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Plan

Organization
TheBushidoCollective
plan

Create tactical implementation plan for a feature or task

Overview

PublisherTheBushidoCollective
Repositoryhan
Skill nameplan
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 Plan 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/plan .claude/skills/plan
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Plan 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 Plan 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 Plan 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.

Technical Planning Skill

Create actionable implementation plans for features and tasks.

Name

han-core:plan - Create tactical implementation plan for a feature or task

Synopsis

/plan [arguments]

Core Principle

A good plan turns a vague goal into concrete, executable steps.

Planning vs Architecture

Technical Planning (this skill):

  • Tactical: "How do I build feature X?"
  • Specific implementation steps
  • Breaks work into tasks
  • Focuses on execution

Architecture Design (architecture-design skill):

  • Strategic: "How should the system be structured?"
  • High-level design decisions
  • Defines components and patterns
  • Focuses on structure

Use planning for:

  • Implementing specific features
  • Breaking down work into tasks
  • Sequencing implementation steps
  • Estimating complexity

Use architecture for:

  • Designing new systems
  • Major refactors
  • Technology choices
  • Long-term strategy

The Planning Process

1. Understand Requirements

Clarify what success looks like:

  • What exactly needs to be built?
  • What problem does it solve?
  • What are the acceptance criteria?
  • What's in scope vs out of scope?

Ask questions:

  • Who will use this?
  • What's the expected behavior?
  • What edge cases should be handled?
  • Are there performance requirements?
  • Security concerns?

2. Analyze Current State

Understand what exists:

  • What code is already there?
  • What patterns are in use?
  • What can be reused?
  • What needs to change?

Research:

bash
# Find similar implementations
grep -r "similar_pattern" .

# Find related files
find . -name "*related*"

# Check existing tests
grep -r "test.*similar" test/

3. Break Down Into Tasks

Good tasks are:

  • Specific: "Add user authentication" -> "Create login API endpoint"
  • Testable: Clear success criteria
  • Right-sized: Hours to days, not weeks
  • Independent (when possible): Can be done in any order
  • Ordered (when dependencies exist): Clear sequence

Task template:

markdown
### Task: [Specific deliverable]

**What:** [Concrete description]
**Why:** [Reasoning for this approach]
**Dependencies:** [None, or list of task numbers]
**Complexity:** S | M | L
**Success criteria:**
- [ ] Criterion 1
- [ ] Criterion 2

**Files affected:**
- `path/to/file1.ts`
- `path/to/file2.ts`

**Testing approach:**
[How to verify this works]

4. Identify Dependencies

Task dependencies:

  • Blocks: Task A must finish before Task B starts
  • Blocked by: Task B can't start until Task A finishes
  • Related: Tasks that should coordinate

Example:

Task 1: Create database schema (no dependencies)
Task 2: Create API endpoint (depends on Task 1)
Task 3: Create UI component (depends on Task 2)
Task 4: Add tests (depends on Tasks 1-3)

Parallel vs Sequential:

Can be parallel:
  Task A: Frontend component
  Task B: Backend API
  (If API contract is defined)

Must be sequential:
  Task 1: Database migration
  Task 2: Update queries to use new schema
  (Task 2 depends on Task 1)

5. Estimate Complexity

Use relative sizing, not time:

  • S (Small): Straightforward, minimal unknowns
  • M (Medium): Moderate complexity, some unknowns
  • L (Large): Complex or uncertain, multiple files

If task is > L: Break it down further

Complexity factors:

  • How well-understood is the requirement?
  • How many unknowns?
  • How many files need changes?
  • Integration complexity?
  • Testing complexity?

6. Define Testing Strategy

How will we verify it works?

  • Unit tests for business logic
  • Integration tests for API endpoints
  • E2E tests for user workflows
  • Manual testing steps

Example:

markdown
## Testing Strategy

**Unit tests:**
- Test validation logic
- Test calculation functions
- Test error handling

**Integration tests:**
- Test API endpoint with real database
- Test with various input scenarios
- Test error responses

**E2E tests:**
- User can complete full workflow
- Error messages display correctly
- Success case works end-to-end

**Manual testing:**
- [ ] Test in Chrome
- [ ] Test in Firefox
- [ ] Test on mobile

Plan Document Structure

markdown
# Implementation Plan: [Feature Name]

**Estimated complexity:** S | M | L | XL
**Status:** Draft | Approved | In Progress | Complete

## Goal

[One paragraph: What are we building and why?]

## Current State

[What exists today that's relevant to this plan?]
[What needs to change?]
[What can be reused?]

## Proposed Approach

[High-level strategy: How will we build this?]
[Key technical decisions made]
[Alternatives considered and why not chosen]

## Tasks

### Phase 1: Foundation

#### Task 1.1: [Specific deliverable] (Complexity: S)

**What:** [Concrete description of what needs to be built]

**Why:** [Reasoning for this approach]

**Dependencies:** None

**Success criteria:**
- [ ] Criterion 1 (testable)
- [ ] Criterion 2 (testable)
- [ ] All tests pass

**Files affected:**
- `src/components/Feature.tsx` (create)
- `src/types/feature.ts` (update)

**Testing approach:**
- Unit test for component logic
- Integration test for data flow

---

#### Task 1.2: [Next task] (Complexity: M)

**What:** [Description]

**Why:** [Reasoning]

**Dependencies:** Task 1.1

**Success criteria:**
- [ ] Criterion 1
- [ ] Criterion 2

**Files affected:**
- `api/routes/feature.ts` (create)

**Testing approach:**
- Integration test for API endpoint

---

### Phase 2: Integration

[Additional tasks organized by phase]

## Testing Strategy

**Overall approach:**
[How we'll verify the entire feature works]

**Test coverage goals:**
- Critical paths: 100%
- Happy paths: 100%
- Edge cases: 80%

## Risks & Considerations

| Risk | Impact | Mitigation |
|------|--------|------------|
| Database migration fails | High | Test in staging first, have rollback plan |
| API performance slow | Medium | Add caching, monitor metrics |

## Out of Scope

**Explicitly NOT included in this plan:**
- [Feature A - deferred to v2]
- [Integration B - separate work]
- [Optimization C - premature]

## Open Questions

- [ ] Should we use library X or Y?
- [ ] What's the rate limit for the external API?

## Success Metrics

**How we'll know this is successful:**
- Feature ships to production
- All tests pass
- Performance meets requirements (< 200ms response)
- Zero critical bugs in first week

## References

- [Related documentation]
- [Design mockups]
- [API specifications]
- [Similar implementations]

Task Breakdown Strategies

By Layer

Frontend tasks:
- Task 1: Create UI component
- Task 2: Add form validation
- Task 3: Connect to API

Backend tasks:
- Task 4: Create API endpoint
- Task 5: Add business logic
- Task 6: Database queries

Infrastructure:
- Task 7: Update deployment config

By Feature Slice

User Authentication (vertical slice):
- Task 1: Login form (frontend)
- Task 2: Login API (backend)
- Task 3: Session management
- Task 4: E2E test for login flow

Password Reset (vertical slice):
- Task 5: Password reset form
- Task 6: Password reset API
- Task 7: Email notification
- Task 8: E2E test for reset flow

By Priority

Must Have (P0):
- Task 1: Core functionality
- Task 2: Critical path

Should Have (P1):
- Task 3: Nice to have feature
- Task 4: Enhancement

Could Have (P2):
- Task 5: Polish
- Task 6: Optimization

Planning Principles

Good plans are:

  • Specific: Concrete steps, not vague ideas
  • Actionable: Each task can be started immediately
  • Ordered: Dependencies clear, sequence logical
  • Right-sized: Tasks are not too large
  • Testable: Success criteria for each task

Bad plans are:

  • Vague ("Make it better")
  • Missing dependencies ("Do A and B" when B depends on A)
  • Too large (one task = weeks of work)
  • Missing context (no reasoning for decisions)

Planning Best Practices

Start Simple

Don't over-plan:

  • Start with high-level tasks
  • Add detail as you learn
  • Plans evolve during implementation

Good enough:

  • Plan should be clear enough to start
  • Not every detail needs to be known upfront
  • Iterate as you go

Make Tasks Actionable

Bad task:

- Improve performance

Good task:

### Task 3: Optimize database queries (Complexity: M)

**What:** Add indexes to users table for email and created_at columns

**Success criteria:**
- [ ] Query time reduced from 500ms to < 50ms
- [ ] Migration runs successfully
- [ ] No impact on existing queries

Document Decisions

Why matters:

markdown
## Why this approach?

We chose REST over GraphQL because:
1. Team is more familiar with REST
2. Simple CRUD operations don't need GraphQL flexibility
3. Can add GraphQL later if needed

**Trade-off:** Less flexible, but simpler to implement

Include Examples

Show, don't just tell:

markdown
## API Design

### Endpoint: POST /api/users

**Request:**
```json
{
  "email": "user@example.com",
  "name": "John Doe"
}

Response:

json
{
  "id": "123",
  "email": "user@example.com",
  "name": "John Doe",
  "createdAt": "2024-01-01T00:00:00Z"
}

## Common Planning Mistakes

### Too Vague

BAD:

  • Implement user system
  • Add features
  • Make it work

### Too Detailed

BAD:

  • Add import statement on line 5
  • Declare variable on line 6
  • Call function on line 7

### Missing Dependencies

BAD: Task 1: Create UI Task 2: Create API (But UI depends on API contract)


### No Success Criteria

BAD: Task: Add validation (How do we know when it's done?)

GOOD: Task: Add validation

  • Email format validated
  • Required fields checked
  • Error messages displayed
  • Tests pass

## Adaptive Planning

**Plans are living documents:**
- Update as you learn
- Add newly discovered tasks
- Remove tasks that aren't needed
- Adjust estimates based on reality

**When to update plan:**
- Discovered new requirement
- Found existing code to reuse
- Identified additional complexity
- Changed approach

## Planning Checklist

Before finishing plan, verify:

- [ ] Goal is clear and specific
- [ ] Current state analyzed
- [ ] Approach is feasible
- [ ] Tasks are specific and actionable
- [ ] Dependencies are identified
- [ ] Success criteria defined for each task
- [ ] Testing strategy included
- [ ] Risks documented
- [ ] Out of scope explicitly stated

## Examples

When the user says:

- "How should I implement user authentication?"
- "Plan out the shopping cart feature"
- "What's the approach for migrating to the new API?"
- "Break down this Jira ticket into tasks"
- "Create a plan for adding dark mode"

## Integration with Other Skills

- Use **simplicity-principles** - Keep plan simple (KISS, YAGNI)
- Use **architecture-design** - For high-level structure decisions
- Use **test-driven-development** - Include testing in tasks
- Reference **solid-principles**, **structural-design-principles** for implementation guidance

## Remember

1. **Plans are guides, not contracts** - Adapt as you learn
2. **Start simple, add detail** - Don't over-plan
3. **Make tasks actionable** - Specific and testable
4. **Document decisions** - Explain the "why"
5. **Include testing** - How will we verify it works?

**A good plan makes it easy to start coding.**

Frequently asked questions

What does the Plan AI skill do?

Create tactical implementation plan for a feature or task

Why use Plan on TypingMind?

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

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

Which AI models can use Plan?

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 Plan?

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

Is the Plan 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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