Develop logo

Develop

Organization
TheBushidoCollective
develop

Guides end-to-end feature development through 8 phases: discover requirements, explore codebase patterns, clarify ambiguities with the user, design architecture, implement with TDD, run multi-agent code review, validate all quality gates, and write a blog post. Use when asked to add a feature, implement a new capability, build functionality, or develop a feature end-to-end.

Overview

PublisherTheBushidoCollective
Repositoryhan
Skill namedevelop
Stars
195
Forks
20
Bundled files
Instructions only
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 TheBushidoCollective on GitHub. Read the source before you install it.

Installation

Install the Develop 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/TheBushidoCollective/han.git /tmp/han
mkdir -p .claude/skills
cp -r /tmp/han/plugins/core/skills/develop .claude/skills/develop
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Develop 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 Develop 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 Develop 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 Development Workflow

Structured 8-phase process for building new features from requirement gathering through documentation. Each phase produces a concrete output that feeds the next.

Overview

  1. Discover - Understand requirements and context
  2. Explore - Analyze existing codebase patterns
  3. Clarify - Resolve ambiguities with user input
  4. Design - Create architecture with specialized agents
  5. Implement - Build with TDD and quality practices
  6. Review - Multi-agent quality review with confidence scoring
  7. Validate - Run all verification hooks and summarize
  8. Document - Write blog post announcing the feature

Phase 1: Discover

Understand requirements and gather context

Objective: Establish clear understanding of what needs to be built and why.

  1. Review the feature request:

    • What is the user-facing goal?
    • What problem does this solve?
    • What are the acceptance criteria?
  2. Identify impacted areas:

    • Which parts of the codebase will change?
    • What existing features might be affected?
    • Are there related issues or PRs?
  3. Check for similar features:

    bash
    # Search for similar implementations
    grep -r "similar_feature_name" .
  4. Review project documentation:

    • Check CLAUDE.md, CONTRIBUTING.md for standards
    • Review architecture docs if available
    • Identify any constraints or requirements

Output: Clear problem statement and high-level approach.


Phase 2: Explore (Parallel Agent Execution)

Analyze codebase with specialized agents

Objective: Understand existing patterns and identify integration points.

Launch multiple Explore agents in PARALLEL (single message with multiple Task calls):

  1. Code Explorer: Map existing features

    • Find entry points and call chains
    • Identify data flow and transformations
    • Document current architecture
  2. Pattern Analyzer: Identify conventions

    • How are similar features implemented?
    • What testing patterns are used?
    • What naming conventions exist?
  3. Dependency Mapper: Understand relationships

    • What modules will be affected?
    • What are the integration points?
    • Are there circular dependencies to avoid?

Consolidation: Synthesize findings from all agents into a cohesive understanding.

Output: Comprehensive map of existing codebase patterns and integration points.


Phase 3: Clarify (Human Decision Point)

Resolve ambiguities before implementation

Objective: Get user input on unclear requirements and design choices.

Use AskUserQuestion tool to resolve:

  1. Architecture decisions:

    • Which approach should we take? (if multiple valid options)
    • What are the trade-offs? (performance vs. simplicity)
  2. Scope clarifications:

    • Should this include X feature?
    • What's the priority if time is limited?
  3. Integration choices:

    • Should we extend existing module or create new one?
    • How should this integrate with system Y?

IMPORTANT: Do not proceed with assumptions. Get explicit user answers.

Output: Clear, unambiguous requirements with user-approved approach.


Phase 4: Design (Parallel Agent Execution)

Create architecture with specialized agents

Objective: Design the implementation before coding.

Select appropriate specialized agent(s) based on feature type:

  • Frontend feature?frontend:presentation-engineer
  • Backend API?backend:api-designer
  • Database changes?databases:database-designer
  • Complex system?architecture:solution-architect

Launch agents in PARALLEL for multi-disciplinary features:

  • Frontend + Backend agents simultaneously
  • Include security:security-engineer for sensitive features
  • Include performance:performance-engineer for high-traffic features

Agent responsibilities:

  • Define module structure and file organization
  • Specify interfaces and contracts
  • Identify testing strategy
  • Document key decisions and trade-offs

Consolidation: Review all design proposals, resolve conflicts, select final approach.

Output: Detailed implementation plan with module structure and interfaces.


Phase 5: Implement (TDD with Quality Enforcement)

Build the feature using test-driven development

Objective: Implement the designed solution with quality practices.

Apply TDD cycle (use tdd:test-driven-development skill):

For each component:
1. Write failing test (Red)
2. Implement minimum code to pass (Green)
3. Refactor for quality (Refactor)
4. Repeat

Implementation guidelines:

  • ✅ Start with tests, not implementation
  • ✅ Follow existing codebase patterns (from Phase 2)
  • ✅ Apply SOLID principles (han-core:solid-principles skill)
  • ✅ Keep it simple (KISS, YAGNI)
  • ✅ Apply Boy Scout Rule - leave code better than found
  • ❌ Don't over-engineer
  • ❌ Don't skip tests
  • ❌ Don't ignore linter/type errors

Integration:

  • Integrate incrementally (don't build everything then integrate)
  • Test integration points early
  • Validate against acceptance criteria continuously

Output: Working implementation with comprehensive tests.


Phase 6: Review (Parallel Multi-Agent Review)

Quality review with confidence-based filtering

Objective: Identify high-confidence issues before final validation.

Launch review agents in PARALLEL (single message with multiple Task calls):

  1. Code Reviewer (han-core:code-reviewer skill):

    • General quality assessment
    • Confidence scoring ≥80%
    • False positive filtering
  2. Security Engineer (security:security-engineer):

    • Security vulnerability scan
    • Auth/authz pattern verification
    • Input validation review
  3. Discipline-Specific Agent:

    • Frontend: frontend:presentation-engineer (accessibility, UX)
    • Backend: backend:backend-architect (API design, scalability)
    • etc.

Review consolidation:

  • Merge findings from all agents
  • De-duplicate issues
  • Filter for confidence ≥80%
  • Organize by: Critical (≥90%) → Important (≥80%)

Present findings to user with options:

Found 3 critical and 5 important issues.

Options:
1. Fix all issues now (recommended)
2. Fix critical only, defer important
3. Review findings and decide per-issue

Output: Consolidated review with high-confidence issues only.


Phase 7: Validate & Summarize

Final verification and change summary

Objective: Ensure all quality gates pass and document the change.

Run all validation hooks:

bash
# All validation plugins automatically run on Stop
# Verify: tests, linting, type checking, etc.

Validation checklist:

  • All tests pass
  • Linting passes
  • Type checking passes
  • No security vulnerabilities introduced
  • Documentation updated
  • No breaking changes (or properly coordinated)

Generate change summary:

  1. What changed: Files modified and why
  2. How to test: Steps to verify functionality
  3. Breaking changes: None, or list with migration guide
  4. Follow-up tasks: Any deferred work or tech debt

Create TODO list (using TaskCreate tool):

  • Document any follow-up tasks
  • Track deferred improvements
  • Note any tech debt introduced

Output: Ready-to-commit feature with comprehensive documentation.


Phase 8: Document (Blog Post)

Write a blog post announcing the feature

Objective: Share the new feature with the community and explain its value.

Blog post creation:

  1. Research context (optional):

    • Use reddit to find related community discussions
    • Identify pain points the feature addresses
    • Understand how users talk about this problem
  2. Write the blog post:

    Location: website/content/blog/{feature-slug}.md

    markdown
    ---
    title: "{Feature Name}: {Compelling subtitle}"
    description: "{One-line description of what problem this solves}"
    date: "{YYYY-MM-DD}"
    author: "The Bushido Collective"
    tags: ["{relevant}", "{tags}"]
    category: "Feature"
    ---
    
    {Opening hook - what problem does this solve?}
    
    ## The Problem
    
    {Describe the pain point this feature addresses}
    
    ## The Solution
    
    {Explain how the feature works}
    
    ### Key Capabilities
    
    {List main features with examples}
    
    ## Getting Started
    
    {How to use the feature}
    
    ## What's Next
    
    {Future improvements or related features}
  3. Writing guidelines:

    • Technical but accessible
    • 500-1000 words for feature announcements
    • Include working code examples
    • Be honest about limitations
    • Make it actionable

IMPORTANT: Every significant feature should have a blog post. This is not optional.

Output: Published blog post in website/content/blog/.


Best Practices

DO

  • ✅ Follow all 8 phases in order
  • ✅ Launch agents in parallel when independent
  • ✅ Use AskUserQuestion to resolve ambiguities
  • ✅ Apply confidence scoring to all reviews
  • ✅ Run TDD cycle for all new code
  • ✅ Pause for user input at decision points
  • ✅ Write a blog post for every significant feature

DON'T

  • ❌ Skip phases (especially Explore, Review, and Document)
  • ❌ Start coding before design (Phases 1-4)
  • ❌ Implement without tests
  • ❌ Report low-confidence review findings
  • ❌ Make architectural decisions without user input
  • ❌ Commit without running validation hooks
  • ❌ Ship features without documentation

Example Workflow

User: /feature-dev Add pagination to user list API

Phase 1: Discover
- Feature: Add pagination to GET /api/users
- Acceptance: Support page/limit query params, return total count
- Impact: Backend API, database queries

Phase 2: Explore (parallel agents)
- Found existing pagination in products API
- Pattern: Uses offset/limit with total count in response
- Testing: Integration tests verify pagination logic

Phase 3: Clarify
Q: Should we use cursor-based or offset-based pagination?
A: [User selects offset-based for consistency]

Phase 4: Design
- Agent: backend:api-designer
- Design: Extend existing UserService with pagination
- Interface: getUsersPaginated(page, limit) -> { users, total }

Phase 5: Implement
- Write test for pagination
- Implement pagination logic
- Test passes ✅

Phase 6: Review (parallel agents)
- Code reviewer: No issues (confidence N/A)
- Security engineer: No issues (confidence N/A)
- Backend architect: No issues (confidence N/A)

Phase 7: Validate
- Tests: ✅ Pass
- Linting: ✅ Pass
- Types: ✅ Pass
- Ready to commit

Phase 8: Document
- Blog post: website/content/blog/user-list-pagination.md
- Title: "Pagination: Handling Large Data Sets Gracefully"
- Tags: [api, pagination, performance]

Summary: Added pagination to user list API
Files: services/user.service.ts, tests/user.service.test.ts, website/content/blog/user-list-pagination.md
Testing: Run GET /api/users?page=1&limit=10

See Also

  • /review - Multi-agent code review with confidence-based filtering
  • /test - Write tests using TDD principles
  • /fix - Debug and fix bugs
  • /refactor - Restructure code without changing behavior

Frequently asked questions

What does the Develop AI skill do?

Guides end-to-end feature development through 8 phases: discover requirements, explore codebase patterns, clarify ambiguities with the user, design architecture, implement with TDD, run multi-agent code review, validate all quality gates, and write a blog post. Use when asked to add a feature, implement a new capability, build functionality, or develop a feature end-to-end.

Why use Develop on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/TheBushidoCollective/han/tree/main/plugins/core/skills/develop. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Develop?

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 Develop?

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

Is the Develop AI skill free?

It is published on GitHub by TheBushidoCollective. Check the repository for licensing terms. 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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