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-review-pr/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-review-pr)<a href="https://agentmods.dev/skills/dartsim/dart/dart-review-pr"><img src="https://agentmods.dev/badge/skills/dartsim/dart/dart-review-pr/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-review-pr"><img src="https://agentmods.dev/badge/skills/dartsim/dart/dart-review-pr.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 | $0.00016 | $0.01032 |
| Opus 5 | $0.00008 | $0.00516 |
| Sonnet 5 | $0.00003 | $0.00206 |
| Haiku 4.5 | $0.00002 | $0.00103 |
Grade A, and why
dart-review-pr 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 3d 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 — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.
dart-review-pr
Use this skill in Codex to run the DART dart-review-pr 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-review-pr <arguments> - Codex:
$dart-review-pr <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
Review or respond to PR: $ARGUMENTS
Required Reading
@AGENTS.md @docs/onboarding/code-style.md @docs/onboarding/ai-reviews.md @docs/onboarding/ai-tools.md @docs/ai/verification.md
Workflow
Pick the sub-workflow from mode= in $ARGUMENTS, defaulting to review.
Review A Local Candidate Or PR
For candidate=<id>, read its candidate.json at the path printed by
review-gate prepare. Verify the supplied base/head/tree and inspect
git diff <merge_base> <head> with surrounding code. Work from the immutable
candidate in an isolated read-only checkout; do not accidentally review dirty
files or a later HEAD. No PR needs to exist. The parent supplies objective,
acceptance criteria, factual gates, prior findings, and author-session IDs.
For a PR number, obtain its current head/base and complete diff with
gh pr view and gh pr diff, then follow the same coverage policy. A PR review
without a prepared local candidate is useful feedback, not publication evidence.
Apply the assigned scope from docs/onboarding/ai-reviews.md: correctness
covers the complete PR diff and acceptance evidence; contracts independently
traces consumers, sibling cases, and negative cases and records the required
input/consumer matrix for exclusions, parsers, or validators. Challenge test
oracles against actual requirements. A non-substantive assessment must prove
unchanged behavior under the owner's strict baseline rules. Missing evidence
or unobserved effective reviewer settings makes the report incomplete.
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.
- 3d ago Changed · +15 lines 833f0e168a8e
- 5d ago Changed · +1 lines 909f33344eb8
- 7d ago Changed · -2 lines f0757b63d605
- 9d ago First seen · 93 lines · 16 tokens per session scan A 63e5cd1dcf60
dart-review-pr is a skill published in the GitHub repository dartsim/dart (1,204 stars, last pushed 3d ago), licensed BSD-2-Clause. It adds 16 tokens to every session and 1,032 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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