madbench-evals

madbench-evals is a skill for Claude Code from MadAppGang/magus. It costs 57 tokens per session (8,158 once invoked), scanned A, original, MIT.

A guide to writing and debugging madbench evaluation files. Madbench is a test harness that runs coding agents through scenarios and grades their results with checks.

In plain words
What is it for?
Use it when authoring or reviewing madbench YAML files, selecting checks, tuning expectations, or diagnosing a failing benchmark.
Why use it?
It helps make agent evaluations reproducible and explains how to investigate failed checks, test data, controls, and reports.

Skill for Claude Code

Written for Claude Code: disable-model-invocation in frontmatter. Also seen: mentions subagents; names the AskUserQuestion tool; mentions Claude Code.

Part of the madbench plugin — 3 skills, 1 command, 1 agent shipped together

Good fit Use it when authoring or reviewing madbench YAML files, selecting checks, tuning expectations, or diagnosing a failing benchmark.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/madappgang/magus/madbench-evals
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 MadAppGang/magus --skill madbench-evals
Clone the repo
git clone --depth 1 https://github.com/MadAppGang/magus

Made for: Claude Code.

Or install madbench, the plugin that ships this one along with the rest of its 3 skills, 1 command, 1 agent.

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 madbench-evals

README.md
[![agentmods](https://agentmods.dev/badge/skills/madappgang/magus/madbench-evals/github.svg)](https://agentmods.dev/skills/madappgang/magus/madbench-evals)
Your own site
<a href="https://agentmods.dev/skills/madappgang/magus/madbench-evals"><img src="https://agentmods.dev/badge/skills/madappgang/magus/madbench-evals/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 madbench-evals

Your own site · 80×15
<a href="https://agentmods.dev/skills/madappgang/magus/madbench-evals"><img src="https://agentmods.dev/badge/skills/madappgang/magus/madbench-evals.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 57 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 8,158 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 pass 7 Sept 2026
How audits are shown
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 $0.00057 $0.08158
Opus 5.5 $0.00023 $0.03263
Sonnet 5.5 $0.00011 $0.01632
Haiku 4.5 $0.00006 $0.00816

Measured yesterday against content hash 2fac2f50dda0, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

madbench-evals 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 yesterday.

The scan reads SKILL.md. This mod also ships 2 executable files (examples/image-generated/generate.ts, examples/metrics-module/module/index.ts), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

plugins/madbench/skills/madbench-evals/SKILL.md · 502 lines

How it starts

The opening of the file, as written. The whole thing — 502 lines — stays where its author put it; the contents beside it link to each section on GitHub.

madbench Benches and Experiments — author, run, debug

madbench is a Go harness that benchmarks agentic coding tools (Claude Code, etc.). You write a bench file (madbench.yaml / *.madbench.yaml), point it at a harness and model, give it scenarios with seeded testdata, and grade the agent's work with checks.

These skills belong to madbench. They are written in the madbench repository and ship with the release they describe; any other copy is a copy. A mistake here is fixed upstream, never by editing a copy.

The madbench release these files were written for is in MADBENCH_VERSION beside this file — one line, x.y.z. No file here restates a version number — five files each asserting one are five places to fall out of step with the binary. Reference files:

  • glossary.md — read first, every time (Step 0): the terms, the deprecated and renamed terms, other tools' terms and what madbench calls each
  • schema.md — every bench and Experiment key: scenarios, repo: · setup: · generate: · image: · cwd:, the sandbox block, driver:, unproven:, metrics: expressions and the bench's module/, params and placeholders, guard_changes:, how an unknown key fails
  • checks-catalog.md — every Check family, incl. the environment:* family (nine types), the 18 session:* types (the Driver's four and session:match among them), thread/outcome scoping, matchers, readout:, judges and votes
  • harness-and-sandbox.md — the harness: block's keys, interactive: and permission modes, sandbox levels, the environment probe and MCP preflight, the whole CLI, exit codes
  • debugging.md — error→cause map, the two controls, report-JSON analysis, tuning

Every madbench capability claim in these files carries a docs/<file>.md:<line> citation into the madbench checkout's docs/. Never pkg/**/*.go: the checkout builds dev.

Read the full file on GitHub · 502 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. yesterday Changed · +2 lines 2fac2f50dda0
  2. 3d ago Changed · +1 lines 458b11236867
  3. 10d ago Changed · +56 lines 214c50b0d4cd
  4. 20d ago Changed a3e94dab6251
  5. 1mo ago Changed 4768c11dabc5
  6. 1mo ago Changed · +170 lines · -3 tokens per session bacda143cc14
  7. 1mo ago First seen · 273 lines · 60 tokens per session scan A 48e1066a167d

Subscribe to this mod's changes

madbench-evals is a skill published in the GitHub repository MadAppGang/magus (13 stars, last pushed today), licensed MIT. It adds 57 tokens to every session and 8,158 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 2026-09-04.

Related

Other skills, from other repositories

research-engineer

An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.

davila7/claude-code-templates · 43 tokens

eval-dataset-design

A guide to creating reliable test sets for AI agents. Each evaluation task includes the request, the simulated user's behaviour, a reset starting state, and a success check that can be independently verified.

bojieli/ai-agent-book · 138 tokens

tika-eval-compare

Compare extracts from two Tika builds over a corpus to detect regressions in content, encoding, exceptions, and embedded-document handling. Use for "compare before/after extracts", "eval this change against the corpus".

apache/tika · 50 tokens

cua-driver

Use Cua Driver for desktop or browser tasks that are awkward or unavailable through Bash/APIs, or when the user explicitly wants GUI interaction: app testing, visual bug reproduction, form filling, calendar entry, screenshots, and demo recording. Also covers Cua setup; not OpenAI Codex Computer Use or web research.

davidondrej/skills · 68 tokens

jetson-validate-image

Use after jetson-flash-image to run static BSP checks, on-target smoke/regression tests on a flashed DUT, or both. Not for build or flash steps. Triggers: validate bsp, on-target validation.

NVIDIA/skills · 50 tokens

atmos-validation

Validate Atmos projects, components, arbitrary JSON Schema inputs, EditorConfig, and GitHub Actions; use affected-file selection and native CI annotations.

cloudposse/atmos · 31 tokens