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/.claude/commands/dart-review-pr.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/commands/dartsim/dart/dart-review-pr)<a href="https://agentmods.dev/commands/dartsim/dart/dart-review-pr"><img src="https://agentmods.dev/badge/commands/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/commands/dartsim/dart/dart-review-pr"><img src="https://agentmods.dev/badge/commands/dartsim/dart/dart-review-pr.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 | $0.00007 | $0.00880 |
| Opus 5 | $0.00003 | $0.00440 |
| Sonnet 5 | $0.00001 | $0.00176 |
| Haiku 4.5 | $0.00001 | $0.00088 |
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 2d 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 — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.
Use a distinct non-author session for each substantive scope. Check code style,
tests, docs, and focused commits. For 3D claims,
require the dart-verify-sim text oracle plus assessed visual/debug evidence,
or a justified replacement. Report every surviving finding as a coherent batch, including
repair regressions and earlier findings whose disposition is unsupported.
Stay read-only. For a local candidate return the final JSON report defined in
docs/onboarding/ai-tools.md for the parent to import with review-gate record.
Include observed session/model/effort, coverage, completion, findings with stable
IDs and concrete evidence, and verified dispositions. Do not mutate the evidence
store yourself. A clean verdict requires complete coverage for the current stage under the
review owner; explicitly retain pending hosted acceptance checks.
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.
- 2d ago Changed · +15 lines 769447e43ebd
- 3d ago Changed · +1 lines 21a7baa7a334
- 5d ago First seen · 70 lines · 7 tokens per session scan A ffc3344265eb
dart-review-pr is a command published in the GitHub repository dartsim/dart (1,202 stars, last pushed 2d ago), licensed BSD-2-Clause. It adds 7 tokens to every session and 880 once invoked, about $0.0000 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 2026-09-03.
Other commands, from other repositories
pr
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quality-check-command
This command performs comprehensive code quality checks. Use it before commits or when implementation is complete.
gsd-quality
Route to the appropriate quality / review skill based on the user's intent. gsd-code-review-fix was absorbed by gsd-code-review --fix in #2790.
p5-impl-refactor
Improves code quality without changing behavior. All tests must continue to pass.
p5-review-code
Checks code for quality, clean code, logic errors and test coverage. Delivers review findings with severity level.