ai-evaluation-patterns

ai-evaluation-patterns is a skill for Claude Code, Codex from FerroxLabs/wayland. It costs 86 tokens per session (8,226 once invoked), scanned A, original, AGPL-3.0.

A guide to evaluating AI systems using decision frameworks, implementation patterns, and reusable templates. Evaluation means measuring whether an AI system gives the expected results on defined examples and tasks.

In plain words
What is it for?
Use it to plan AI tests, choose evaluation methods, and create workflows for checking model or agent quality.
Why use it?
It helps teams replace informal impressions with repeatable checks. This makes it easier to compare changes and identify problems before or after release.

Skill for Claude CodeCodex

Which agent this was written for is unclear — body not stored (licence); the path alone says nothing.

Good fit Use it to plan AI tests, choose evaluation methods, and create workflows for checking model or agent quality.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/ferroxlabs/wayland/ai-evaluation-patterns
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 FerroxLabs/wayland --skill ai-evaluation-patterns
Clone the repo
git clone --depth 1 https://github.com/FerroxLabs/wayland

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 ai-evaluation-patterns

README.md
[![agentmods](https://agentmods.dev/badge/skills/ferroxlabs/wayland/ai-evaluation-patterns/github.svg)](https://agentmods.dev/skills/ferroxlabs/wayland/ai-evaluation-patterns)
Your own site
<a href="https://agentmods.dev/skills/ferroxlabs/wayland/ai-evaluation-patterns"><img src="https://agentmods.dev/badge/skills/ferroxlabs/wayland/ai-evaluation-patterns/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 ai-evaluation-patterns

Your own site · 80×15
<a href="https://agentmods.dev/skills/ferroxlabs/wayland/ai-evaluation-patterns"><img src="https://agentmods.dev/badge/skills/ferroxlabs/wayland/ai-evaluation-patterns.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 86 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 8,226 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. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 1 finding, up to medium

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • medium Excessive Agency · line 565
    Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.
    Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
How audits are shown
Origin unknown 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 $0.00086 $0.08226
Opus 5.5 $0.00034 $0.03290
Sonnet 5.5 $0.00017 $0.01645
Haiku 4.5 $0.00009 $0.00823

Measured 1mo ago against content hash 8082fe549611, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

ai-evaluation-patterns 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.

src/process/resources/skills-library/bodies/skills/ai-machine-learning/ai-evaluation-patterns/SKILL.md · 576 lines

The source is not reproduced here

Licensed AGPL-3.0

The repository is licensed AGPL-3.0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.

Read it on GitHub

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 · 576 lines · 86 tokens per session scan A 8082fe549611

Subscribe to this mod's changes

ai-evaluation-patterns is a skill published in the GitHub repository FerroxLabs/wayland (613 stars, last pushed yesterday), licensed AGPL-3.0. It adds 86 tokens to every session and 8,226 once invoked, about $0.0003 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.