geometry

geometry is a skill for Claude Code from graph-robots/open-robot-skills. It costs 64 tokens per session (1,246 once invoked), scanned A, original, Apache-2.0.

A CPU-based toolkit for turning camera masks, depth, and calibration data into 3D points, object boxes, grasp poses, and collision scenes.

In plain words
What is it for?
Use it to prepare perception data for robot picking, generate top-down or front grasp options, and build planner collision worlds from RGB-D camera frames.
Why use it?
It removes the need to write the geometry calculations needed before a robot planner can reason about objects and obstacles.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the open-robot-skills plugin — 15 skills shipped together

Install

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.

agentmods
npx agentmods add skills/graph-robots/open-robot-skills/geometry
Any agent
npx skills add graph-robots/open-robot-skills --skill geometry
Clone the repo
git clone --depth 1 https://github.com/graph-robots/open-robot-skills

Made for: Claude Code.

Or install open-robot-skills, the plugin that ships this one along with the rest of its 15 skills.

Wrote 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.

agentmods badge for geometry

README.md
[![agentmods](https://agentmods.dev/badge/skills/graph-robots/open-robot-skills/geometry.svg)](https://agentmods.dev/skills/graph-robots/open-robot-skills/geometry)
Your own site
<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>
Per session 64 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,246 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 6d ago against content hash ee555d67747f, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

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.

The scan reads SKILL.md. This mod also ships 2 executable files (_impl.py, tools.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

tools/geometry/SKILL.md · 91 lines

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_candidates for tabletop pick (feed the full list to curobo.plan_to_grasp_poses as a goalset), front_grasp_from_obb for horizontal interactions (drawer/door handles).
  • Building the collision world for the planner: build_world_config with the target's mask in object_masks so the planner can ignore_obstacle_names it.

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 extent is HALF-extents (gap.types convention, same as the proto). compute_obb is 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 /app sandbox file.)
  • top_down_grasp_candidates default z_offset=-0.04: fingertip 4 cm below the OBB top. With z_offset=0.0 the 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_points keeps only depths in [0.015, 20.0] m (HyRL bounds); invalid/zero-depth pixels are dropped.
  • filter_noise returns the ORIGINAL cloud unchanged when DBSCAN labels everything noise (defensive fallback, mirrors HyRL).
  • build_world_config: table removal only runs when table_z_threshold != 0 (typical -0.01); robot-point exclusion is Franka-only (simplified DH FK) and skips non-7-DOF joint states; prefer explicit object_masks over the target_obb projection fallback — masks are pixel-accurate, the OBB projection is a corner-AABB approximation inflated by 2 cm.
  • top_down_grasp_from_obb yaw is NOT derived from the OBB — fingers may close across the wide axis; use the candidate fan when orientation matters.

Read the full file on GitHub · 91 lines

Files

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.

Changes

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.

  1. 6d ago First seen · 91 lines · 64 tokens per session scan A ee555d67747f

Subscribe to this mod's changes

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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