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/phenomenoner/ai-agent-thinkroom/agents-mdgit clone --depth 1 https://github.com/phenomenoner/ai-agent-thinkroomWrote 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/phenomenoner/ai-agent-thinkroom/agents-md)<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>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 | $0.00362 | $0.00362 |
| Opus 5 | $0.00181 | $0.00181 |
| Sonnet 5 | $0.00072 | $0.00072 |
| Haiku 4.5 | $0.00036 | $0.00036 |
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.
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=Trueor 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.
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.
- 5d ago First seen · 36 lines · 362 tokens per session scan A 6ef3cb3a98e3
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.
Other instructions, from other repositories
codeflow AGENTS.md
AGENTS.md instructions for xiaojiou176-open/codeflow, covering agents guide, mission, canonical read order, key commands and generated governance context.
codeflow CLAUDE.md
Claude Code instructions for xiaojiou176-open/codeflow, covering claude.md, read first, working rules, key commands and generated governance context.
geolens CLAUDE.md
Claude Code instructions for geolens-io/geolens: This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
flyto-core CLAUDE.md
Claude Code instructions for flytohub/flyto-core, covering claude notes, cross-agent handoff and shared code intelligence.
briefloop AGENTS.md
AGENTS.md instructions for Stahl-G/briefloop, covering agents.md, purpose, instruction scope, environment separation and context mode.
dev-challenge CLAUDE.md
Claude Code instructions for micheltlutz/dev-challenge, covering claude.md, the one rule to internalise, slash commands, subagents and skills.