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/attenlabs/hotato/agents-mdgit clone --depth 1 https://github.com/attenlabs/hotatoWhat 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 | $0.02697 | $0.02697 |
| Opus 5 | $0.01349 | $0.01349 |
| Sonnet 5 | $0.00539 | $0.00539 |
| Haiku 4.5 | $0.00270 | $0.00270 |
Grade A, and why
hotato 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 yesterday.
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 — 231 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md
You are an agent evaluating or wiring up hotato, the local-first AI
engineering platform, to trace, evaluate, test, and gate an AI agent (voice,
chat, or LLM). This file is written to be executed top to bottom. Every command
below is offline, reversible, and needs no account or key. Prefer the CLI's own
contract
(hotato describe --format json) over this prose when they disagree.
The core loop (run this first)
One path, first touch to a CI gate. Each step prints the exact next command, so you can chain the whole loop from stdout alone:
1 hotato start --demo see it catch a failure on two bundled calls
2 hotato investigate ./call.wav score your own two-channel recording
3 hotato investigate label <ref> commit the caught moment as a regression
4 hotato pr create --fixtures <bundle> open the pull request that adds the CI gate
5 hotato contract verify contracts/ the gate re-runs the stored evidence
- Step 1 sweeps the two bundled demo calls, builds one failure contract, and runs one say-do conversation check. It exits 0 because setup finished; the gate command it points at exits 1.
- Step 2 opens a local dual-channel WAV (or pulls one live with
--stack vapi --call-id ID), authenticates its capture origin, runs the input-health gate, and ranks the timing moments. It marks the top-ranked onemost likely failureand prints oneinvestigate labelcommand for it. No intent is inferred; a suspected channel swap refuses the verdict path. - Step 3 writes a content-addressed contract bundle to
contracts/<id>.hotato/from the candidate ref (.hotato/investigate-state.json#N) and the--expect yield|holdlabel you supply. The label is the human decision; hotato measures whether the timing matched it. - Step 4 stages that bundle byte-identical under
tests/hotato/contracts/and opens the PR (dry run by default;--yesruns git and gh). - Step 5 re-measures the stored evidence deterministically. This is the CI gate: exit 0 pass, exit 1 fail.
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.
- yesterday First seen · 231 lines · 2,697 tokens per session scan A b1a2edd8a1a1
hotato AGENTS.md is an instructions file published in the GitHub repository attenlabs/hotato (1 stars, last pushed 4d ago), licensed MIT. It adds 2,697 tokens to every session, about $0.0135 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 2026-08-31.
Other instructions, from other repositories
sayna CLAUDE.md
Instructions for SaynaAI/sayna, covering claude.md, project overview, development commands, feature flags and high-level architecture.
steganography-mcp CLAUDE.md
Claude Code instructions for badchars/steganography-mcp, covering steganography-mcp — steganography analysis mcp server, architecture, key rules, categories (17) and commands.
Paperlab AGENTS.md
Instructions for NourMtir0722/Paperlab, covering paperlab — for coding agents, integrating paperlab into a project, stage mode — paper as architecture, content types and lighting is data, not an enum.
markdy-com copilot-instructions.md
Instructions for HoangYell/markdy-com, a project described as: 🎬 Open-source animated architecture diagrams as code. Diagram-native DSL, 17 layout engines, Web Animations API, and MCP server for AI agents.
diffusers AGENTS.md
AGENTS.md instructions for huggingface/diffusers, a project described as: 🤗 Diffusers: State-of-the-art diffusion models for image, video, and audio generation in PyTorch.
comfy-prompt-studio AGENTS.md
Instructions for yxhpy/comfy-prompt-studio, covering agents.md - ai 代理配置文档, ai 提供商, 1. ollama (默认), 2. gemini and 提示词生成服务.