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/.claude/commands/dart-model-upgrade.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/commands/dartsim/dart/dart-model-upgrade)<a href="https://agentmods.dev/commands/dartsim/dart/dart-model-upgrade"><img src="https://agentmods.dev/badge/commands/dartsim/dart/dart-model-upgrade.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 | $0.00035 | $0.02083 |
| Opus 5 | $0.00017 | $0.01042 |
| Sonnet 5 | $0.00007 | $0.00417 |
| Haiku 4.5 | $0.00003 | $0.00208 |
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 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 — 149 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Improve DART's AI harness for: $ARGUMENTS
Objective
A named model triggers a whole-harness audit: skills, instructions, docs,
context/state ownership, tools, agents, hooks, and verification, including this
workflow — review both content and structure using the audit contract in
docs/ai/components.md; updating a model-routing entry is not the whole task.
Later model/effort preferences constrain execution, not this coverage, unless
the user explicitly narrows the audit. Prefer coherent evidence-backed changes:
substantial restructuring is welcome when comparisons show a benefit, while
more machinery or fewer words alone do not establish improvement.
Required Reading
Read the compact intake first: @AGENTS.md @docs/ai/principles.md @docs/ai/components.md
Then load the relevant sections at the phase that needs them; audit each surface without loading every referenced document in full:
- Target/control:
docs/ai/README.md§ "Model Routing" and the target tool's section indocs/onboarding/ai-tools.md. - Structure/discovery: relevant rows of
docs/ai/workflows.md, owner indexes indocs/README.md, anddocs/AGENTS.mdplusdocs/information-architecture.mdbefore documentation changes. - Project state: current state in
docs/ai/north-star.md,docs/plans/dashboard.md, and one representative active plan/handoff;docs/dev_tasks/README.mdanddocs/ai/orchestration.mdown continuation and phase-specific authorization. Search long handoffs for current state before reading history. - Comparison/gates:
docs/ai/verification.mdanddocs/onboarding/agent-sim-verification.mdwithdart-verify-sim. - Tracking/closeout:
docs/dev_tasks/README.md; load changelog policy only at closeout. Usedocs/plans/README.mdonly when changing plan state.
Workflow
- Normalize target, boundary, and success. Preserve the named model,
reasoning modes, tool version, and branch. Default to
applyfor local improvements; honor explicitaudit-only, discussion-only, and tool Plan Mode restrictions. State the boundary, permitted model/effort set, branch, and existing authorization. Do not substitute targets or pin the project.audit-onlymeans no tracked edits, regeneration, dev-task creation, auto-fixing lint, commits, pushes, or external mutations.applypermits local implementation; GitHub mutations still need explicit authorization. Reuse supplied decisions; ask only about unresolved consequential choices. - Capture the unchanged control. Record git state, installed versions,
pixi run ai-doctor --json, authored words/files and declared-reading sizes, skill metadata, model/config references, agent inheritance, hooks, scenarios, owner/discovery paths, freshness advisories, and focused read-only gates. Inspect current state anddocs/dev_tasks/*/RESUME.mdhandoffs without importing their historical logs by default. Preserve an unchanged snapshot outside the changing checkout; keep audit notes and expected answers out of evaluation lanes. In multi-sessionapply, create the usual dev-task home. - Refresh primary guidance. Fetch current official model, prompting, migration, configuration, skills, agents, and hook guidance for the target. Record URLs, retrieval date, and installed-version evidence. Treat remembered limits and repository assumptions as hypotheses. Identify behavior changes that call for new evaluation cases, not generic prompt additions.
- Audit content and structure; form hypotheses. Cover every surface in
docs/ai/components.md's harness audit contract, including the workflow itself. Classify each finding: preserve (intentional and supported), update (stale/incorrect), remove/consolidate (duplicate or harmful), or add (a distinct missing responsibility/failure boundary). For each proposed change name the observed failure or cost, its owner, the candidate change, the comparison that can reject it, and preservation gates. Check conflicting stops/approvals, discovery gaps, overloading, stale state, repeated facts, and verification that does not cover the actual claim. Report removed/merged content and retained owners; distinguish authored reductions from generated copies, formatting, and deferred loading. Justify net additions and preserved duplication. Do not finish with routing/version edits alone unless other surfaces have evidence-backed preserve verdicts. - Compare one variable at a time. Run fresh non-interactive sessions with the same task, tool permissions, physics sources, and input artifacts. Keep model, effort, prompt/docs changes, and delegation separate. User restrictions bound all lanes and children. Use an older model or lower effort only when permitted; otherwise compare the target with unchanged/proposed instructions at each authorized setting. Verify recorded parent/child model and effort, supply runner settings when the client does not expose them, and inspect transcripts for answer leakage, outside reads, and unexpected mutations. Record correctness, missing constraints, pauses, context reads, tokens, available cost, turns, and wall time. Structural checks and smaller prompts alone do not prove model quality; if no behavioral runner is available, report structural evidence and the exact limitation instead.
- Exercise DART physics and failure boundaries. Rebuild stale simulation
artifacts before comparing. Route every target through
dart-verify-sim: seed static overlap hidden by a render, an unstated dynamic rest/tolerance criterion, and poor framing offered as trusted. Establish a text oracle (metrics, collision/scene/trajectory comparison, or focused behavioral test) shown to detect the defect; corroborate it with assessed headless captures and only claim-tied debug layers. Require native semantic image inspection, repair/rejection of bad views, text/image disagreement handling, and an explicit statement of what images do not prove. If rendering or image review is unavailable, exercise the text path andverification-bundlewhere possible, record the exact limitation, and make no visual-quality claim. Also use fresh direct, indirect, incomplete, non-trigger, and edge requests; test current-state/resume discovery, supplied decisions, authorization carry-forward, unapproved actions, and unavailable requested settings. A bare invocation of this workflow must discover the whole-harness audit without extra coaching. Inaudit-only, report coverage gaps without edits. - Implement and retest supported changes in
applyonly. Keep outcome, domain constraints, permissions, evidence, and stop conditions explicit. Improve owner placement and progressive disclosure; preserve safety and public paths through structural changes. Edit.claude/commands/and.claude/skills/sources; runpixi run sync-ai-commands, never hand-edit generated adapters. Keep model names indocs/ai/README.md§ "Model Routing" and tested-version evidence, not in generic procedures. Followdocs/ai/orchestration.mdfor model/effort restrictions and phase-scoped delegation. Re-run affected comparisons after meaningful fixes; reject or revise candidates that lose required behavior or lack sufficient evidence. - Verify and review the final state. Run
pixi run check-ai-infra,pixi run test-ai-infra, and relevant docs/AI gates fromdocs/ai/verification.md, includingpixi run check-docs-policyfor durable context changes. Usepixi run exercise-agent-scenariosseparately when scenarios changed or diagnostics are needed; the aggregate already checks them. Preserve runner-probe invariants, model-pin boundaries, generated parity, and instruction discovery.audit-onlyuses read-only lint such aspixi run check-lint;applyrunspixi run lintbefore any commit. Complete the principle audit and two clean role-separated reviews on the post-fix state. Label unavailable behavioral, cross-tool, or hosted evidence; hosted rate limits do not require waiting after local review converges. - Close out by mode and branch.
audit-onlystops with findings, recommendations, limitations, proposed gates, and apply/adapt/omit advice. Inapply, make the changelog decision, promote durable conclusions to their owners, and remove the completed dev-task folder. Report achieved outcomes separately from remaining work. Shared infrastructure lands on DART 7 first; after an authorized merge, inspectrelease-6.20from its own base and record apply/adapt/omit. If parallel release work was explicitly requested, keep branches independent and re-audit final main before release publication. Never copy main-only paths into the smaller release catalog.
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 · -47 lines · +3 tokens per session f1eb41fecc9e
- 3d ago Changed · +6 lines 90a30bbf06ca
- 5d ago First seen · 190 lines · 32 tokens per session scan A 4e3645c4fc9c
dart-model-upgrade is a command published in the GitHub repository dartsim/dart (1,201 stars, last pushed yesterday), licensed BSD-2-Clause. It adds 35 tokens to every session and 2,083 once invoked, about $0.0002 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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