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Feature Planning

Community
mhattingpete
feature-planning

Break down feature requests into detailed, implementable plans with clear tasks. Use when user requests a new feature, enhancement, or complex change.

Overview

Publishermhattingpete
Repositoryclaude-skills-marketplace
Skill namefeature-planning
Stars
675
Forks
96
Bundled files
1
LicenseApache-2.0
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.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Feature Planning 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/mhattingpete/claude-skills-marketplace.git /tmp/claude-skills-marketplace
mkdir -p .claude/skills
cp -r /tmp/claude-skills-marketplace/engineering-workflow-plugin/skills/feature-planning .claude/skills/feature-planning
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Feature Planning 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 Feature Planning 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 Feature Planning 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.

Feature Planning

Systematically analyze feature requests and create detailed, actionable implementation plans.

When to Use

  • Requests new feature ("add user authentication", "build dashboard")
  • Asks for enhancements ("improve performance", "add export")
  • Describes complex multi-step changes
  • Explicitly asks for planning ("plan how to implement X")
  • Provides vague requirements needing clarification

Planning Workflow

1. Understand Requirements

Ask clarifying questions:

  • What problem does this solve?
  • Who are the users?
  • Specific technical constraints?
  • What does success look like?

Explore the codebase: Use Task tool with subagent_type='Explore' and thoroughness='medium' to understand:

  • Existing architecture and patterns
  • Similar features to reference
  • Where new code should live
  • What will be affected

2. Analyze & Design

Identify components:

  • Database changes (models, migrations, schemas)
  • Backend logic (API endpoints, business logic, services)
  • Frontend changes (UI, state, routing)
  • Testing requirements
  • Documentation updates

Consider architecture:

  • Follow existing patterns (check CLAUDE.md)
  • Identify reusable components
  • Plan error handling and edge cases
  • Consider performance implications
  • Think about security and validation

Check dependencies:

  • New packages/libraries needed
  • Compatibility with existing stack
  • Configuration changes required

3. Create Implementation Plan

Break feature into discrete, sequential tasks:

markdown
## Feature: [Feature Name]

### Overview
[Brief description of what will be built and why]

### Architecture Decisions
- [Key decision 1 and rationale]
- [Key decision 2 and rationale]

### Implementation Tasks

#### Task 1: [Component Name]
- **File**: `path/to/file.py:123`
- **Description**: [What needs to be done]
- **Details**:
  - [Specific requirement 1]
  - [Specific requirement 2]
- **Dependencies**: None (or list task numbers)

#### Task 2: [Component Name]
...

### Testing Strategy
- [What types of tests needed]
- [Critical test cases to cover]

### Integration Points
- [How this connects with existing code]
- [Potential impacts on other features]

Include specific references:

  • File paths with line numbers (src/utils/auth.py:45)
  • Existing patterns to follow
  • Relevant documentation

4. Review Plan with User

Confirm:

  • Does this match expectations?
  • Missing requirements?
  • Adjust priorities or approach?
  • Ready to proceed?

5. Execute with plan-implementer

Launch plan-implementer agent for each task:

Task tool with:
- subagent_type: 'plan-implementer'
- description: 'Implement [task name]'
- prompt: Detailed task description from plan

Execution strategy:

  • Implement sequentially (respect dependencies)
  • Verify each task before next
  • Adjust plan if issues discovered
  • Let test-fixing skill handle failures
  • Let git-pushing skill handle commits

Best Practices

Planning:

  • Start broad, then specific
  • Reference existing code patterns
  • Include file paths and line numbers
  • Think through edge cases upfront
  • Keep tasks focused and atomic

Communication:

  • Explain architectural decisions
  • Highlight trade-offs and alternatives
  • Be explicit about assumptions
  • Provide context for future maintainers

Execution:

  • Implement one task at a time
  • Verify before moving forward
  • Keep user informed
  • Adapt based on discoveries

Integration

  • plan-implementer agent: Receives task specs, implements
  • test-fixing skill: Auto-triggered on test failures
  • git-pushing skill: Triggered for commits

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Feature Planning AI skill do?

Break down feature requests into detailed, implementable plans with clear tasks. Use when user requests a new feature, enhancement, or complex change.

Why use Feature Planning on TypingMind?

Because you install it once and use it with any model. Feature Planning 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 Feature Planning in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mhattingpete/claude-skills-marketplace/tree/main/engineering-workflow-plugin/skills/feature-planning. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Feature Planning?

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 Feature Planning?

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

Is the Feature Planning AI skill free?

Yes. It is published on GitHub by mhattingpete under the Apache-2.0 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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