SkillsBench is a benchmark for measuring how effectively AI agents use modular skills—folders containing instructions, scripts, and resources—to complete specialized tasks. It helps researchers and developers evaluate both skill quality and agent behavior, including tasks that require combining multiple skills. The catalogue’s skills and instructions are evaluated as part of this workflow.
Borrowing it
Nothing to install: this file belongs to benchflow-ai/skillsbench. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/benchflow-ai/skillsbench/main/AGENTS.mdgit clone --depth 1 https://github.com/benchflow-ai/skillsbenchWrote 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/instructions/benchflow-ai/skillsbench/agents-md)<a href="https://agentmods.dev/instructions/benchflow-ai/skillsbench/agents-md"><img src="https://agentmods.dev/badge/instructions/benchflow-ai/skillsbench/agents-md/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/instructions/benchflow-ai/skillsbench/agents-md"><img src="https://agentmods.dev/badge/instructions/benchflow-ai/skillsbench/agents-md.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 | $0.00692 | $0.00692 |
| Opus 5.5 | $0.00277 | $0.00277 |
| Sonnet 5.5 | $0.00138 | $0.00138 |
| Haiku 4.5 | $0.00069 | $0.00069 |
Grade A, and why
skillsbench AGENTS.md 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.
How it starts
The opening of the file, as written. The whole thing — 59 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SkillsBench
Benchmark for evaluating how well AI agents use skills. 87 default runnable task definitions, expanding toward 100+.
Commands
uv tool install "benchflow>=0.6.2,<0.7"
uv sync --locked
bench tasks init <task-id>
bench tasks check tasks/<task-id>
bench eval run --tasks-dir tasks/<task-id> --agent oracle --sandbox docker
bench eval run --tasks-dir tasks/<task-id> --agent claude-agent-acp --model <model> --skill-mode with-skill --skills-dir tasks/<task-id>/environment/skills/
bench eval run --tasks-dir tasks/<task-id> --agent claude-agent-acp --model <model> --skill-mode no-skill
bench skills list
bench skills eval <skill-dir> # evaluate a skill against its evals/evals.json
Task Layout
SkillsBench tasks are native BenchFlow task.md packages:
tasks/<task-id>/
task.md # YAML frontmatter + human-written prompt body
environment/
Dockerfile # Container setup
skills/ # Domain skills (generalizable, not task-specific)
oracle/
solve.sh # Oracle (human-written, derives answers via computation)
verifier/
test.sh # Pytest runner, writes reward.txt
test_outputs.py # Outcome-based assertions
Default runnable tasks live in tasks/. Credential-dependent or
integration-incompatible tasks live in tasks-extra/ and are included in
integration sweeps only when --no-default-excludes is passed.
Rules
task.mdprompt body andoracle/solve.shmust be human-written — never generate these- Never mention skill names in the task prompt
- Skills must be generalizable and reusable, not task-specific
- Tests verify outcomes, not process — don't check which tools were used
- Oracle must derive answers through computation, not hardcode them
- No fake scenarios, no synthetic data when real data exists
- Prefer tasks without external API dependencies
task.mdfrontmattermetadatamust validate against taxonomy.yaml — pickcategoryfrom the controlled list, plussubcategory,task_type,modality,interface,skill_type. See taxonomy.md for the codebook and decision rules. CI runs.github/scripts/lint_taxonomy.pyon every PR that touchestasks/**/task.md.
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 First seen · 59 lines · 692 tokens per session scan A 11d227e867ba
skillsbench AGENTS.md is an instructions file published in the GitHub repository benchflow-ai/skillsbench (1,867 stars, last pushed today), licensed Apache-2.0. It adds 692 tokens to every session, about $0.0028 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 2026-08-30.
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next.js AGENTS.md
AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.
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Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, cache eligibility and rollout and applying the notice (cutover).
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AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.
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AGENTS.md instructions for deepseek-ai/deepseek-harness, covering agents.md, pre-stable apis and released session data, repository layout, commands and host sandbox failures.
vscode copilot-instructions.md
Copilot instructions for microsoft/vscode, covering vs code copilot instructions, project overview, root folders, core architecture (src/ folder) and built-in extensions (extensions/ folder).