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.
npx agentmods add instructions/dylanroscover/embody/agents-mdgit clone --depth 1 https://github.com/dylanroscover/EmbodyWrote 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/dylanroscover/embody/agents-md)<a href="https://agentmods.dev/instructions/dylanroscover/embody/agents-md"><img src="https://agentmods.dev/badge/instructions/dylanroscover/embody/agents-md.svg" alt="Measured on agentmods" 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 | $0.01999 | $0.01999 |
| Opus 5 | $0.01000 | $0.01000 |
| Sonnet 5 | $0.00400 | $0.00400 |
| Haiku 4.5 | $0.00200 | $0.00200 |
Grade A, and why
Embody 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 4d 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 — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Embody + Envoy - AI Instructions
This project uses Embody (TouchDesigner externalization) and Envoy (MCP server for AI coding tools).
- Embody externalizes TouchDesigner operators to version-controlled files (
.py,.tox,.tdxn,.json, etc.) - Envoy provides MCP tools for AI assistants to inspect and modify the live TD network
- Use the
Configure Fortoggles on the Embody COMP's Envoy page to regenerate these files for your AI tool (more than one client can be on at once)
Note: For Claude Code users, a
CLAUDE.mdwith modular.claude/rules/and.claude/skills/is also generated. ThisAGENTS.mdis the universal fallback read by Codex, Cursor, GitHub Copilot, Windsurf, Gemini, and others.
Critical Rules
- Prefer the externalized network file for reading TDXN-externalized COMPs - these are YAML on disk with complete network structure (operators, parameters, connections, positions, flags, DAT content, annotations). Reading them directly is faster than MCP round-trips. Never glob for an extension: Embody writes
.tdxn, and keeps writing.tdnfor any COMP externalized before Embody 6.1 - both are read and round-tripped forever, so a project can hold a mix. Letexternalizations.tsvorget_externalizationsname the exact file instead. The strategy column value istdnfor all of them, unchanged by the rename - searching for atdxnstrategy finds nothing and will make you wrongly conclude the project has no externalized networks. To edit: modify the file on disk, then callimport_networkvia MCP with the COMP path, the parsed network, andclear_first=Trueto reload it in TD. Use MCP when you need live runtime state (evaluated expressions, cook errors) or for non-TDXN operators. - Use Envoy MCP tools for live TD state and non-TDXN operators - never say "I can't access that binary file." For operators not externalized as TDXN, use MCP tools to inspect and modify them.
- Do NOT assume network paths - use
query_networkon/to discover the actual root structure. - Default new COMPs to Embody's container -
execute_pythonwithresult = op.Embody.parent().pathreturns the same home every run. Build inui.panes.current.owner.pathonly when you have deliberately navigated into a content network, and never treat the bare root/as that home. - Never edit
externalizations.tsvdirectly - managed exclusively by Embody's tracking system. - Always use forward slashes in file paths.
- Check for errors and warnings after each group of operator creations --
get_op_errorswithrecurse=trueonce the group is wired/positioned. - Batch repetitive MCP operations -- never make 3+ individual calls to the same tool. Use
batch_operationsfor repeated positioning, connecting, parameter, or flag edits; useexecute_pythonfor computed or looped logic. - Thread boundary: MCP server worker thread must never import TD modules. All TD access goes through
_execute_in_td().
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.
- 4d ago First seen · 118 lines · 1,999 tokens per session scan A 870433759c0e
Embody AGENTS.md is an instructions file published in the GitHub repository dylanroscover/Embody (169 stars, last pushed today), licensed MIT. It adds 1,999 tokens to every session, about $0.0100 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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