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Planning Mastery

Community
xenitV1
planning-mastery

Create concise, architectural implementation plans using the RFC-Lite format. STRICTLY LIMITED VERBOSITY.

Overview

PublisherxenitV1
Repositoryclaude-code-maestro
Skill nameplanning-mastery
Stars
231
Forks
34
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by xenitV1 on GitHub. Read the source before you install it.

Installation

Install the Planning Mastery AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/xenitV1/claude-code-maestro.git /tmp/claude-code-maestro
mkdir -p .claude/skills
cp -r /tmp/claude-code-maestro/skills/planning-mastery .claude/skills/planning-mastery
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Planning Mastery in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Planning Mastery on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Planning Mastery is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

<domain_overview>

📋 RFC-Lite Planning Protocol

The 300-Line Limit: If your plan exceeds 300 lines, YOU HAVE FAILED. Rule: Code belongs in files, not plans. Do not write pseudo-code. Do not paste entire file contents. Focus: Define What (Files), How (Logic Strategy), and Success (Verification).

DEPENDENCY FORECASTING MANDATE (CRITICAL): Never propose a change without mapping its "Blast Radius". AI-generated plans frequently fail by ignoring downstream effects on coupled modules. Before defining file changes, you MUST explicitly identify which existing features or tests might break. If a change requires "Shotgun Surgery" (modifying more than 5 files for one feature), you MUST pause and propose an architectural abstraction instead. </domain_overview>

🎯 CORE PHILOSOPHY

Understanding comes before implementation. A well-designed solution is half-implemented. Never code without a clear design. <template_enforcement>

📝 MANDATORY TEMPLATE (Copy & Fill)

markdown
# [Task/Feature Name] - Implementation Plan
## 1. 🎯 Objective
[1-2 sentences strictly defining the goal.]
## 2. 🏗️ Tech Strategy
- **Pattern:** [e.g. Composition vs Inheritance]
- **State:** [e.g. Global Store vs Local Hook]
- **Constraints:** [e.g. "Must use LCH colors", "No external libs"]
## 3. 📂 File Changes
| Action | File Path | Brief Purpose |
|:-------|:----------|:--------------|
| [NEW]  | `src/components/MyComp.tsx` | Visual shell |
| [MOD]  | `src/App.tsx` | Routing integration |
## 4. 👣 Execution Sequence
1.  **Scaffold:** Create component files with types (No logic yet).
2.  **Logic:** Implement `useLogic.ts` hook with TDD.
3.  **Visuals:** Apply LCH gradients & Glassmorphism.
4.  **Connect:** Wire up to parent component.
## 5. ✅ Verification Standards
- [ ] **Visual:** Check against `frontend_reference.md` (no flat colors).
- [ ] **Interaction:** Verify `scale(0.97)` tap effect.
- [ ] **Console:** Zero errors during flow.

</template_enforcement> <strict_rules>

⛔ ZERO TOLERANCE RULES

  1. NO CODE BLOCKS: Do not write function bodies in the plan.
  2. NO EXPLANATIONS: Do not teach the user why React is good.
  3. NO CONVERSATION: Do not talk to the user in the plan.
  4. STAY HIGH LEVEL: "Implement Auth" is better than "Write function login() { ... }". </strict_rules> <audit_and_reference>

📂 COGNITIVE AUDIT CYCLE

  1. Does the plan exceed 300 lines?
  2. Are all breaking changes identified?
  3. Is it RFC-Lite compliant?
  4. Are verification steps actionable commands? </audit_and_reference>

Frequently asked questions

What does the Planning Mastery AI skill do?

Create concise, architectural implementation plans using the RFC-Lite format. STRICTLY LIMITED VERBOSITY.

Why use Planning Mastery on TypingMind?

Because you install it once and use it with any model. Planning Mastery is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Planning Mastery in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/xenitV1/claude-code-maestro/tree/main/skills/planning-mastery. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Planning Mastery?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Planning Mastery?

As many as you like. As long as a model supports skills, you can use Planning Mastery with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Planning Mastery AI skill free?

Yes. It is published on GitHub by xenitV1 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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