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 commands/horizonrobotics/embodiedgen/gen_indoorgit clone --depth 1 https://github.com/HorizonRobotics/EmbodiedGenWrote 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/commands/horizonrobotics/embodiedgen/gen_indoor)<a href="https://agentmods.dev/commands/horizonrobotics/embodiedgen/gen_indoor"><img src="https://agentmods.dev/badge/commands/horizonrobotics/embodiedgen/gen_indoor.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 5.1 | $0.00019 | $0.00181 |
| Opus 5 | $0.00010 | $0.00090 |
| Sonnet 5 | $0.00004 | $0.00036 |
| Haiku 4.5 | $0.00002 | $0.00018 |
Grade A, and why
gen_indoor 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 6d 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.
What it actually says
Gen Indoor Skill Command
Route the user's request to the EmbodiedGen room creation workflow.
Workflow
Step 1: Interpret the request
Use $ARGUMENTS if provided. If it is empty, ask for room type, output root, and whether export is needed.
Step 2: Load the skill
Use skill: "embodiedgen:room-creator".
Step 3: Execute the workflow
Follow the skill and build the correct python -m embodied_gen.scripts.room_gen.gen_room or room-cli command.
Step 4: Deliver
Return:
- The exact command used
- The output directory
- Runtime and export-stage notes
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.
- 6d ago First seen · 30 lines · 19 tokens per session scan A be4031642eb1
gen_indoor is a command published in the GitHub repository HorizonRobotics/EmbodiedGen (655 stars, last pushed 12d ago), licensed Apache-2.0. It adds 19 tokens to every session and 181 once invoked, about $0.0001 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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