieviettq/awesome-agent-skill/agent-evaluation)<a href="https://agentmods.dev/skills/charlieviettq/awesome-agent-skill/agent-evaluation"><img src="https://agentmods.dev/badge/skills/charlieviettq/awesome-agent-skill/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/charlieviettq/awesome-agent-skill/agent-evaluation"><img src="https://agentmods.dev/badge/skills/charlieviettq/awesome-agent-skill/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 | $0.00045 | $0.00370 |
| Opus 5 | $0.00023 | $0.00185 |
| Sonnet 5 | $0.00009 | $0.00074 |
| Haiku 4.5 | $0.00005 | $0.00037 |
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 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.
What it actually says
Agent evaluation
What to measure
| Dimension | Examples |
|---|---|
| Task success | End state matches spec (binary or rubric) |
| Tool use | Correct tool, valid args, no spurious calls |
| Safety | No policy violations, no secret leakage |
| Efficiency | Tokens, latency, tool call count |
| Stability | Same input -> consistent outcome across runs |
Workflow
- Define tasks — realistic user intents with clear pass/fail or scored rubric.
- Build dataset — golden set + edge cases (errors, ambiguous input, empty context).
- Run baseline — fixed model/settings; log traces (inputs, tools, outputs).
- Score — automated checks first; human review for ambiguous cases.
- Compare — A/B prompts, models, or tool schemas; report deltas with confidence notes.
- Gate — block release on regression in must-pass tasks.
Automated checks
- Schema validation on tool arguments.
- Assert final answer contains required fields or avoids forbidden content.
- Snapshot tests for deterministic sub-steps where possible.
Human rubric (when needed)
Score 1-5 on: correctness, completeness, tone, safety. Document disagreements.
Anti-patterns
- Eval only on cherry-picked happy paths.
- Changing task and model simultaneously without isolation.
- No trace logs when debugging tool failures.
Output
Summary table: variant | success rate | avg tools | avg latency | notes.
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 · 47 lines · 45 tokens per session scan A e2b93ce67a00
agent-evaluation is a skill published in the GitHub repository charlieviettq/awesome-agent-skill (25 stars, last pushed 1mo ago), licensed MIT. It adds 45 tokens to every session and 370 once invoked, about $0.0002 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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