plan
Creates detailed implementation plan from validated research. Produces task breakdown with dependencies and file inventory.
Best use case
plan is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Creates detailed implementation plan from validated research. Produces task breakdown with dependencies and file inventory.
Teams using plan should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/plan/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How plan Compares
| Feature / Agent | plan | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Creates detailed implementation plan from validated research. Produces task breakdown with dependencies and file inventory.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Plan Skill
Transforms validated research into an actionable implementation plan.
---
## Agent Compatibility
- AskUserQuestion: use the tool in Claude Code; in Codex CLI, ask the user directly.
- OUTPUT_DIR: `.claude/output` for Claude Code, `.codex/output` for Codex CLI.
---
## Phase 1: Architectural Decisions
Before task breakdown, decide:
- Which existing patterns and components to reuse (from research + AGENTS.md)
- New vs. extend: create new files or modify existing?
- Data flow: API → Repository → Service → Controller → UI (follow project layers)
- State management approach (follow AGENTS.md)
---
## Phase 2: Open Questions
Before writing the plan, identify any open edge cases or scope questions:
- Empty states, error states, boundary conditions with no specified behavior
- Scope ambiguity: "should X be included?"
- Conflicting requirements
**If there are ANY open questions, list them ALL and ask in a single batch before writing the plan.**
> Do NOT silently assume edge case behavior — ask.
> Document the user's answer in the plan under "Confirmed Decisions".
Example:
```
Before I write the plan, I need to clarify a few things:
1. [Edge case] When the list is empty, should we show "No results" or hide the section?
2. [Scope] Should {feature X} be included in this sprint or deferred?
3. [Architecture] Should we extend {ExistingController} or create a new one?
```
---
## Phase 3: Task Decomposition
Break implementation into atomic, sequential tasks. Each task:
```
T{n}: {Short title}
Layer: data / domain / application / presentation
Files: {list of files to create or modify}
Requires: {R1, R2...} (requirement IDs it fulfills)
Depends on: {T1, T2...} (tasks that must complete first)
Acceptance criteria:
- [ ] {specific verifiable criterion}
```
### Task Ordering Rules
1. **Foundation first:** Models → Services → Controllers → Screens
2. **Layer order:** Data → Domain → Application → Presentation
3. **No circular dependencies**
4. **Tests adjacent to related code**
---
## Phase 4: File Inventory
List every file to create or modify:
```
Create:
lib/src/features/{feature}/data/{name}_response.dart
lib/src/features/{feature}/presentation/{screen}_screen.dart
...
Modify:
lib/src/routes/app_router.dart (add route)
...
```
---
## Phase 5: Risk Assessment
Brief notes on:
- Technical unknowns remaining
- External dependencies (new API endpoints, third-party libs)
- Rollback considerations
---
## Output Template
Save to `OUTPUT_DIR/plan-{feature}.md`:
```markdown
# Implementation Plan: {Feature Name}
## Metadata
- Date: {date}
- Source: research-{feature}.md
- Complexity: {Low / Medium / High}
## Confirmed Decisions
| Question | Decision |
|----------|----------|
| {edge case} | {answer} |
## Architectural Approach
{Brief description of approach and patterns used}
## Tasks
### T1: {Title}
- **Layer**: {layer}
- **Files**: `{path}`
- **Requires**: R1, R2
- **Depends on**: —
- **Acceptance criteria**:
- [ ] {criterion}
### T2: {Title}
...
## File Inventory
### Created
| File | Purpose |
|------|---------|
| `path` | {purpose} |
### Modified
| File | Changes |
|------|---------|
| `path` | {changes} |
## Requirement Traceability
| Requirement | Addressed by |
|-------------|--------------|
| R1: {desc} | T1, T3 |
| R2: {desc} | T2 |
## Risks
{List or "None identified"}
```
---
## Quick Commands
```
/plan — Create plan from validated research
/plan verify — Verify existing plan against requirements
```Related Skills
rpi
Use when implementing features from Jira tickets, PRDs, or user requirements. Orchestrates Research-Plan-Implement workflow with quality gates.
research
Use when needing to understand requirements before implementation. Gathers context from Jira, Confluence, codebase, and docs. Produces research document with confidence assessment.
implement
Executes implementation plan with quality checks and progress tracking. Follows AGENTS.md patterns strictly.
code-review
Reviews code for correctness, security, performance, and pattern compliance. P0/P1/P2 severity. Absorbs security and performance audit checks.
audit
Validates research or plan against hallucination, overscoping, and traceability. Produces a clear PASS/WARN/FAIL verdict.
RPI Stack Skill Distribution
Lean Research-Plan-Implement workflow skills for Claude Code and Codex.
plankton-code-quality
使用Plankton进行编写时代码质量强制执行——通过钩子在每次文件编辑时自动格式化、代码检查和Claude驱动的修复。
inventory-demand-planning
为多地点零售商提供需求预测、安全库存优化、补货规划及促销提升估算的编码化专业知识。基于拥有15年以上管理数百个SKU经验的需求规划师的专业知识。包括预测方法选择、ABC/XYZ分析、季节性过渡管理及供应商谈判框架。适用于预测需求、设定安全库存、规划补货、管理促销或优化库存水平时使用。license: Apache-2.0
inventory-demand-planning
Codified expertise for demand forecasting, safety stock optimisation, replenishment planning, and promotional lift estimation at multi-location retailers.
executing-plans
Use when you have a written implementation plan to execute in a separate session with review checkpoints
concise-planning
Use when a user asks for a plan for a coding task, to generate a clear, actionable, and atomic checklist.
FP&A Command Center — Financial Planning & Analysis Engine
You are a senior FP&A professional. You build financial models, run variance analysis, produce board-ready reports, and turn raw numbers into strategic decisions. You work with whatever data the user provides — spreadsheets, CSV, pasted numbers, or verbal estimates.