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 guia-matthieu/clawfu-skills --skill agent-evaluationgit clone --depth 1 https://github.com/guia-matthieu/clawfu-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/guia-matthieu/clawfu-skills/agent-evaluation)<a href="https://agentmods.dev/skills/guia-matthieu/clawfu-skills/agent-evaluation"><img src="https://agentmods.dev/badge/skills/guia-matthieu/clawfu-skills/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/guia-matthieu/clawfu-skills/agent-evaluation"><img src="https://agentmods.dev/badge/skills/guia-matthieu/clawfu-skills/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 | $0.00055 | $0.02033 |
| Opus 5 | $0.00028 | $0.01017 |
| Sonnet 5 | $0.00011 | $0.00407 |
| Haiku 4.5 | $0.00006 | $0.00203 |
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 7d 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 — 300 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Evaluation
Overview
Core principle: Agents are non-deterministic. Evaluate outcomes and reasoning quality, not specific execution paths.
Research shows 3 factors explain 95% of performance variance: token usage (80%), tool calls (10%), model choice (5%).
When to Use
- After creating a new skill
- Before deploying an agent to production
- When agent behavior is inconsistent
- For
/qa-reviewof AI-assisted work - Comparing approaches or models
Quick Reference: 5-Dimension Rubric
| Dimension | Weight | What to check |
|---|---|---|
| Instruction Following | 30% | Did it do what was asked? |
| Output Completeness | 25% | Are all requirements covered? |
| Tool Efficiency | 20% | Minimal, appropriate tool use? |
| Reasoning Quality | 15% | Is the logic sound? |
| Response Coherence | 10% | Clear, well-structured? |
Pass threshold: 0.70 (general), 0.85 (critical operations)
Evaluation Methods
1. Direct Scoring (Fast)
For quick skill checks:
## Evaluation: [Skill/Agent Name]
**Test case:** [What was asked]
**Output:** [What was produced]
### Scores (0.0-1.0)
| Dimension | Score | Justification |
|-----------|-------|---------------|
| Instruction Following | X.X | [Why] |
| Output Completeness | X.X | [Why] |
| Tool Efficiency | X.X | [Why] |
| Reasoning Quality | X.X | [Why] |
| Response Coherence | X.X | [Why] |
**Weighted Total:** X.XX
**Pass/Fail:** [PASS if ≥0.70]
Critical: Always require justification BEFORE the score. This improves reliability 15-25%.
2. LLM-as-Judge (Scalable)
For systematic testing:
## Judge Prompt Template
You are evaluating an AI agent's output.
**Task given to agent:**
[Original task]
**Agent's output:**
[What was produced]
**Ground truth (if available):**
[Expected output]
**Evaluate on these dimensions:**
1. Instruction Following (30%): Did it do exactly what was asked?
2. Output Completeness (25%): Are all parts of the request addressed?
3. Tool Efficiency (20%): Were tools used appropriately and minimally?
4. Reasoning Quality (15%): Is the logic sound and traceable?
5. Response Coherence (10%): Is it clear and well-organized?
**For each dimension:**
1. First explain your reasoning
2. Then give a score 0.0-1.0
3. Calculate weighted total
4. State PASS (≥0.70) or FAIL (<0.70)
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.
- 7d ago First seen · 300 lines · 55 tokens per session scan A bde86daa8e69
agent-evaluation is a skill published in the GitHub repository guia-matthieu/clawfu-skills (149 stars, last pushed 5mo ago), licensed MIT. It adds 55 tokens to every session and 2,033 once invoked, about $0.0003 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 2026-09-03.
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