Most MCP servers just wrap CRUD JSON APIs into tools — I did it too with scim-mcp and garmin-mcp-app. It works, until you realize a tool call dumps 50KB+ into context.
MCP isn't dead — but we need to design MCP tools with the context window in mind.
Most MCP servers just wrap CRUD JSON APIs into tools — I did it too with scim-mcp and garmin-mcp-app. It works, until you realize a tool call dumps 50KB+ into context.
MCP isn't dead — but we need to design MCP tools with the context window in mind.
These rules define how an AI coding agent should plan, execute, verify, communicate, and recover when working in a real codebase. Optimize for correctness, minimalism, and developer experience.
This document was written by Shun Tsukamoto (who goes by Shun in my AI-related activities).
These tags directly influence the perceived quality, detail, and "finish" of the image.
| Modifier (Tag) | Description |
|---|---|
| "Magic" Tags | (Highest impact for Noobai/Illustrious) |
very awa |
(Noobai Specific) The most powerful tag, trained on the top 5% of aesthetically-rated images. |
masterpiece |
A classic, high-tier tag for high-quality, artistic output. |
best quality |
Similar to masterpiece, strongly pushes for a clean, well-rendered image. |
On a modern macOS system, the default shell is Zsh.
~/.zshrc (This file is in your home directory. The ~ is a shortcut for /Users/yourusername).nano ~/.zshrc to open the file in a terminal editor.| { | |
| "$schema": "http://json-schema.org/draft-07/schema#", | |
| "title": "Claude Code Settings", | |
| "description": "Configuration schema for Claude Code settings.json files", | |
| "type": "object", | |
| "additionalProperties": false, | |
| "properties": { | |
| "$schema": { | |
| "type": "string", | |
| "description": "JSON Schema reference for this configuration file" |
Launch a new agent that has access to the following tools: Bash, Glob, Grep, LS, exit_plan_mode, Read, Edit, MultiEdit, Write, NotebookRead, NotebookEdit, WebFetch, TodoRead, TodoWrite, WebSearch. When you are searching for a keyword or file and are not confident that you will find the right match in the first few tries, use the Agent tool to perform the search for you.
When to use the Agent tool:
When NOT to use the Agent tool:
| { | |
| "customModes": [ | |
| { | |
| "name": "🧑✈️ Commander", | |
| "slug": "commander", | |
| "roleDefinition": "You are the Commander of the Content Army. You act as the central coordinator for content creation pipelines. Your responsibilities include interacting with the user (for initial requests and mid-pipeline reviews/selections), dynamically planning the workflow based on the briefing output and project context (like style guides and project language), delegating tasks to specialist modes, and managing the state for each content piece by creating and updating task definition files.", | |
| "customInstructions": "As the Commander:\n\n**Core Directives:**\n\n- **Interact with the user in the language they are currently using.** Adapt your responses accordingly.\n- **Internal logic, task definitions, and status reporting should remain in English** for system consistency.\n- **You MUST meticulously track your own token usage and cost.** Before initiating any action that involves significant processing or int |
| I want you to refine this brainstorming document into a prompt for a deep research system that will be tasked with writing a technical spike | |
| research document on a software engineering project. The goal of this research is to help guide future agentic coding systems into | |
| having a good understanding of the technical landscape around the software the user wants to create. | |
| <context> | |
| Deep research is a category of product where large language models capable of test time compute are paired with capacities to: | |
| - search the web | |
| - browse documentatin | |
| - read research paper | |
| - further refine their research based on their finding |