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sdf

earthtojake/text-to-cad

Generate, validate, and hand off SDFormat models and worlds for Gazebo and other simulators.

What is sdf?

This skill creates and edits SDFormat (SDF) documents for robot models, worlds, and simulator configuration. Use it when you need to generate `.sdf` files, work with Python `gen_sdf()` sources, or prepare models for simulator handoff with proper frames, joints, inertials, sensors, and physics.

  • Generate SDFormat models and worlds from Python source files using `gen_sdf()` functions
  • Validate SDF structure and compatibility with target simulators like Gazebo
  • Define robot structure: links, joints, poses, frames, inertials, and spatial transforms
  • Configure visual and collision geometry, mesh URIs, and sensor/light placement
  • Manage simulator-specific metadata, physics parameters, plugins, and includes
  • Hand off completed SDF files to CAD Viewer for inspection

How to install sdf

npx skills add https://github.com/earthtojake/text-to-cad --skill sdf
Prerequisites
  • Python environment with the skill installed via `npx skills add`
  • Upstream geometry, mesh, and robot-description assets finalized before SDF generation
  • Optional: Gazebo/libsdformat installed for `gz sdf --check` validation
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How to use sdf

  1. 1.Locate or create the Python `gen_sdf()` source file and identify the target `.sdf` output path
  2. 2.Read the design ledger and frame-semantics reference to establish spatial assumptions
  3. 3.Edit the generator source (not the generated XML) to define models, links, joints, poses, and simulator metadata
  4. 4.Run `python scripts/sdf path/to/source.py` or `python scripts/sdf path/to/source.py -o path/to/output.sdf` to generate the SDF file
  5. 5.Run bundled validation and optional `gz sdf --check` to verify structure and simulator compatibility
  6. 6.Hand off the generated `.sdf` file path to `$cad-viewer` for visual inspection
  7. 7.Report checks run, assumptions made, and any unresolved resource paths or compatibility risks

Use cases

Good for
  • Export a robot description from Python to a reusable SDFormat model for Gazebo simulation
  • Create a world file that positions multiple models and configures physics and lighting
  • Validate that joint axes, frame transforms, and inertial properties are correctly specified before simulator load
  • Generate sensor and plugin configurations for a robot model with proper frame references
  • Prepare a model package with correct mesh paths and SDFormat version for a specific simulator target
Who it's for
  • Roboticists and simulation engineers working with Gazebo or libsdformat
  • Developers building robot description pipelines that output to SDFormat
  • Anyone preparing models for physics simulation or multi-robot world setup

sdf FAQ

When should I use this skill vs. editing SDF XML directly?

Treat the Python `gen_sdf()` source as the source of truth. Edit the generator source, not the generated XML, to avoid losing changes on regeneration. Use this skill for reproducible, maintainable SDF pipelines.

What units should I use in SDF files?

Use SI units by default: meters for distance, kilograms for mass, seconds for time, and radians for angles. Only deviate if your target simulator explicitly requires different units.

How do I ensure poses and frames are correct?

Read `references/frame-semantics.md` before editing any pose, frame, joint axis, or relative_to attribute. Derive transforms from upstream source data, drawings, or measured values—never infer from visual impression alone.

What should I include in the final report?

Report the generated file path, which checks were run (bundled validation, gz sdf --check, simulator load) and which were skipped, any assumptions about mesh units or frame placement, and compatibility risks for the target simulator.

Can I use this skill for signed-distance-field geometry?

No. This skill is for SDFormat documents only, not signed-distance-field (SDF) geometry. Use other geometry generation tools for implicit surface modeling.

Full instructions (SKILL.md)

Source of truth, from earthtojake/text-to-cad.


name: sdf description: SDFormat/SDF model and world generation, validation, and simulator handoff. Use for .sdf files, SDFormat XML, Python gen_sdf() sources, 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 or a Python gen_sdf() source. 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.

Core rules

  1. Treat the Python file defining gen_sdf() as source of truth. Treat configured .sdf files as generated artifacts unless the user explicitly asks for direct XML editing.
  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. Use references/design-ledger.md and references/llm-guardrails.md.
  7. 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.
  8. Prefer helper functions and named constants over large XML string literals. Hidden numbers are a common SDF failure mode.
  9. Generate only explicit targets with scripts/sdf or the repository's existing SDF launcher. Do not run directory-wide generation.
  10. Regenerate upstream geometry, mesh, robot-description, render, topology, or package assets with their owning workflows before regenerating SDF that references them.
  11. After generation, 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 and generators. 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 gen_sdf() source and intended .sdf output.
  2. Read or create the design ledger.
  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. Edit the generator source, not generated XML.
  5. Use optional builder helpers when they make the generated structure clearer; raw ElementTree is still allowed.
  6. Regenerate the explicit target.
  7. Treat bundled validation as a guardrail, not simulator proof.
  8. Run target-consumer smoke tests when available.
  9. Report checks run, checks skipped, and assumptions. Static rendering does not execute SDF plugins or read file-authored motion metadata.

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.

python scripts/sdf path/to/source.py
python scripts/sdf path/to/source.py -o path/to/output.sdf
python scripts/sdf path/to/a.py=out/a.sdf path/to/b.py=out/b.sdf

Plain Python targets write sibling .sdf files beside their sources. -o / --output is valid only with one plain target. SOURCE.py=OUTPUT.sdf supports custom multi-target destinations.

If the runtime supports optional external checking:

python scripts/sdf path/to/source.py --gz-check auto
python scripts/sdf path/to/source.py --gz-check required
python scripts/sdf path/to/source.py --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:

Generated: path/to/model.sdf from path/to/model.py
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.

References

  • Generation command: references/gen-sdf.md
  • Generator contract: references/generator-contract.md
  • SDF workflow: references/sdf-workflow.md
  • Builder helpers: references/builder-helpers.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: references/interoperability.md
  • Examples: references/examples.md
  • Runtime notes and current limitations: references/implementation-notes.md