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dart-verify-sim
DART Verify Sim: text-first and visual checks for 3D scenes and physics (metrics, scene dump, trajectories, headless render, image verdict/golden)
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
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DART Verify Sim: text-first and visual checks for 3D scenes and physics (metrics, scene dump, trajectories, headless render, image verdict/golden)
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
DART Analyze: analyze repository evidence without editing
DART Architecture: the DART 7 multi-physics, multi-solver, multi-backend simulation pipeline and where each abstraction is owned
DART Audit Agent Compliance: audit and fix gaps when agents miss or cannot discover documented rules
DART Backport PR: backport a merged main PR to a release branch
DART Benchmark Packet: author or refresh a benchmark evidence packet for an owning plan
DART Branch Cleanup: analyze or clean stale repository branches
| name | dart-verify-sim |
| description | DART Verify Sim: text-first and visual checks for 3D scenes and physics (metrics, scene dump, trajectories, headless render, image verdict/golden) |
Load this skill when verifying that a DART 3D scene or physics simulation is correct — implementing, debugging, benchmarking, or reviewing dynamics, collision, contact, or GUI output. DART's domains need 3D understanding that language models lack natively; this tooling makes it checkable without a GUI.
Lead with text, corroborate with images. Measured A/B evidence: per-step metrics and trajectories detect nearly all seeded physics defects; a rendered image alone misses static geometry defects (penetration, interpenetration). Decide correctness from text; use images for scene comprehension and gross dynamic failures.
Use this skill for any task whose claim depends on 3D structure or behavior: model/scene loading, dynamics, collision/contact/constraints, simulation stepping, GUI/rendering, or visual examples. First run a text oracle (metrics, scene diff, trajectory/contact comparison, or focused behavioral test), then corroborate it end to end with an assessed headless view and only the debug layers needed by the claim. If rendering is unavailable or genuinely irrelevant, record why and name the replacement evidence; never treat an image as the sole correctness oracle.
docs/onboarding/agent-sim-verification.md
— the durable guide. docs/ai/verification.md owns the gate policy;
docs/onboarding/profiling.md owns text-first profiling.
Text (primary):
world.compute_step_metrics() — energy/momentum/penetration/contacts/residualdartpy.dump_scene_json(world) / dump_scene_text(world) — "what is in this
world?" (glTF/USD-flavored hierarchy + flat index)pixi run scene-diff — structural JSON verdict for intended-vs-actual scene
dumpspixi run trajectory-record / pixi run trajectory-compare — per-body TSV +
contact JSONL; bit-exact or tolerance diff with first-divergenceVisual (corroboration):
dart.gui.render(world, camera=None, size=(w, h), debug=(...layers...)) →
headless image with optional world-derived debug layers (grid,
world_frame, body_frames, coms, inertia_boxes, collision_bounds,
velocities, contacts, labels; trajectories additionally requires a
sampled dart.gui.TrajectoryTracker via debug_scene_for_world);
dart.gui.render_annotated(...) composites label text; .png_bytes() for
notebooks; dart.gui.orbit_camera(...) / look_at(...)dart.gui.assess_view(world, camera, size, focus=...) → ViewReport with
issues (cropped/too-far/too-close/occluded/ambiguous);
dart.gui.select_viewpoints(...) picks deterministic best views;
dart.gui.frame_body/frame_region reframe onto a subject. Assess first;
fix flagged views before capturing evidence.--view {three-quarter|front|side|top},
--camera-azimuth/-elevation/-distance/-target, --turntable N, --fitpixi run py-demo-capture — headless PNG/MP4 capture from Pythonpixi run agent-capture — deterministic evidence harness: auto/explicit
cameras, debug layers, stills/turntable/motion video, reproducible sidecarpixi run image-compose — side-by-side / blend / diff-heatmap compositespixi run evidence-select — claim-driven artifact selection with recorded
rationale; pixi run evidence-publish — PR "Visual verification" section
with GitHub-hosted media (manual placeholders by default; gh-release
upload only with --yes + maintainer approval)pixi run image-verdict / image-golden / image-sheet — JSON verdict,
golden diff, contact sheet (contrast is report-only; --require-contrast to
gate)pixi run image-ab-study — blind-judge detection deltas for single-view,
multi-view, turntable, and annotated capturespixi run image-ab-round2 — prepare a blinded round-2 packet and score
completed judge observationsOpt-in:
pixi run render-golden-gate — opt-in golden gate (backend-specific golden,
curated locally with -- --update; not default CI)pixi run rerun-trajectory — rerun.io inspection (opt-in; graceful when
rerun-sdk is absent)pixi run verification-bundle — package text evidence plus still/grid images
for a provider-neutral VLM or reviewer callDefault capture for agent review: one ~1280 px frame, UI hidden, 3/4 view; add a 9-frame grid for motion. Keep images as corroboration, never the sole oracle for static geometry.
Equivalent capture over OpenSceneGraph: dart::gui::osg::setUpOffscreenViewer
/ captureOffscreen + dartpy bindings, pixi run capture (needs a real X
server or Xvfb), the ported image tooling, and pixi run bm-boxes-headless for
rendering-free determinism checksums.