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Agent skill

Sdf

by autonomous-ai in autonomous-ai/openharness

SDFormat/SDF model and world authoring, validation, and simulator handoff.

MITAuto-check passedGame Development

Install Sdf

skills CLI
$ npx skills add autonomous-ai/openharness --skill sdf -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install autonomous-ai/openharness sdf --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/autonomous-ai/openharness.git skills-src && mkdir -p .claude/skills && cp -r skills-src/store/agents/text-to-cad/skills/sdf .claude/skills/sdf && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
sdf
GitHub stars
1.1k
Token cost
~2.1k tokens
SKILL.md length
947 words
Files
12 (incl. references)
Skills in repo
100
Repo updated
First seen
Licence
MIT

At a glance

SDFormat/SDF model and world authoring, validation, and simulator handoff.

  • Works in 12 steps: Author .sdf XML directly and validate… → Identify the target consumer before… → Decide document kind: model-level SDF,… → …
  • Visual/collision geometry
  • SKILL.md covers Setup, Core rules, Scope and CAD Viewer Handoff, plus 5 more sections
  • Calls python and git

What it does

Sdf is an agent skill from autonomous-ai/openharness. SDFormat/SDF model and world authoring, validation, and simulator handoff. Use for .sdf files, SDFormat XML, models, worlds, links, joints, poses, frames, inertials, visual/collision geometry, mesh URIs, sensors, lights, physics, plugins, includes, Gazebo, static SDF review, or simulator-specific metadata. Do not use for signed-distance-field geometry.

Its SKILL.md is about 2.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 13 other files, including reference files (for example `agents/openai.yaml`, `references/design-ledger.md` and `references/examples.md`).

It sits in Game Development. The repository describes itself as: The ultimate harness for coding agents and beyond. All your agents. All your machines. One command center. Start with code, then follow your curiosity and build across… The licence is MIT.

When your agent uses it

  • Visual/collision geometry
  • Static SDF review
  • Simulator-specific metadata
  • Signed-distance-field geometry

Example prompts

  • “/sdf”

Requirements

  • Python 3

Workflow steps

12 steps, taken from the first numbered list in SKILL.md.

  1. Author .sdf XML directly and validate every created or modified file with cadgen sdf validate before reporting completion.
  2. Identify the target consumer before editing: Gazebo/libsdformat version, another simulator, visualization-only tooling, model package, or…
  3. Decide document kind: model-level SDF, world-level SDF, or model-in-world. Prefer model-level SDF for reusable robot/object exports.
  4. Use SI units unless the target explicitly requires otherwise: meters, kilograms, seconds, radians.
  5. Prefer version="1.12" for new outputs unless the target consumer constrains the version.
  6. Establish the design ledger before writing poses, frames, joint axes, mesh scales, inertials, sensors, or plugins, and keep it as a…
  7. Write relative_to / expressed_in explicitly on every nontrivial pose and axis. Implicit frame defaults are the top SDF failure mode. See…
  8. Do not infer spatial transforms from visual impression alone. Derive poses, axes, scale, mass, inertia, and frame names from upstream…
  9. When the robot already has a URDF, derive the SDF from it instead of re-authoring geometry; see references/interoperability.md.
  10. Regenerate upstream geometry, mesh, robot-description, render, topology, or package assets with their owning workflows before editing SDF…
  11. After authoring, run available checks: bundled validation, optional gz sdf --check, simulator load, joint motion, and plugin/sensor startup.
  12. Report assumptions, skipped checks, unresolved resource paths, and target-specific compatibility risks.

What it can do on your machine

Read from SKILL.md and the folder at commit 50da5db. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • python
    • git

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • github.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Sdf loads about 2.1k tokens when it runs, and up to ~11k if it reads all its reference files. Until then it costs about 90 tokens; SKILL.md has 947 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~90
When it runs · the whole SKILL.md, loaded when a task matches
~2.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~11k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from autonomous-ai/openharness at commit 50da5db, republished under its MIT licence (© autonomous-ai). 947 words, ~2,070 tokens.

Download SKILL.mdSave it as .claude/skills/sdf/SKILL.md (or your agent's skills folder). This skill also uses 11 other files; get the full folder from GitHub.
name
sdf
description
SDFormat/SDF model and world authoring, validation, and simulator handoff. Use for `.sdf` files, SDFormat XML, models, worlds, links, joints, poses, frames, inertials, visual/collision geometry, mesh URIs, sensors, lights, physics, plugins, includes, Gazebo, static SDF review, or simulator-specific metadata. Do not use for signed-distance-field geometry.

SDF

Provenance: maintained in earthtojake/text-to-cad. Use the installed local skill files as the runtime source of truth; the repository link is only for provenance and release review.

Use this skill when the deliverable is an SDFormat document. SDFormat describes simulator and world behavior: models, worlds, frames, poses, links, joints, inertials, visuals, collisions, sensors, lights, physics, plugins, includes, and simulator metadata.

This skill is for SDFormat, not signed-distance-field geometry.

The .sdf file is the source of truth: author and edit the XML directly. There is no gen_sdf() contract.

Setup

This skill's commands are thin entrypoints over the cadgen distribution, which carries the Python build runtime and the JavaScript it executes. Install it once:

bash
python -m pip install -r requirements.txt

Rendering additionally needs a browser, which pip cannot supply:

bash
python -m playwright install chromium

Core rules

  1. Author .sdf XML directly and validate every created or modified file with cadgen sdf validate before reporting completion.
  2. Identify the target consumer before editing: Gazebo/libsdformat version, another simulator, visualization-only tooling, model package, or world handoff.
  3. Decide document kind: model-level SDF, world-level SDF, or model-in-world. Prefer model-level SDF for reusable robot/object exports.
  4. Use SI units unless the target explicitly requires otherwise: meters, kilograms, seconds, radians.
  5. Prefer version="1.12" for new outputs unless the target consumer constrains the version.
  6. Establish the design ledger before writing poses, frames, joint axes, mesh scales, inertials, sensors, or plugins, and keep it as a comment block at the top of the .sdf. Use references/design-ledger.md and references/llm-guardrails.md.
  7. Write relative_to / expressed_in explicitly on every nontrivial pose and axis. Implicit frame defaults are the top SDF failure mode. See references/frame-semantics.md.
  8. Do not infer spatial transforms from visual impression alone. Derive poses, axes, scale, mass, inertia, and frame names from upstream source data, drawings, simulator documentation, measured values, or explicit assumptions. Never freehand computed numbers — use formulas or a throwaway helper script (inertia tensors, unit conversions).
  9. When the robot already has a URDF, derive the SDF from it instead of re-authoring geometry; see references/interoperability.md.
  10. Regenerate upstream geometry, mesh, robot-description, render, topology, or package assets with their owning workflows before editing SDF that references them.
  11. After authoring, run available checks: bundled validation, optional gz sdf --check, simulator load, joint motion, and plugin/sensor startup.
  12. Report assumptions, skipped checks, unresolved resource paths, and target-specific compatibility risks.

Scope

Use this skill for SDFormat outputs. Do not use it for signed-distance-field modeling, raw geometry generation, planning semantics, or to paper over incorrect upstream robot/source data unless the task is explicitly simulator-only.

CAD Viewer Handoff

After completing SDF work that creates or modifies a .sdf, you must ALWAYS hand the explicit file path to $cad-viewer when that skill is installed. $cad-viewer must start CAD Viewer if it is not already running and return link(s) to the relevant created or updated file(s); if $cad-viewer is unavailable or startup fails, report that instead of silently omitting the handoff.

Workflow

  1. Locate the target .sdf and its consumers.
  2. Read or create the design ledger comment block.
  3. Read references/frame-semantics.md before editing any <pose>, <frame>, joint axis, relative_to, expressed_in, nested scope, sensor frame, or plugin frame.
  4. Author the XML directly, following the worked examples in references/examples.md.
  5. Validate the explicit target with cadgen sdf validate; treat bundled validation as a guardrail, not simulator proof.
  6. Run target-consumer smoke tests when available (references/smoke-tests.md).
  7. Hand the file to $cad-viewer. Static rendering does not execute SDF plugins or read file-authored motion metadata.
  8. Report checks run, checks skipped, and assumptions.
Show full SKILL.md (373 more words)Show less

Commands

Run with the project or workspace Python environment. Treat python in examples as an interpreter placeholder; if bare python is unavailable, substitute python3, a project virtualenv interpreter, or the configured interpreter path. The validator uses only the Python standard library.

bash
cadgen sdf validate path/to/model.sdf
cadgen sdf validate path/to/model.sdf --strict
cadgen sdf validate path/to/model.sdf --json
cadgen sdf snapshot path/to/model.sdf review.png

The validator checks document shape, name scopes, pose/frame graphs, joints, geometry, mesh URIs, inertials, sensors, and plugins, and prints its findings plus a summary. One run validates ONE file: --strict treats warnings as failures and --json emits the machine-readable findings document. It exits nonzero if the target fails.

Optional external checking:

bash
cadgen sdf validate path/to/model.sdf --gz-check auto
cadgen sdf validate path/to/model.sdf --gz-check required
cadgen sdf validate path/to/model.sdf --gz-check never

gz sdf --check is optional target-consumer validation. It should be reported as skipped when unavailable unless explicitly required.

Required report shape

When finishing an SDF task, include a compact report:

text
Validated: path/to/model.sdf
Checks run:
- bundled SDF validation: passed
- gz sdf --check: skipped, gz not installed
- simulator load: skipped, target simulator unavailable
- viewer handoff: `$cad-viewer` link returned
Assumptions:
- Assumed mesh units are meters.
- Assumed lidar frame is coincident with lidar_link.
Risks:
- Camera plugin filename was not verified in the target simulator environment.

Snapshot Tool

cadgen sdf snapshot renders the robot to a PNG still, using the same shared CLI and headless browser runtime every rendering skill uses — so a snapshot matches what the CAD Viewer shows.

bash
cadgen sdf snapshot path/to/robot.sdf review.png

It accepts .sdf only (a format door, same TARGET [OUT] grammar as the rest). Pose the robot with --joint-values — {joint: degrees} JSON, joints you do not name staying at the rest pose (the "jointValues" job field is the same thing in a packet). Robots are authored in metres and are framed on the robot scene scale automatically.

Theme settings live under one --theme, mirroring the viewer's Theme tab. The default theme is snapshot — Workbench Light with the ground grid, origin axis and shadows removed, because in a still image those read as geometry. There is no --display: display settings (mode, clip, exploded, edges) are CAD topology settings, and a robot carries none.

Link meshes are resolved relative to the description, so they must be present: an unhydrated Git LFS pointer fails as "No link mesh loaded for robot". Run git lfs checkout <mesh dir> first.

The grammar is cadgen sdf snapshot TARGET [OUT] [flags], the same one every format door uses. Use cadgen sdf snapshot --help for the complete current interface — the flags a robot cannot act on are absent from it, not refused by it.

References

  • SDF workflow: references/sdf-workflow.md
  • Worked examples (golden skeletons): references/examples.md
  • LLM guardrails: references/llm-guardrails.md
  • Design ledger: references/design-ledger.md
  • Frame semantics: references/frame-semantics.md
  • Validation scope: references/validation.md
  • Smoke tests: references/smoke-tests.md
  • Interoperability notes (URDF-derived SDF, meshes, Gazebo): references/interoperability.md

© autonomous-ai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 11 other files (references) in store/agents/text-to-cad/skills/sdf of autonomous-ai/openharness.

  • SKILL.md
  • LICENSE
  • agents/openai.yaml
  • references/design-ledger.md
  • references/examples.md
  • references/frame-semantics.md
  • references/interoperability.md
  • references/llm-guardrails.md
  • references/sdf-workflow.md
  • references/smoke-tests.md
  • references/validation.md
  • requirements.txt

Open the folder on GitHubat commit 50da5db

Compare with similar skills

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Categories

Questions about Sdf

What does Sdf do?

SDFormat/SDF model and world authoring, validation, and simulator handoff. Sdf is an agent skill from autonomous-ai/openharness. SDFormat/SDF model and world authoring, validation, and simulator handoff.

When should I use Sdf?

Sdf fits situations like: visual/collision geometry; static SDF review; simulator-specific metadata; signed-distance-field geometry.

How do I install Sdf in Claude Code?

Run `npx skills add autonomous-ai/openharness --skill sdf -a claude-code`. Or copy the skill folder (store/agents/text-to-cad/skills/sdf in autonomous-ai/openharness) into .claude/skills/sdf in your project. Claude Code loads it when a task matches its description.

How do I install Sdf in Codex?

Run `npx skills add autonomous-ai/openharness --skill sdf -a codex`. Or copy the skill folder (store/agents/text-to-cad/skills/sdf in autonomous-ai/openharness) into .agents/skills/sdf in your project. Codex loads it when a task matches its description.

Can I use Sdf in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add autonomous-ai/openharness --skill sdf -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/sdf, .gemini/skills/sdf, .github/skills/sdf and .opencode/skills/sdf in your project.

What does Sdf need to run?

Going by SKILL.md and its folder, Sdf needs the command-line tools its instructions call (python and git). Our summary lists: Python 3.

Does Sdf access the network?

SKILL.md names 1 domain. As links in the text: github.com. This is read from the text; nothing was executed.

Is Sdf safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Sdf use?

Sdf is published under the MIT licence (from the LICENSE file in the skill folder). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Sdf use?

About 2.1k tokens (SKILL.md is roughly 8.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 8.7k tokens, read only when the agent opens those files.

What are the alternatives to Sdf?

Skills that share tags, products or a category with Sdf: Image to Three.js Model (img2threejs/img2threejs, 18k stars), Web Clone (Jane-xiaoer/claude-skill-web-clone, 1k stars), Threejs Game Director (majidmanzarpour/threejs-game-skills, 2.4k stars) and Game Asset Generator (htdt/godogen, 7.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Sdf?

autonomous-ai (a GitHub organization) maintains it in autonomous-ai/openharness, which has 1,149 GitHub stars. The repository holds 100 skills in this directory. The repository was last updated on October 8, 2026.

Source: autonomous-ai/openharness on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.