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.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add dartsim/dart --skill dart-downstream-fixgit 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-downstream-fix)<a href="https://agentmods.dev/skills/dartsim/dart/dart-downstream-fix"><img src="https://agentmods.dev/badge/skills/dartsim/dart/dart-downstream-fix/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-downstream-fix"><img src="https://agentmods.dev/badge/skills/dartsim/dart/dart-downstream-fix.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 | $0.00701 |
| Opus 5.5 | $0.00010 | $0.00280 |
| Sonnet 5.5 | $0.00005 | $0.00140 |
| Haiku 4.5 | $0.00003 | $0.00070 |
Grade A, and why
dart-downstream-fix 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 10d 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 — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.
dart-downstream-fix
Use this skill to run the DART dart-downstream-fix workflow. The editable workflow
source lives in .claude/commands/; this generated skill is its entrypoint for
Codex and Muse.
Invocation
- Claude Code:
/dart-downstream-fix <arguments> - Codex:
$dart-downstream-fix <arguments> - Muse:
/dart-downstream-fix <arguments>
Treat the text after the skill name, or the user's request when the skill is
loaded on demand, as $ARGUMENTS. When the workflow references $1, $2,
etc., map those to the positional values supplied by the user.
Command Body
Fix downstream-reported DART issue: $ARGUMENTS
Required Reading
@AGENTS.md @docs/onboarding/contributing.md @docs/onboarding/ci-cd.md
When To Use
Use for downstream issues in gz-physics, Gazebo, or gz-sim that trace back to DART behavior: crashes, assertions, NaN/Inf propagation, missing validation, or DART performance regressions.
Workflow
If the downstream symptom depends on scene/model structure, simulation,
collision/contact, or Filament output, route through dart-verify-sim: establish a
text oracle, add assessed claim-tied visual evidence, or document a
visual exception when graphics support is unavailable or not applicable.
- Read the downstream issue, logs, stack traces, and reproduction steps.
- Identify the DART API, component, and invalid usage pattern involved.
- Search for related validation and recovery patterns in DART.
- Plan the smallest fix and the regression test location.
- Decide whether the bug applies to the active release line. For applicable
bug fixes, implement on the development branch,
main:- branch:
fix/<downstream-project>-<issue-number>-<brief-description> - add a regression test that reproduces the downstream symptom
- keep the fix minimal; no unrelated refactors
- branch:
- Run
pixi run lintand relevant tests; usepixi run test-allwhen feasible, and runpixi run -e gazebo test-gzwhen Gazebo/gz-physics compatibility could be affected. - Ask for explicit maintainer/user approval before pushing or creating PRs.
After approval, create the PR against
mainwith the milestone resolved from the target branch'sdocs/onboarding/release-management.md, and reference the downstream issue.
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.
- 10d ago Changed · +1 lines 15dd881cd8ed
- 12d ago Changed · +1 lines 0ce9bd0476ca
- 13d ago Changed d74abaa477d9
- 18d ago Changed 3072c1be07d8
- 24d ago Changed · -2 lines a427cc865973
- 1mo ago Changed add1797dc2d0
- 1mo ago Changed · -3 lines f7ed27916c27
- 1mo ago First seen · 78 lines · 25 tokens per session scan A 27d01432f871
dart-downstream-fix is a skill published in the GitHub repository dartsim/dart (1,217 stars, last pushed yesterday), licensed BSD-2-Clause. It adds 25 tokens to every session and 701 once invoked, about $0.0001 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 2026-09-01.
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