grasping-direct-ik

grasping-direct-ik is a skill for Claude Code from graph-robots/open-robot-skills. It costs 139 tokens per session (2,292 once invoked), scanned A, original, Apache-2.0.

A robotic grasping procedure that turns and positions a gripper above an object, then moves straight down before closing it. It is intended for simple, uncluttered scenes without a collision-aware trajectory planner.

In plain words
What is it for?
Opening the gripper, finding a top-down grasp, aligning it, descending, and closing it to pick up an object when obstacles are not a concern.
Why use it?
It avoids the failure that can happen when the gripper rotates and descends at the same time, while keeping the motion straightforward.

Skill for Claude Code

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

Part of the open-robot-skills plugin — 39 skills shipped together

Good fit Opening the gripper, finding a top-down grasp, aligning it, descending, and closing it to pick up an object when obstacles are not a concern.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/graph-robots/open-robot-skills/grasping-direct-ik
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.

Any agent
npx skills add graph-robots/open-robot-skills --skill grasping-direct-ik
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 39 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 grasping-direct-ik

README.md
[![agentmods](https://agentmods.dev/badge/skills/graph-robots/open-robot-skills/grasping-direct-ik/github.svg)](https://agentmods.dev/skills/graph-robots/open-robot-skills/grasping-direct-ik)
Your own site
<a href="https://agentmods.dev/skills/graph-robots/open-robot-skills/grasping-direct-ik"><img src="https://agentmods.dev/badge/skills/graph-robots/open-robot-skills/grasping-direct-ik/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for grasping-direct-ik

Your own site · 80×15
<a href="https://agentmods.dev/skills/graph-robots/open-robot-skills/grasping-direct-ik"><img src="https://agentmods.dev/badge/skills/graph-robots/open-robot-skills/grasping-direct-ik.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 139 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,292 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 $0.00139 $0.02292
Opus 5 $0.00069 $0.01146
Sonnet 5 $0.00028 $0.00458
Haiku 4.5 $0.00014 $0.00229

Measured yesterday against content hash 752d563a12cb, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

grasping-direct-ik 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.

The scan reads SKILL.md. This mod also ships 3 executable files (scripts/compute_align_pose.py, scripts/plan_to_pose.py, scripts/refine_top_down_grasp.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.

skills/grasping-direct-ik/SKILL.md · 183 lines

How it starts

The opening of the file, as written. The whole thing — 183 lines — stays where its author put it; the contents beside it link to each section on GitHub.

grasping-direct-ik

Direct-IK grasp: rotate the gripper to grasp orientation at a safe height above the target, then descend straight down, then close. No trajectory planner — works on platforms where CuRobo is not deployed, or in uncluttered scenes where planning is overkill.

Nothing here is written for one hand. The grasp rotation is composed by the connector's robot.grasp_frame from the hand's measured approach and closing axes, the fingertip floor and the hover clearance come from robot.describe_gripper / robot.describe_workspace, so the same subgraph grasps the same way on a hand that closes along tool-local x and on one that closes along tool-local y.

When to use

  • The curobo tool bundle is not deployed (no collision-aware planner available).
  • The scene is uncluttered enough that a straight-line approach is safe.

When NOT to use

  • Cluttered scenes where the arm must thread between obstacles. Prefer grasping-with-planner if available.

The subgraph state machine the agent generates should look like (7 states):

open → compute_grasp → refine_grasp → compute_align → rotate_align → descend → close → grasped

(grasped is the success-marker noop from sg.add_exit("grasped"), with an edge to END.)

State details:

  1. opentype: tool, tool: "robot.open_gripper", inputs: { settle_steps: 40 }.
  2. compute_grasptype: tool, tool: "geometry.top_down_grasp_candidates", inputs: { obb: Ref("in.target_obb") }.
  3. refine_grasptype: script, file scripts/<sg>/refine_top_down_grasp.py (from this bundle's canonical_scripts). Inputs: grasp_pose = Ref("compute_grasp.candidates.poses.0"), target_obb = Ref("in.target_obb"). Returns grasp_pose: the candidate with its rotation rebuilt so this hand's closing axis lies across the OBB's short horizontal axis (robot.describe_gripper + robot.grasp_frame(approach=-z, close_heading_deg)), and its Z raised to the fingertip floor (support_z + finger.reach_m + finger.clearance_m) when the hand states its finger envelope. Keep this state: the candidate fan is world-aligned and a thin object's centred grasp jams the jaws on the table without the floor.
  4. compute_aligntype: script, file scripts/<sg>/compute_align_pose.py. Inputs: grasp_pose = Ref("refine_grasp.grasp_pose"), target_obb = Ref("in.target_obb"), optionally clearance (a literal in metres). Returns align_pose at the grasp XY and rotation, with z = max(obb_top, grasp_z) + clearance. Omit clearance (or pass 0) and the script asks robot.describe_workspace for align_clearance_m — the hand's own envelope above the fingertips. Pin it (e.g. 0.12) when the held object is what needs the room, such as a long tool that will hang below the fingertips on the way up.
  5. rotate_aligntype: tool, tool: "robot.go_to_pose", inputs: { pose: Ref("compute_align.align_pose") }.
  6. descendtype: tool, tool: "robot.go_to_pose", inputs: { pose: Ref("refine_grasp.grasp_pose") } — the same pose compute_align was given, so hover and grasp share one rotation.
  7. closetype: tool, tool: "robot.close_gripper", inputs: { settle_steps: 60 }. Edge directly from close to the grasped success marker; the subgraph's on_error: "failed" catches any raise from earlier steps. Whether the gripper actually closed on the object is checked by the target_held postcondition checkpoint (see ## Checkpoints), NOT by a re-check-and-raise node (none such exists).

Read the full file on GitHub · 183 lines

Files

What ships with it

4 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. yesterday Changed · +52 lines · +54 tokens per session 752d563a12cb
  2. 10d ago First seen · 131 lines · 85 tokens per session scan A e6f5c168f48a

Subscribe to this mod's changes

grasping-direct-ik is a skill published in the GitHub repository graph-robots/open-robot-skills (41 stars, last pushed yesterday), licensed Apache-2.0. It adds 139 tokens to every session and 2,292 once invoked, about $0.0007 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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