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-next/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-next)<a href="https://agentmods.dev/skills/dartsim/dart/dart-next"><img src="https://agentmods.dev/badge/skills/dartsim/dart/dart-next.svg" alt="Measured on agentmods" 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 | $0.00025 | $0.01728 |
| Opus 5 | $0.00013 | $0.00864 |
| Sonnet 5 | $0.00005 | $0.00346 |
| Haiku 4.5 | $0.00003 | $0.00173 |
Grade A, and why
dart-next 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 — 166 lines — stays where its author put it; the contents beside it link to each section on GitHub.
dart-next
Use this skill in Codex to run the DART dart-next 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-next <arguments> - Codex:
$dart-next <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
Select and execute the next bounded DART task: $ARGUMENTS
Required Reading
@AGENTS.md @docs/ai/principles.md @docs/ai/north-star.md @docs/ai/workflows.md @docs/ai/verification.md @docs/plans/dashboard.md @docs/dev_tasks/README.md
Load docs/plans/README.md, docs/onboarding/contributing.md,
docs/onboarding/ci-cd.md, and docs/onboarding/ai-tools.md only when the
selected mode or routed workflow needs them.
Arguments
Interpret $ARGUMENTS as optional constraints:
mode=select: choose one task and stop with evidence.mode=execute: choose one task, make local changes, and verify locally. This is the default when the user asks to do the work.mode=pr: execute locally and prepare PR text. Before an action requiring explicit maintainer/user approval underdocs/ai/principles.mdand the PR owner docs, verify existing authorization covers its action, target, and scope; ask only for missing authority. Ordinary authorized local branch creation is preparation; shared-state mutations, branch deletion, and destructive Git operations retain their approval requirements.size=tiny|small|medium|largeordays=N: fit the chosen task to the requested scope. Default tosmall, meaning one focused local session.focus=<topic>: prefer a focus area without making it the only allowed candidate. Examples:ai-native,easy-start,algorithm,compute,release,ci,docs,python,io,PLAN-122,world_split,dartpy, or a file path.area=<dimension>: alias forfocus=<dimension>.- Any issue, PR, branch, milestone, failing check, file path, or user-stated priority overrides the default dashboard order.
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 · +4 lines d0c5b5e3ceaa
- 3d ago Changed · -4 lines · +9 tokens per session 891a109c4fb7
- 5d ago First seen · 166 lines · 16 tokens per session scan A 2379ace83a58
dart-next is a skill published in the GitHub repository dartsim/dart (1,202 stars, last pushed today), licensed BSD-2-Clause. It adds 25 tokens to every session and 1,728 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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