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-resume/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-resume)<a href="https://agentmods.dev/skills/dartsim/dart/dart-resume"><img src="https://agentmods.dev/badge/skills/dartsim/dart/dart-resume/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-resume"><img src="https://agentmods.dev/badge/skills/dartsim/dart/dart-resume.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 | $0.00014 | $0.01661 |
| Opus 5 | $0.00007 | $0.00830 |
| Sonnet 5 | $0.00003 | $0.00332 |
| Haiku 4.5 | $0.00001 | $0.00166 |
Grade A, and why
dart-resume 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 — 168 lines — stays where its author put it; the contents beside it link to each section on GitHub.
dart-resume
Use this skill in Codex to run the DART dart-resume 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-resume <arguments> - Codex:
$dart-resume <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
Resume unfinished work: $ARGUMENTS
Objective
dart-resume is a completion-oriented task manager, not a status lookup or a
single-slice helper by default. Resume the named or reconstructed work, build or
refresh the execution plan, track progress in the repo-owned task surface, split
independent work into verifiable packets, use subagents/sub-sessions only when
the user explicitly requested delegation and the current AI surface permits it,
verify every result, and keep going until the whole task is complete or a real
blocker or approval boundary remains.
Decisions must be evidence-based. Before choosing between meaningful options, first improve or define the verification/debugging method so it can catch false positives and false negatives. Use repository inspection, focused tests, benchmarks, A/B comparisons, GUI or visual evidence, logs, and external resource searches as needed to decide from evidence instead of preference.
For a docs/dev_tasks/<task> target, full completion means all feasible task
work is finished, durable decisions and deferred work are promoted, and the
temporary dev-task folder is removed in the completing change. Do not stop after
one successful slice unless the user explicitly requested a limited mode.
Argument Handling
Use $ARGUMENTS to identify the target, explicit scope limits, and execution
modifiers. Interpret arguments in this 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.
- 2d ago Changed 74d918b6f7a7
- 4d ago Changed · -44 lines e07f481e874d
- 6d ago Changed · -3 lines b9d827d85c9d
- 8d ago First seen · 215 lines · 14 tokens per session scan A cc508e6592b2
dart-resume is a skill published in the GitHub repository dartsim/dart (1,204 stars, last pushed 3d ago), licensed BSD-2-Clause. It adds 14 tokens to every session and 1,661 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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