DART is an open-source C++23 physics engine that simulates the movement and interactions of articulated rigid-body systems for robotics, animation, and machine learning. Researchers and developers use it for kinematics, dynamics, collision handling, constraints, and loading robot models, with C++ and Python interfaces. The catalogue add-ons support workflows built around this engine.
Borrowing it
Nothing to install: this file belongs to dartsim/dart. 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/dartsim/dart/main/.agents/skills/dart-audit-agent-compliance/SKILL.mdgit clone --depth 1 https://github.com/dartsim/dartWrote 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/skills/dartsim/dart/dart-audit-agent-compliance)<a href="https://agentmods.dev/skills/dartsim/dart/dart-audit-agent-compliance"><img src="https://agentmods.dev/badge/skills/dartsim/dart/dart-audit-agent-compliance/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/skills/dartsim/dart/dart-audit-agent-compliance"><img src="https://agentmods.dev/badge/skills/dartsim/dart/dart-audit-agent-compliance.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00025 | $0.00565 |
| Opus 5 | $0.00013 | $0.00282 |
| Sonnet 5 | $0.00005 | $0.00113 |
| Haiku 4.5 | $0.00003 | $0.00056 |
Grade A, and why
dart-audit-agent-compliance 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 4d 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 — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.
dart-audit-agent-compliance
Use this skill in Codex to run the DART dart-audit-agent-compliance workflow. The editable
workflow source lives in .claude/commands/; this file is its generated adapter
in the shared .agents/skills/ catalog.
Invocation
- Claude Code:
/dart-audit-agent-compliance <arguments> - Codex:
$dart-audit-agent-compliance <arguments>
Treat the text after the skill name as $ARGUMENTS. When the workflow
references $1, $2, etc., map those to the positional values supplied by the
user.
Command Body
Audit agent compliance issue: $ARGUMENTS
Required Reading
@AGENTS.md @docs/ai/principles.md @docs/ai/verification.md @docs/ai/components.md @docs/onboarding/ai-tools.md @docs/onboarding/contributing.md
Incident Inputs
- Rule violated: $1
- Where documented: $2
- Actual behavior: $3
- Expected behavior: $4
Treat missing positional values as details to infer from the user request or ask about before editing.
Workflow
- Locate the exact existing rule and confirm it is still correct.
- Diagnose why it was missed:
- buried in prose
- wrong file for the task type
- weak emphasis
- duplicated or conflicting guidance
- not referenced from relevant commands or skills
- Prefer restructuring existing docs over adding duplicate content.
- Improve visibility with one or more focused changes:
- move the rule to a loaded file
- make it scannable with a checklist or mandatory marker
- consolidate duplicate guidance
- add cross-references from relevant commands or skills
- Run the principle audit from
docs/ai/principles.mdand usedocs/ai/verification.mdto map audit results to evidence. - Run the relevant gate set from
docs/ai/verification.md.
Output
- Root cause for the missed rule
- Files changed and why
- Which audit items were proven by automation vs manual inspection
- The check, test, or gate that would now catch a recurrence, or an explicit statement that none exists
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.
- 4d ago Changed 47346510aa9a
- 7d ago Changed · -1 lines 15b926908c26
- 9d ago First seen · 75 lines · 25 tokens per session scan A 4048351298b6
dart-audit-agent-compliance is a skill published in the GitHub repository dartsim/dart (1,204 stars, last pushed 4d ago), licensed BSD-2-Clause. It adds 25 tokens to every session and 565 once invoked, about $0.0001 per session on Opus 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 2026-09-01.
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