An evaluation runner for testing a named AI analyst configuration against a fixed set of cases. An evaluation is a repeatable check of how well a system handles known tasks.

Use it to run working, held-out, capability, or regression tests, compare system changes, inspect accuracy, and report passes, failures, blocks, and errors.

It makes changes easier to compare and keeps trial outputs, test conditions, and result types separate and recorded.

eval — Skill for Claude Code by ai-analyst-lab/ai-analyst

Install

Getting it into your agent

It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.

Clone the repo

git clone --depth 1 https://github.com/ai-analyst-lab/ai-analyst

agentmods

npx agentmods add skills/ai-analyst-lab/ai-analyst/eval

Made for: Claude Code.

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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/ai-analyst-lab/ai-analyst/eval/github.svg)](https://agentmods.dev/skills/ai-analyst-lab/ai-analyst/eval)

Your own site

<a href="https://agentmods.dev/skills/ai-analyst-lab/ai-analyst/eval"><img src="https://agentmods.dev/badge/skills/ai-analyst-lab/ai-analyst/eval/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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/ai-analyst-lab/ai-analyst/eval"><img src="https://agentmods.dev/badge/skills/ai-analyst-lab/ai-analyst/eval.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 43 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 2,061 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.

Model	Per session	Once invoked
Fable 5.1	$0.00043	$0.02061
Opus 5.5	$0.00017	$0.00824
Sonnet 5.5	$0.00009	$0.00412
Haiku 4.5	$0.00004	$0.00206

Measured 1mo ago against content hash 3696e3567bb8, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

eval 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.

.claude/skills/eval/SKILL.md · 153 lines

How it starts

The opening of the file, as written. The whole thing — 153 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Evaluate the system

Before running

Name the exact system under test. Record its model, instructions, skills, agents, helpers, knowledge, workflow, tools, connector configuration, and data snapshot.

Use one of these modes honestly:

Working mode supports iteration. Its references may be visible to the evaluator, but never to the child trial before its output is locked.
Course heldout mode sends locked outputs to the course-controlled grader. The expected results do not live in the student clone.
A local visible answer file is development material. Do not call it a secret heldout evaluation.

Run

For a complete-analysis case, load the question-only case from evals/cases/public/. Focused component and calculation suites live under evals/focused/public/.
Select the exposure, purpose, named cases, and any slice before the run starts. Exposure is working or heldout. Purpose is capability or regression. Do not treat these as one dimension.
Use helpers.evals.controller.EvaluationController to launch and record the trials.
Give each trial only its public task, permitted system files, permitted data, and permitted tools.
Lock every trial output before grading begins.
Grade deterministic criteria first. Keep model-based grades separate.
Preserve pass, fail, blocked, error, invalid, and unknown as different results.
Report every case and slice before discussing the aggregate.

The local controller is available through python3 -m helpers.evals.cli run-suite. Use --model claude-opus-4-6, --exposure, optional --purpose, and repeated --case-id arguments when selecting a subset. General code access is not required for routing or contract cases. When local data analysis requires --allow-code, state that local process isolation is not the same as course-heldout answer isolation.

For a reviewed working suite with local references, lock the trial outputs first, then grade them with python3 -m helpers.evals.cli grade-suite. Pass the run ID, public manifest, and reviewed reference file. Never copy the reference file into the trial workspace.

Read the full file on GitHub · 153 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.

1mo ago Changed · +62 lines 3696e3567bb8
1mo ago Changed · +42 lines e6def2467ae1
1mo ago Changed · +8 lines d0d6e37de55b
1mo ago Changed · -2 tokens per session 7fd64defc46f
1mo ago First seen · 41 lines · 45 tokens per session scan A c9fc9f395aab

Subscribe to this mod's changes

eval is a skill published in the GitHub repository ai-analyst-lab/ai-analyst (322 stars, last pushed 1mo ago), licensed MIT. It adds 43 tokens to every session and 2,061 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 2026-09-12.

Analytics and metrics Building agents

eval next to 6 skills from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
evalai-analyst-lab/ai-analyst	Skill	322	1mo ago	MIT	A	43	Claude Code
imagegenopenai/codex	Skill	133k	yesterday	Apache-2.0	A	113	Codex
playwright-climicrosoft/playwright	Skill	99k	yesterday	Apache-2.0	A	19	Claude Code
local-ai-agentsmicrosoft/ai-agents-for-beginners	Skill	79k	4d ago	MIT	A	200	Claude Code, Codex
systematic-debuggingobra/superpowers	Skill	313k	3d ago	MIT	A	21	Claude Code
next-cache-components-adoptionvercel/next.js	Skill	143k	yesterday	MIT	A	95	Claude Code
next-partial-prefetching-adoptionvercel/next.js	Skill	143k	yesterday	MIT	A	103	Claude Code