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-model-upgradegit 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-model-upgrade)<a href="https://agentmods.dev/skills/dartsim/dart/dart-model-upgrade"><img src="https://agentmods.dev/badge/skills/dartsim/dart/dart-model-upgrade/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-model-upgrade"><img src="https://agentmods.dev/badge/skills/dartsim/dart/dart-model-upgrade.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 | $0.00042 | $0.02290 |
| Opus 5.5 | $0.00017 | $0.00916 |
| Sonnet 5.5 | $0.00008 | $0.00458 |
| Haiku 4.5 | $0.00004 | $0.00229 |
Grade A, and why
dart-model-upgrade 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 7d 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 — 186 lines — stays where its author put it; the contents beside it link to each section on GitHub.
dart-model-upgrade
Use this skill to run the DART dart-model-upgrade workflow. The editable workflow
source lives in .claude/commands/; this generated skill is its entrypoint for
Codex and Muse.
Invocation
- Claude Code:
/dart-model-upgrade <arguments> - Codex:
$dart-model-upgrade <arguments> - Muse:
/dart-model-upgrade <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
Audit or update DART's AI infrastructure for: $ARGUMENTS
Objective
Use live primary guidance and representative DART tasks to decide what to preserve, update, remove, consolidate, or add. Prefer the smallest change that improves the target without weakening the branch's compatibility rules, safety, evidence, public paths, or cross-tool capability parity. The reusable intake, comparison, verification, and closeout core stays model-agnostic. This workflow is itself an audit surface: improve it when a target exposes a reusable gap, and replace stale target-specific guidance instead of accumulating one command or permanent branch per model family. Because DART is a 3D physics simulator, every target also demonstrates how well it investigates simulation state with text-first and Filament visual/debug evidence.
Required Reading
@AGENTS.md @docs/AGENTS.md @docs/README.md @docs/information-architecture.md @docs/ai/principles.md @docs/ai/README.md @docs/ai/components.md @docs/ai/sessions.md @docs/ai/workflows.md @docs/ai/verification.md @docs/ai/branch-profile.json @docs/onboarding/ai-tools.md @docs/onboarding/release-management.md @docs/dev_tasks/README.md
Workflow
- Normalize target, mode, and authority. Preserve the named model,
reasoning mode, tool version, branch, and
audit-onlyorapplyboundary. Record whether external mutations are approved. Do not substitute a model or pin the repository to the audit target.audit-onlyis read-only for tracked files and external state: do not edit, regenerate, create task state, run auto-fixing lint, commit, push, or mutate GitHub.applypermits scoped local implementation; external mutations still need approval. - Capture the release control. Before apply-mode edits, record
gitstate, installed versions,pixi run ai-doctor --json, model/config references, prompt and instruction sizes, generated skill-metadata size, custom-agent inheritance, hooks, scenarios, durable context and project state, active plan/dev-task handoffs, and the branch-localdart-verify-sim/Filament/image-evaluation route. Run baseline read-only gates. Createdocs/dev_tasks/<task>/only when the apply task is genuinely multi-session; audit-only reports the proposed path without creating it. - Refresh primary guidance. Read current official model, prompting, migration, configuration, skills, agents, and hook guidance relevant to the target. Record URLs, retrieval date, and installed-version evidence. Separate reusable procedure from target-specific evidence and flag any assumption in this workflow that the target invalidates.
- Classify every finding. Use these verdicts:
- preserve — intentional and evidence-backed;
- update — stale or incorrect guidance/configuration;
- remove/consolidate — repeated detail costs context without changing behavior;
- add — a missing trigger, contract, diagnostic, or gate has a distinct owner and representative failure it prevents.
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.
- 7d ago Changed · +3 lines d84b370d2256
- 11d ago Changed · +1 lines 00c496ee5afd
- 13d ago Changed · +2 lines · -2 tokens per session 4a0a7d359134
- 14d ago Changed · +2 lines d7a96a073301
- 19d ago Changed 9c3974b4a066
- 25d ago Changed · +7 lines · -1 tokens per session c842783ac044
- 1mo ago Changed · +1 lines d34189d4178a
- 1mo ago Changed · -47 lines · +3 tokens per session 0dca1c1afd09
- 1mo ago Changed · +6 lines d68742db3c7d
- 1mo ago First seen · 211 lines · 42 tokens per session scan A be9d7ffc072d
dart-model-upgrade is a skill published in the GitHub repository dartsim/dart (1,217 stars, last pushed today), licensed BSD-2-Clause. It adds 42 tokens to every session and 2,290 once invoked, about $0.0002 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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