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 agentmods add commands/dartsim/dart/dart-plan-updategit 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/commands/dartsim/dart/dart-plan-update)<a href="https://agentmods.dev/commands/dartsim/dart/dart-plan-update"><img src="https://agentmods.dev/badge/commands/dartsim/dart/dart-plan-update.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 | $0.00008 | $0.00676 |
| Opus 5 | $0.00004 | $0.00338 |
| Sonnet 5 | $0.00002 | $0.00135 |
| Haiku 4.5 | $0.00001 | $0.00068 |
Grade A, and why
dart-plan-update 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 yesterday.
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 — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Discuss or update DART living plans: $ARGUMENTS
Required Reading
@AGENTS.md @docs/ai/principles.md @docs/ai/north-star.md @docs/plans/README.md @docs/plans/dashboard.md @docs/plans/north-star-roadmap.md @docs/ai/verification.md
Workflow
- Classify the request:
- discussion-only: compare options, priority, scope, or sequencing;
- plan edit: revise
docs/plans/**or related indexes; - task derivation: turn a plan item into a bounded implementation or docs task.
- Inspect current evidence before changing plan state. Use repo docs, code,
tests, CI evidence, issue/PR state, benchmark data, or explicit maintainer
direction.
- For solver/paper implementation plans, hold the plan to
docs/ai/verification.md§ "Research Paper Implementation Evidence", record the completed slice and the next missing paper-parity gap, and keep the corpus matrix (tests,py-demos, visual artifacts, benchmark JSON, CPU reference comparisons, GPU parity) explicit about missing rows.
- For solver/paper implementation plans, hold the plan to
- Keep the plan manageable:
- revise an existing initiative before adding a duplicate;
- use stable initiative IDs when renaming, splitting, consolidating, or parking work;
- keep
docs/plans/dashboard.mdas the single source of truth for priority, status, horizon, dimension, next step, and gate. - when deriving packets or dev-task work, include the DART specification
intake from
docs/ai/orchestration.md: value, scope, non-goals, assumptions/open decisions, acceptance evidence, gates, and dependencies. Use owner-localDecision neededblocks for consequential ambiguity instead of silent defaults.
- For discussion-only requests, present the tradeoff and proposed plan delta; do not edit unless the user asks for an edit or the request already implies one.
- For plan edits, update
docs/plans/dashboard.mdfor operating state, the detailed numbered initiative file or external owner document for rationale and workstreams, anddocs/plans/north-star-roadmap.mdonly for strategic framing. - If the plan item becomes implementation work, route to
/dart-new-taskin Claude/OpenCode or$dart-new-taskin Codex, and usedocs/dev_tasks/README.mdwhen it is multi-session or needs design tracking. - Verify with
docs/ai/verification.md: use the docs-only gate for plan-only docs, and the AI docs/adapters gate set when AI docs, workflow sources, or generated adapters change. - Do not perform GitHub or remote mutations without explicit maintainer/user approval.
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.
- yesterday Changed · -6 lines 64019c38d554
- 3d ago First seen · 73 lines · 8 tokens per session scan A 0bdf7c034e0e
dart-plan-update is a command published in the GitHub repository dartsim/dart (1,201 stars, last pushed today), licensed BSD-2-Clause. It adds 8 tokens to every session and 676 once invoked, about $0.0000 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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