How do I install the Claude Mega Brain skill?

Run npx skillmds@latest add guhcostan/claude-mega-brain in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Claude Mega Brain safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Claude Mega Brain?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Claude Mega Brain free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Claude Mega Brain?

guhcostan (@guhcostan) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Claude Mega Brain by guhcostan — Agent Skill

Name: Claude Mega Brain
Author: guhcostan

Claude Mega Brain

OKF knowledge navigation. ALWAYS use this skill the moment a `<mega-brain>` block appears in session context — it means the project has documented concepts and you must consult them before answering questions about data, schemas, metrics, APIs, or systems. Also trigger when the user asks about a table, column, metric definition, service, or API endpoint and there's any chance it's documented in the project. When in doubt, read the file directly.

guhcostan df822cf 2.3 KB Updated Sep 17, 2026

File contents

claude-mega-brain — OKF Knowledge Navigator

When <mega-brain> context is present, the project has OKF-documented concepts — any Markdown file with type: in its YAML frontmatter. These are the authoritative source for domain knowledge: exact schemas, metric formulas, API contracts, runbooks, business definitions. Answers from the knowledge base beat answers from training data.

What OKF files look like

Any .md file anywhere in the project with YAML frontmatter containing type::

---
type: BigQuery Table
title: Orders
description: One row per completed customer order.
resource: https://...
tags: [sales, revenue]
timestamp: 2026-06-29T00:00:00Z
---

# Schema
| Column | Type | Description |
...

No dedicated folder required. The file can live in docs/, wiki/, project root, anywhere.

Navigation

Read the file — paths in <mega-brain> are relative to the project root; use the Read tool directly
Follow links — [text](rel-path.md) links in the body point to related concepts; read them
log.md — chronological changelog; last 3 entries are injected automatically
Linked concepts auto-surface — after you read an OKF file, linked concept summaries appear via PostToolUse hook

When to proactively read files

User asks about a data source, table, column, or metric — check <mega-brain> index first
Before writing SQL, queries, or code that touches project data
When a business term is ambiguous — OKF has the canonical definition
When the user says "check the knowledge base", "what do we know about X", or "olha no okf"

If `<mega-brain>` context is missing

No OKF concepts found in the project yet. Suggest:

/mega-brain:init — creates index.md and log.md to get started
/mega-brain:migrate — scans existing docs and generates OKF files automatically

---name: claude-mega-braindescription: OKF knowledge navigation. ALWAYS use this skill the moment a `<mega-brain>` block appears in session context — it means the project has documented concepts and you must consult them before answering questions about data, schemas, metrics, APIs, or systems. Also trigger when the user asks about a table, column, metric definition, service, or API endpoint and there's any chance it's documented in the project. When in doubt, read the file directly.---# claude-mega-brain — OKF Knowledge NavigatorWhen `<mega-brain>` context is present, the project has OKF-documented concepts — any Markdown file with `type:` in its YAML frontmatter. These are the authoritative source for domain knowledge: exact schemas, metric formulas, API contracts, runbooks, business definitions. **Answers from the knowledge base beat answers from training data.**## What OKF files look likeAny `.md` file anywhere in the project with YAML frontmatter containing `type:`:```yaml---type: BigQuery Tabletitle: Ordersdescription: One row per completed customer order.resource: https://...tags: [sales, revenue]timestamp: 2026-06-29T00:00:00Z---# Schema| Column | Type | Description |...```No dedicated folder required. The file can live in `docs/`, `wiki/`, project root, anywhere.## Navigation1. **Read the file** — paths in `<mega-brain>` are relative to the project root; use the Read tool directly2. **Follow links** — `[text](rel-path.md)` links in the body point to related concepts; read them3. **log.md** — chronological changelog; last 3 entries are injected automatically4. **Linked concepts auto-surface** — after you read an OKF file, linked concept summaries appear via PostToolUse hook## When to proactively read files- User asks about a data source, table, column, or metric — check `<mega-brain>` index first- Before writing SQL, queries, or code that touches project data- When a business term is ambiguous — OKF has the canonical definition- When the user says "check the knowledge base", "what do we know about X", or "olha no okf"## If `<mega-brain>` context is missingNo OKF concepts found in the project yet. Suggest:- `/mega-brain:init` — creates `index.md` and `log.md` to get started- `/mega-brain:migrate` — scans existing docs and generates OKF files automatically

Frequently asked questions