Borrowing it
Nothing to install: this file belongs to Yinzhanqing/embodied-eval-automation. 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/Yinzhanqing/embodied-eval-automation/main/AGENTS.mdgit clone --depth 1 https://github.com/Yinzhanqing/embodied-eval-automationWrote 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/yinzhanqing/embodied-eval-automation/agents-md)<a href="https://agentmods.dev/instructions/yinzhanqing/embodied-eval-automation/agents-md"><img src="https://agentmods.dev/badge/instructions/yinzhanqing/embodied-eval-automation/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/yinzhanqing/embodied-eval-automation/agents-md"><img src="https://agentmods.dev/badge/instructions/yinzhanqing/embodied-eval-automation/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 | $0.00179 | $0.00179 |
| Opus 5.5 | $0.00072 | $0.00072 |
| Sonnet 5.5 | $0.00036 | $0.00036 |
| Haiku 4.5 | $0.00018 | $0.00018 |
Grade A, and why
embodied-eval-automation 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 2mo 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
Repository agent guidance
This repository contains a public, provider-neutral Agent Skill and skills-only Codex plugin.
Rules
- Keep the installable skill under
skills/embodied-eval-automation/. - Keep
SKILL.mdconcise and link every detailed reference directly from it. - Never commit credentials, private hosts, institutional identifiers, model weights, datasets, generated episodes, videos, caches, or run logs.
- Use standard-library Python for bundled validation scripts unless a dependency is essential and documented.
- Treat installation, upload, paid compute, permission changes, deletion, Git writes, and external messaging as separate approval boundaries.
- Do not hardcode a user’s operating system, drive letter, home path, server provider, model, or benchmark.
- Record newly added repository files in
log/file_description.md. - Run the repository validator and unit tests before handoff.
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.
- 2mo ago First seen · 15 lines · 179 tokens per session scan A 8f5132ebe22a
embodied-eval-automation AGENTS.md is an instructions file published in the GitHub repository Yinzhanqing/embodied-eval-automation (6 stars, last pushed 3mo ago), licensed MIT. It adds 179 tokens to every session, about $0.0007 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 2026-08-31.
embodied-eval-automation AGENTS.md compared with instructions from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| embodied-eval-automation AGENTS.mdYinzhanqing/embodied-eval-automation | Instructions file | 6 | 3mo ago | MIT | A | 179 | Codex, OpenCode |
| vscode buildNext.instructions.mdmicrosoft/vscode | Instructions file | 195k | yesterday | MIT | A | 9,719 | GitHub Copilot |
| next.js AGENTS.mdvercel/next.js | Instructions file | 143k | yesterday | MIT | A | 7,461 | Codex, OpenCode |
| langchain AGENTS.mdlangchain-ai/langchain | Instructions file | 149k | yesterday | MIT | A | 4,680 | Codex, OpenCode |
| vscode oss-third-party-notices.instructions.mdmicrosoft/vscode | Instructions file | 195k | yesterday | MIT | A | 5,516 | GitHub Copilot |
| vscode copilot-instructions.mdmicrosoft/vscode | Instructions file | 195k | yesterday | MIT | A | 3,093 | GitHub Copilot |
| deepseek-harness AGENTS.mddeepseek-ai/deepseek-harness | Instructions file | 274k | 2d ago | MIT | A | 4,313 | Codex, OpenCode |