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-perfgit 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-perf)<a href="https://agentmods.dev/skills/dartsim/dart/dart-perf"><img src="https://agentmods.dev/badge/skills/dartsim/dart/dart-perf/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-perf"><img src="https://agentmods.dev/badge/skills/dartsim/dart/dart-perf.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 | $0.00025 | $0.00333 |
| Opus 5.5 | $0.00010 | $0.00133 |
| Sonnet 5.5 | $0.00005 | $0.00067 |
| Haiku 4.5 | $0.00003 | $0.00033 |
Grade A, and why
dart-perf 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.
What it actually says
DART Performance
Required Reading
@docs/ai/principles.md @docs/onboarding/profiling.md
Follow docs/onboarding/profiling.md § "Performance Methodology", the shared
standard for agents and contributors. Reproduce the user's scenario and state
deviations before optimizing. Choose exact-count A/B gates for regressions and
controlled wall-time repeats for speed claims; pair every claim with behavior
guards. Attribute engine and integration costs by phase before choosing a
change, then verify the full workload and downstream compatibility.
For exact-count commands and policy, use the owner's "Revision Comparisons"
section. For visible simulation changes, load dart-verify-sim and follow the
linked verification route. For publication, follow the linked evidence flow;
uploading requires explicit approval.
Output
Leave reproducible evidence: named DART baseline and candidate revisions, scenario/deviations, setup and timed windows, count-gate results, wall-time median/min-max when measured, behavior results, attribution, and compatibility limitations. Report the user effect first with plots using the linked "PR Descriptions" guidance. Public comparisons use DART revisions only.
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 First seen · 36 lines · 25 tokens per session scan A 8df5c6a81e7a
dart-perf is a skill published in the GitHub repository dartsim/dart (1,217 stars, last pushed today), licensed BSD-2-Clause. It adds 25 tokens to every session and 333 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 2026-10-09.
Other skills, from other repositories
systematic-debugging
4-phase root cause debugging: understand bugs before fixing.
experimental-code-coverage-local-debugger
Runs code coverage locally via Universal Test Runner (UTR) or helper scripts, mimicking LUCI trybots. Activate when CQ tryjobs fail or underreport coverage, to test local GN/recipe repairs before uploading, or to debug hermetic crashes.
cli-e2e
Write, modify, or debug Docker-based Composio CLI end-to-end tests under ts/e2e-tests/cli, including binary invocation, fixture isolation, output assertions, and package manifests. Use for CLI E2E test suites only; use cli-command for CLI source implementation.
adversarial-reviewer
Stress-test a code change for concrete correctness defects, unsafe assumptions, and failure modes. Use when the user explicitly asks for an adversarial review, a deep bug hunt, or to tear a change apart. Do not use for ordinary review, style feedback, or general improvement suggestions.
work
Deliver one maintainer-approved EmDash issue, choosing the bug-fix path for a defect and the direct implementation path for an enhancement or task.
ios-simulator
Verify and debug native, React Native, Expo, or Flutter apps on an iOS Simulator with agent-device. Use when an agent needs to launch an app, inspect its live UI, tap, type, scroll, validate a code change, collect failure evidence, or reproduce a workflow on an iPhone or iPad Simulator.