text-to-cad is a collection of agent workflows for creating, examining, finding, preparing, and transferring CAD, engineering, manufacturing, and robot-description files. It is for agents working with mechanical design, fabrication, simulation, and robotics projects. Catalogue skills, instructions, and a plugin implement these workflows.
Borrowing it
Nothing to install: this file belongs to earthtojake/text-to-cad. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/earthtojake/text-to-cad/main/AGENTS.mdgit clone --depth 1 https://github.com/earthtojake/text-to-cadWrote 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.
[](https://agentmods.dev/instructions/earthtojake/text-to-cad/agents-md)<a href="https://agentmods.dev/instructions/earthtojake/text-to-cad/agents-md"><img src="https://agentmods.dev/badge/instructions/earthtojake/text-to-cad/agents-md/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.
<a href="https://agentmods.dev/instructions/earthtojake/text-to-cad/agents-md"><img src="https://agentmods.dev/badge/instructions/earthtojake/text-to-cad/agents-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.04617 | $0.04617 |
| Opus 5.5 | $0.01847 | $0.01847 |
| Sonnet 5.5 | $0.00923 | $0.00923 |
| Haiku 4.5 | $0.00462 | $0.00462 |
Grade A, and why
text-to-cad AGENTS.md 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 16d ago.
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.
How it starts
The opening of the file, as written. The whole thing — 300 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md
Installing text-to-cad rather than developing it? Follow the Install section of README.md.
This repo is a workbench for CAD-related agent skills. Treat skills/ as the
product and models/ as the shared fixture/artifact area.
Branch First
main is the only branch you develop on: the source tree, and what releases
are cut from. Branch from main and open PRs against main; never push it
directly. There is no development symlink layout — every path in the tree is the
real file. Installers follow latest, and claude.ai's directory
claude-plugin: the plugin alone, one commit per release,
written only by Publish Release once the release is on PyPI (see below); never
commit to them. main stays an installable plugin too: every manifest and MCP
config lives at its root.
Release Workflow
A pull request that changes VERSION is a release, and merging it releases it.
Never bump during normal development work, and never pick the bump yourself: if
a request to make or ship a release does not name patch, minor, major, or an
exact version, ask which one. A release pull request comes one of two ways:
- Its own pull request, for a release of what
mainalready has — what "make a release" means unless the user names a pull request to carry it: dispatchPrepare Release(gh workflow run release-prepare.yml --ref main -f bump=<patch|minor|major>, or-f set_version=X.Y.Z). It opensrelease/X.Y.Zwith the bump and never merges it. - On the pull request that should carry it: on its branch,
scripts/release/bump-version.sh <patch|minor|major|X.Y.Z>setsVERSIONpastmain's and stamps the derived metadata and everycadgen==pin with it.
Merge a release pull request only on the user's explicit word, once its checks pass: the merge is the release.
Testruns every job for aVERSIONchange, so a release is tested whole. Its Version Check vets the bump: a branch of this repository only (a fork cannot release), a version pastmain's and the latest tag, every stamp in step.Publish Release(release-publish.yml) runs on the merge. It finds theTestrun that recorded the merged tree as tested in full (else it runs everyTestjob on it first), builds the plugin ZIP, the bundle and thecadgenwheel and sdist, installs and exercises the wheel, uploads the pages' source maps to PostHog and the wheel to PyPI, and waits until PyPI's index lists it. Only then does it commit the plugin alone onto the branches installers follow (scripts/release/plugin_branch.py):latest, which every install command names, andclaude-plugin, which claude.ai's directory tracks. After them it deploys the docs site and tags (v<VERSION>; releases before 0.5.0 are bare0.4.xtags) and GitHub-Releases the release commit, with the wheel and sdist that went to PyPI attached as release assets, plus the plugin ZIP that a person uploads to OpenAI's plugin portal, which has no API.
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.
- 16d ago Changed · +2 lines · +43 tokens per session 5e74eca7edeb
- 18d ago Changed · +1 lines · +13 tokens per session 8f856d22f460
- 19d ago Changed · +30 lines · +488 tokens per session 0e4f2f126656
- 20d ago Changed · +16 lines · +272 tokens per session c39cac68bf3c
- 21d ago Changed 5e1455ab047a
- 22d ago Changed · +2 lines · +35 tokens per session e90902c3b41c
- 22d ago Changed · +25 tokens per session 84cfdbed25de
- 24d ago Changed · +2 lines · +55 tokens per session 273279ac9258
- 26d ago Changed · +3 tokens per session da27fa7d8d79
- 28d ago First seen · 247 lines · 3,683 tokens per session scan A 557a4e9e8274
text-to-cad AGENTS.md is an instructions file published in the GitHub repository earthtojake/text-to-cad (22,986 stars, last pushed today), licensed MIT. It adds 4,617 tokens to every session, about $0.0185 per session on Opus 5.5. 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.
Other instructions, from other repositories
vscode buildNext.instructions.md
Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).
next.js AGENTS.md
AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, cache eligibility and rollout and applying the notice (cutover).
langchain AGENTS.md
AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.
vscode copilot-instructions.md
Copilot instructions for microsoft/vscode, covering vs code copilot instructions, project overview, root folders, core architecture (src/ folder) and built-in extensions (extensions/ folder).
deepseek-harness AGENTS.md
AGENTS.md instructions for deepseek-ai/deepseek-harness, covering agents.md, pre-stable apis and released session data, repository layout, commands and host sandbox failures.