Install
$ agentstack add skill-graph-robots-open-robot-skills-geometry ✓ scanned · ✓ verified — works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
About
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 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_configwith 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
extentis 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_candidatesdefaultz_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_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 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_obbyaw is NOT derived from the OBB — fingers may
close across the wide axis; use the candidate fan when orientation matters.
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: graph-robots
- Source: graph-robots/open-robot-skills
- License: Apache-2.0
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.