agent-evaluation

agent-evaluation is a skill for Claude Code, Codex from guia-matthieu/clawfu-skills. It costs 55 tokens per session (2,033 once invoked), scanned A, original, MIT.

A guide to checking the quality of AI skills, commands, and agents by judging their results and reasoning. It uses a scoring framework covering instruction following, completeness, tool use, logic, and clarity.

In plain words
What is it for?
Use it after creating a skill, before deploying an agent, when behavior is inconsistent, or when comparing workflows and models.
Why use it?
AI output can vary from run to run, making it difficult to know whether a workflow is reliable enough to deploy.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it after creating a skill, before deploying an agent, when behavior is inconsistent, or when comparing workflows and models.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/guia-matthieu/clawfu-skills/agent-evaluation
Install

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.

Any agent
npx skills add guia-matthieu/clawfu-skills --skill agent-evaluation
Clone the repo
git clone --depth 1 https://github.com/guia-matthieu/clawfu-skills

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for agent-evaluation

README.md
[![agentmods](https://agentmods.dev/badge/skills/guia-matthieu/clawfu-skills/agent-evaluation/github.svg)](https://agentmods.dev/skills/guia-matthieu/clawfu-skills/agent-evaluation)
Your own site
<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.

agentmods 80×15 button for agent-evaluation

Your own site · 80×15
<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>
Per session 55 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,033 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00055 $0.02033
Opus 5.5 $0.00022 $0.00813
Sonnet 5.5 $0.00011 $0.00407
Haiku 4.5 $0.00006 $0.00203

Measured 1mo ago against content hash bde86daa8e69, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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 1mo 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.

skills/meta/agent-evaluation/SKILL.md · 300 lines

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-review of 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)

Read the full file on GitHub · 300 lines

Changes

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.

  1. 1mo ago First seen · 300 lines · 55 tokens per session scan A bde86daa8e69

Subscribe to this mod's changes

agent-evaluation is a skill published in the GitHub repository guia-matthieu/clawfu-skills (150 stars, last pushed 24d ago), licensed MIT. It adds 55 tokens to every session and 2,033 once invoked, about $0.0002 per session on Opus 5.5. 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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