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Cap Tool Usage

Organization
NVlabs
cap-tool-usage

Localize objects and compose low-level CAP MCP actions using observations, Cartesian or synchronized motion, joint targets, and single-arm gripper control.

Overview

PublisherNVlabs
RepositoryASPIRE
Skill namecap-tool-usage
Stars
838
Forks
69
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by NVlabs on GitHub. Read the source before you install it.

Installation

Install the Cap Tool Usage AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/NVlabs/ASPIRE/tree/main/.agents/skills/cap-tool-usage
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/NVlabs/ASPIRE.git /tmp/ASPIRE
mkdir -p .claude/skills
cp -r /tmp/ASPIRE/.agents/skills/cap-tool-usage .claude/skills/cap-tool-usage
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Cap Tool Usage in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Cap Tool Usage on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Cap Tool Usage is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

CAP Tool Usage

Follow AGENTS.md. Use the native mcp__cap__* interface attached to the ASPIRE CAP session. Keep that session as the only environment and camera owner; do not create another robot or camera runtime during a physical task.

For a plainly short-horizon task, execute the shortest dependency-ordered MCP sequence immediately. Skip source inspection, a full planning report, and redundant checks.

ASPIRE action contract

Native MCP schemas are authoritative. The low-level action tools are:

ToolTarget
cap_move_to_posetarget_pose, with arm="left" or "right"
cap_move_synchronizedtargets={"left": pose, "right": pose} for jointly planned motion
cap_move_to_jointstarget containing six joint angles in radians, with arm
cap_set_gripperposition from 0 closed to 1 open, with arm

An absolute pose includes position: [x, y, z] in metres, frame, and exactly one of rpy_deg: [roll, pitch, yaw] or quaternion_wxyz: [w, x, y, z]. RPY uses degrees and fixed-axis XYZ: R = Rz(yaw) @ Ry(pitch) @ Rx(roll) in the declared frame. Returned poses use WXYZ quaternions. Read measured arm poses with cap_get_robot_state.

For Cartesian or synchronized pose motion with cuRobo configured, select strategy={"ik_solver":"mink","trajectory_planner":"curobo"}. Never disable collision checking to force a failed plan through. Do not use direct joint motion as implicit IK or to bypass a failed collision-aware Cartesian plan.

For a layer turn, derive successive absolute pose targets from the measured cube axis and EEF pose. An offset EEF needs both its position and orientation rotated about that axis. Verify contact and actual sticker motion from returned evidence. Use separate cap_set_gripper calls for intended grip changes. Prefer synchronized motion only when both targets are grounded and neither depends on the other's feedback.

Object-approach tool chain

Use the MCP schemas as authoritative. The expected discoverable tools are:

cap_localize_object → cap_move_to_pose → optional observe

cap_localize_object performs one-shot SAM3 plus shared RGB-D localization. cap_move_to_pose performs its own IK and motion planning. Observation and localization are read-only; only action tools actuate the robot.

Read current arm state when a target depends on the measured EEF pose. Form a full pose from grounded object geometry and the intended grasp orientation; an object's estimated orientation is not automatically a suitable gripper orientation.

Use the runtime's available camera names; their names and count are not fixed. observe and get_job accept an optional cameras list. World-frame localization requires a calibrated RGB-D view. Check the installed pads before applying the cube guide's soft UMI contact guidance.

Pose evidence

  1. Discover the current tool schemas from CAP MCP; do not infer arguments from this summary.
  2. When object-relative metric geometry is needed, call cap_localize_object in a calibrated RGB-D view. Reuse that result when its identity, frame, geometry, and diagnostics are valid. Repeat only after concrete invalid or contradictory evidence, or after motion changed the scene; do not sample twice merely to estimate stability. Reject unreliable estimates, not the entire task: another view or a bounded correction grounded in measured EEF state and visible alignment may remain feasible.
  3. Refresh grounding that motion invalidated. A new localization call is needed when the next action depends on object geometry that fresh returned evidence does not establish.

Shared rules

  • A job receipt is not a terminal result; handle execution and completion as described in AGENTS.md. Process terminal status, errors, state, and images before the next dependent action.
  • Use successful measured postconditions as evidence of the metric effects they actually verify. Continue directly when they resolve the next decision. Call observe when a required relation is absent, stale, ambiguous, or contradicted; never use it as a routine acknowledgement of successful motion.
  • Action feedback includes images by default. Reuse fresh returned evidence; numeric motion success alone does not verify grasp, placement, or task completion.
  • When IK succeeds and motion executes, large measured tracking errors near an object, the table, or the other gripper can indicate contact. Use returned views to distinguish obstruction from free-space tracking error; do not push farther or loosen tolerance to get past suspected contact. An IK rejection by itself is not evidence of contact.
  • For a supporting arm, retain the intended hold reference across calls; do not silently replace it with each drifted measurement. Plan corrections from the achieved state with contact and clearance checked. Distinguish a changed reference from failure to track an unchanged one before attributing drift to hardware or declaring the task blocked.
  • Perception tools acquire their own fresh observation. Do not call observe first unless visual inspection is needed to select or disambiguate the subject or target.
  • Ground geometry from a fresh official observation before a metric action.
  • Require evidence sufficient for the next action's precision and contact risk, not a uniformly precise scene model. An endpoint miss measures that attempt, not the best achievable accuracy; missing camera extrinsics prevent world-frame localization, not use of its RGB view for alignment.
  • Prefer one collision-aware motion whose returned terminal state verifies the requested effect.
  • Preserve the current gripper through metric motion unless a change is intended. Gripper commands are actions, not acquisition probes: use them only when the gripper-state transition is the grounded subgoal, and verify support before releasing a load.
  • Use cap_set_gripper for one arm. Position 0.0 is fully closed, 1.0 is fully open, and intermediate values are proportional. One successful call completes the transition; do not repeat it when the returned gripper state already verifies the target.

Frequently asked questions

What does the Cap Tool Usage AI skill do?

Localize objects and compose low-level CAP MCP actions using observations, Cartesian or synchronized motion, joint targets, and single-arm gripper control.

Why use Cap Tool Usage on TypingMind?

Because you install it once and use it with any model. Cap Tool Usage is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Cap Tool Usage in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/NVlabs/ASPIRE/tree/main/.agents/skills/cap-tool-usage. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Cap Tool Usage?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Cap Tool Usage?

As many as you like. As long as a model supports skills, you can use Cap Tool Usage with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Cap Tool Usage AI skill free?

Yes. It is published on GitHub by NVlabs under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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