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-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-pr)<a href="https://agentmods.dev/skills/dartsim/dart/dart-pr"><img src="https://agentmods.dev/badge/skills/dartsim/dart/dart-pr.svg" alt="Measured on agentmods" 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 | $0.00022 | $0.01213 |
| Opus 5 | $0.00011 | $0.00607 |
| Sonnet 5 | $0.00004 | $0.00243 |
| Haiku 4.5 | $0.00002 | $0.00121 |
Grade A, and why
dart-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 today.
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 — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.
dart-pr
Use this skill in Codex to run the DART dart-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-pr <arguments> - Codex:
$dart-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
Prepare the PR locally within the authorized task: $ARGUMENTS Before an action requiring explicit maintainer/user approval under the owner docs, verify that existing authorization covers its action, target, and scope. Ask only for missing or changed authority after completing authorized preparation.
Required Reading
@AGENTS.md @docs/onboarding/contributing.md @docs/onboarding/ai-reviews.md @docs/ai/verification.md @docs/onboarding/changelog.md @.github/PULL_REQUEST_TEMPLATE.md
Drafting the PR
Follow the PR-writing guidance in
docs/onboarding/contributing.md#submitting-a-pull-request and fill the compact
PR template. Keep titles plain, scoped, and outcome-focused, without agent
prefixes. Recent PRs can supply relevant context; use the current owner guidance
rather than copying their length or structure.
Draft from the final diff and the reason for it, not from the session report. Before publication, read the rendered body as a reviewer: the opening must explain the concrete problem and solution, and the rest must earn its space through review-relevant rationale or evidence. Short but generic is not enough.
For 3D structure or behavior changes (model/scene, dynamics, collision/contact,
simulation, rendering, GUI, visual examples), use dart-verify-sim. Preserve the
owner's visible media, assessed comparisons, text oracle, claim boundaries, and
reproduction evidence. Publish transient evidence with pixi run evidence-publish
as described in docs/onboarding/agent-sim-verification.md; never commit
transient evidence. A compact template does not reduce these requirements.
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.
- today Changed · -14 lines c949530ed8fa
- 4d ago Changed · -17 lines 1bdd7b561cf7
- 6d ago First seen · 154 lines · 22 tokens per session scan A 0ea55b614c60
dart-pr is a skill published in the GitHub repository dartsim/dart (1,202 stars, last pushed today), licensed BSD-2-Clause. It adds 22 tokens to every session and 1,213 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.
Other skills, from other repositories
github-pr-workflow
Prepare a GitHub pull request from a feature branch — branch hygiene, commit shape, title/body, verification notes, screenshots for UI work, and replies to review comments.
github-automation
GitHub workflow automation, PR management, issue tracking, and code review coordination. Integrates with GitHub Actions and repository management. Use when: PR creation, code review, issue management, release automation, workflow setup. Skip when: local-only changes, non-GitHub repositories.
review-delta
Review only changes since last commit using impact analysis. Token-efficient delta review with automatic blast-radius detection.
work-unit-commits
Plan commits as reviewable work units. Trigger: implementation, commit splitting, chained PRs, or keeping tests and docs with code.
pr-pending-feedback
Evaluate pending (unsubmitted) review comments on the current branch's PR and, after user confirmation, address each in a separate sub-agent and separate commit.
lean-review
CocoLean diff-scoped over-engineering audit — scans uncommitted git diff and applies five classification tags (delete/stdlib/native/yagni/shrink) to identify unnecessary surface area before commit.