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 skills/graph-robots/open-robot-skills/geometrynpx skills add graph-robots/open-robot-skills --skill geometrygit clone --depth 1 https://github.com/graph-robots/open-robot-skillsWrote 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/graph-robots/open-robot-skills/geometry)<a href="https://agentmods.dev/skills/graph-robots/open-robot-skills/geometry"><img src="https://agentmods.dev/badge/skills/graph-robots/open-robot-skills/geometry.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 | $0.00064 | $0.01246 |
| Opus 5 | $0.00032 | $0.00623 |
| Sonnet 5 | $0.00013 | $0.00249 |
| Haiku 4.5 | $0.00006 | $0.00125 |
Grade A, and why
geometry 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 6d 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 — 91 lines — stays where its author put it; the contents beside it link to each section on GitHub.
geometry
Pure-math perception/planning geometry as in-process typed tools, from
mask back-projection through OBB fitting to grasp-candidate generation,
plus the two scalar helpers (geometry.iou, geometry.pose_distance).
Fully CPU — no model weights, no GPU.
When to use
- Turning a segmentation mask + depth + camera calibration into world-frame
points (
mask_to_world_points) and an object OBB (filter_and_compute_obb). - Deriving grasp poses from an OBB:
top_down_grasp_candidatesfor tabletop pick (feed the full list tocurobo.plan_to_grasp_posesas a goalset),front_grasp_from_obbfor horizontal interactions (drawer/door handles). - Building the collision world for the planner:
build_world_configwith the target's mask inobject_masksso the planner canignore_obstacle_namesit.
Install
uv sync --extra geometry # open3d + scikit-learn (cv2/scipy come with gap core)
# (pip: pip install -e ".[geometry]")
The module imports lazily — the bundle loads (and the light tools work) without the extra; only OBB fitting, DBSCAN filtering and world reconstruction need open3d/sklearn/cv2.
Gotchas (carried over from the service)
- OBB
extentis HALF-extents (gap.types convention, same as the proto).compute_obbis upright-only: rotation is around world Z (no 3D tilt), and extents use the 2nd/98th percentile of points, not strict min/max. - Single-camera clouds are 2.5D: only camera-facing surfaces are observed, so
OBB centers carry a few cm of depth bias on opaque objects. (The service's
rehearsal-sandbox ground-truth snap that compensated for this in-container
was deliberately NOT ported — it depended on a
/appsandbox file.) top_down_grasp_candidatesdefaultz_offset=-0.04: fingertip 4 cm below the OBB top. Withz_offset=0.0the fingers close above the object (silent empty grip). Grasp Z is clamped to -0.05 m (table-clearance floor; LIBERO table top is at world z=0).mask_to_world_pointskeeps only depths in [0.015, 20.0] m (HyRL bounds); invalid/zero-depth pixels are dropped.filter_noisereturns the ORIGINAL cloud unchanged when DBSCAN labels everything noise (defensive fallback, mirrors HyRL).build_world_config: table removal only runs whentable_z_threshold != 0(typical -0.01); robot-point exclusion is Franka-only (simplified DH FK) and skips non-7-DOF joint states; prefer explicitobject_masksover thetarget_obbprojection fallback — masks are pixel-accurate, the OBB projection is a corner-AABB approximation inflated by 2 cm.top_down_grasp_from_obbyaw is NOT derived from the OBB — fingers may close across the wide axis; use the candidate fan when orientation matters.
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 6d ago First seen · 91 lines · 64 tokens per session scan A ee555d67747f
geometry is a skill published in the GitHub repository graph-robots/open-robot-skills (39 stars, last pushed yesterday), licensed Apache-2.0. It adds 64 tokens to every session and 1,246 once invoked, about $0.0003 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 2026-08-30.
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