ai-agent-thinkroom AGENTS.md

ai-agent-thinkroom AGENTS.md is an instructions file for Codex, OpenCode from phenomenoner/ai-agent-thinkroom. It costs 362 tokens per session, scanned A, original, MIT.

A repository instruction file that tells a coding agent how to work on the project, including design rules, safety rules, testing, and release checks.

In plain words
What is it for?
Use it to guide implementation, testing, dependency checks, type checks, and release builds in the ai-agent-thinkroom project.
Why use it?
It gives the agent clear boundaries and completion checks, reducing the risk of unsafe code, skipped requirements, or unverified changes.

Instructions file for CodexOpenCode

Install

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.

agentmods
npx agentmods add instructions/phenomenoner/ai-agent-thinkroom/agents-md
Clone the repo
git clone --depth 1 https://github.com/phenomenoner/ai-agent-thinkroom

Made for: Codex, OpenCode.

Wrote 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.

agentmods badge for ai-agent-thinkroom AGENTS.md

README.md
[![agentmods](https://agentmods.dev/badge/instructions/phenomenoner/ai-agent-thinkroom/agents-md.svg)](https://agentmods.dev/instructions/phenomenoner/ai-agent-thinkroom/agents-md)
Your own site
<a href="https://agentmods.dev/instructions/phenomenoner/ai-agent-thinkroom/agents-md"><img src="https://agentmods.dev/badge/instructions/phenomenoner/ai-agent-thinkroom/agents-md.svg" alt="Measured on agentmods" height="20"></a>
Per session 362 This file is loaded in full into every session.
When invoked 362 The same file — it is already loaded in full.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00362 $0.00362
Opus 5 $0.00181 $0.00181
Sonnet 5 $0.00072 $0.00072
Haiku 4.5 $0.00036 $0.00036

Measured 5d ago against content hash 6ef3cb3a98e3, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

ai-agent-thinkroom 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 5d 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.

AGENTS.md · 36 lines

What it actually says

Repository instructions

Authority

Implement docs/specification.md. Preserve the product semantics in thinkroom_ai_think_tank_product_concept.md. Do not silently reduce P0 scope or release gates.

Engineering method

  • Use strict RED → GREEN → REFACTOR for behavior changes.
  • Keep domain and application layers independent of FastAPI, SQLite, subprocess, provider SDKs, CLI, and MCP.
  • Prefer small cohesive modules and explicit typed ports over framework magic.
  • Treat model output as untrusted input: validate schemas, bound retries, and preserve evidence verification status.
  • Never use shell=True or interpolate user input into commands.
  • Do not add distributed infrastructure without an observed need.
  • Keep production defaults loopback-only and fail closed on unsafe public binding while authentication is absent.

Required gates

The implementation is not complete until these pass from a clean checkout:

uv lock --check
uv sync --locked --all-extras --dev
uv run ruff format --check .
uv run ruff check .
uv run mypy src
uv run pytest
python3 scripts/build_release.py --out-dir /absolute/external/empty/dist

Add package-install and native production-process smoke tests/scripts. A production wheel release must include requirements-production.txt, uv.lock, and the flat asset verify_locked_runtime.py; install hash-locked dependencies first and the wheel with --no-deps. Docker is an operator-owned reference integration, not part of the v0.2 native release claim. Docker availability or smoke status does not gate the native release.

Claim discipline

A scripted backend proves orchestration, not model quality. A real provider smoke proves integration, not correctness of the research. SQLite release support is single-instance only.

Changes

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.

  1. 5d ago First seen · 36 lines · 362 tokens per session scan A 6ef3cb3a98e3

Subscribe to this mod's changes

ai-agent-thinkroom AGENTS.md is an instructions file published in the GitHub repository phenomenoner/ai-agent-thinkroom (2 stars, last pushed yesterday), licensed MIT. It adds 362 tokens to every session, about $0.0018 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.