brain-kit AGENTS.md

Repository instructions for a learning kit in which an AI agent collects information from videos, articles, research papers, and code repositories and turns it into cited, reusable knowledge.

In plain words
What is it for?
Use them to ingest a source, inspect relevant material, compare supporting evidence, summarize findings, and file knowledge for later use.
Why use it?
They provide a repeatable way to capture and check what was learned instead of letting useful information disappear after one session.

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/0xchamin/brain-kit/agents-md
Clone the repo
git clone --depth 1 https://github.com/0xchamin/brain-kit

Made for: Codex, OpenCode.

Per session 9,708 This file is loaded in full into every session.
When invoked 9,708 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 $0.09708 $0.09708
Opus 5 $0.04854 $0.04854
Sonnet 5 $0.01942 $0.01942
Haiku 4.5 $0.00971 $0.00971

Measured yesterday against content hash a7f46ce443b8, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

brain-kit 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.

AGENTS.md · 501 lines

How it starts

The opening of the file, as written. The whole thing — 501 lines — stays where its author put it; the contents beside it link to each section on GitHub.

AGENTS.md - Brain (agent-driven compounding learning kit)

Behavioral contract for any agent harness (Copilot CLI, Claude Code, Cursor, ...) working in this kit. Read this first, then the relevant sources/<id>/ and brain/. Brain turns things you learn from - YouTube videos, blog posts, research papers, and code repositories - into durable, cited, compounding knowledge.

The agent is the engine. There is no application. You (the agent) run the pipeline: capture the source, view visual candidates (your view tool is the vision model) or trace code (grep / code-intel tools), judge corroboration, distill, and file the knowledge. This is a kit for learning from sources - including repos - not for building on top of them. See prd.md for the full design.

Repo-specific defaults (this clone)

Default Value Used for
Owner chamin pre-fills the SOURCE.md Owner row (always this person)
Source naming YYMMDD_slug big-endian date sorts chronologically; _ divides date/name, - between words (e.g. 260724_mcp-security-talk)
Env .venv in this folder yt-dlp, faster-whisper, imagehash, pillow (see requirements.txt); ffmpeg is a system binary (macOS: brew install ffmpeg; Windows: winget install Gyan.FFmpeg); git for cloning code repos; gh (GitHub CLI) recommended for code sources (license, commit SHA, orient-before-clone) - optional
Seed topics agents, mcp, skills, rag, agent-security, inferencing live under brain/topics/; seeds, not a whitelist - the set is open (see "Scope: topics are open")
Repo clones sources/<id>/repo/ (git-ignored) clone-per-source, snapshot pinned by commit SHA in SOURCE.md
Kit scripts tools/ingest.py (mechanical toolbox), validate.py (contract type checker) the only two frozen scripts; everything else is assembled per source

Scope: topics are open

The goal is to learn state-of-the-art AI broadly - agents, MCP, skills, RAG, agent-security, inferencing, and new topics as they emerge. The seed topics are a starting point, not a closed list. When a source teaches a recognizable, reusable area the brain does not yet cover, capture it as a new topic (see the compound step) - do not force it into an ill-fitting existing note. Domain guardrail: stay within AI / ML / agentic engineering; if a source is clearly off-domain, flag it and ask rather than silently ingesting. The architect persona owns the create-vs-merge call and keeps the taxonomy from ballooning.

Read the full file on GitHub · 501 lines

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. yesterday First seen · 501 lines · 9,708 tokens per session scan A a7f46ce443b8

Subscribe to this mod's changes

brain-kit AGENTS.md is an instructions file published in the GitHub repository 0xchamin/brain-kit (2 stars, last pushed 1mo ago), licensed MIT. It adds 9,708 tokens to every session, about $0.0485 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.

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