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naname-zz

FeaturePlanningAutoPilot

by NANAME-ZZ · GitHub ↗ · v1.0.1 · MIT-0
cross-platform ✓ Security Clean
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Install in OpenClaw
/install featureplanningautopilot
Description
A general-purpose feature development planning skill. Used to generate executable, verifiable, and iterative development plans before coding, and to automati...
README (SKILL.md)

Feature Planning AutoPilot

This Skill standardizes the "analyze first → plan next → implement last" approach. It applies to frontend, backend, full-stack, API integration, SQL migrations, and mobile page refactoring tasks.

1) When to Use

Activate this Skill when the user expresses any of the following intentions:

  • "Analyze before writing code"
  • "Give me a plan first" / "Break it into steps" / "Execute in phases"
  • "Ensure correctness and feasibility"
  • "Create a reusable implementation blueprint"

2) Expected Outputs

Every planning session must produce the following four deliverables:

  1. Scope Definition: Clearly state what is and is not included in this task.
  2. Execution Plan: 3–9 verifiable steps in clear sequence.
  3. Risks & Rollback: Key risk points and a minimal rollback strategy.
  4. Acceptance Checklist: Actionable verification items (APIs, pages, logs, builds, tests).

3) Standard Workflow (Must Follow in Order)

Phase A – Quick Clarification

  • Restate the goal in 1–2 sentences
  • Confirm constraints: tech stack, directory, whether API/DB changes are allowed, timeline
  • If information is missing: ask at most 1–3 key questions; default when possible

Phase B – Context Scan

Complete at minimum the following checks before producing a plan:

  • Location of relevant pages / APIs / services
  • Data sources and state transitions (e.g., Pending Delivery → Pending Inbound → Completed)
  • Existing similar implementations (prefer reuse)
  • List of files likely to be affected

Phase C – Plan Drafting

Plan requirements:

  • 3–9 steps, each with a concise 3–7 word action title
  • Every step must be verifiable (has a "definition of done")
  • Only 1 step may be in-progress at a time
  • State prerequisites for external dependencies or database changes

Phase D – Implementation & Validation

  • Execute the plan step by step; update status after each step
  • Perform at least one local validation per phase
  • Before finishing, run a minimum viable acceptance check (build / key path click-through / API response)

Phase E – Lessons Captured (Auto-Iterate)

At the end of the conversation, append three categories of experience to evolution.json:

  • preferences: User's preferred output style or delivery format
  • fixes: Issues encountered this session and how they were resolved
  • custom_prompts: Strong prompts that can be reused directly next time

Also sync the learnings back into the "Experience Enhancements" section of SKILL.md.

4) Plan Template (Ready to Reuse)

## Feature Development Plan (AutoPilot)

### 0. Goal & Scope
- Goal:
- Included in this task:
- Excluded from this task:
- Prerequisites:

### 1. Execution Steps
1. [ ] Review current state and dependencies
   - Definition of done: Relevant files and data flow located
2. [ ] Design minimal-change solution
   - Definition of done: Modification points and impact surface confirmed
3. [ ] Implement core changes
   - Definition of done: Core path code complete
4. [ ] Handle edge cases and errors
   - Definition of done: Empty state / failure state / permission state all functional
5. [ ] Verify and regression test
   - Definition of done: Key paths pass, results are reproducible

### 2. Risks & Rollback
- Risk:
- Monitoring signal:
- Rollback method:

### 3. Acceptance Criteria
- [ ] Feature behavior matches requirements
- [ ] No new build errors introduced
- [ ] Key UI / API paths verified

5) High-Quality Plan Rules (Hard Constraints)

  • No "hollow steps" (e.g., "implement feature")
  • Never omit "data source" or "state transitions"
  • No unrelated large-scale changes (only minimal changes relevant to the task)
  • Never skip validation (at least one local check + one result check)
  • Never overstate conclusions (explicitly say so when something cannot be verified)

6) Common Task Mappings

A. Mobile Page Optimization

Must include:

  • Visual layer (spacing, overflow, readability, tap target size)
  • Interaction layer (default state, disabled state, loading state, empty state)
  • Data layer (API fields consistent with rendered output)

B. List / Stats Consistency

Must include:

  • Confirm same source API
  • Confirm same filtering criteria
  • Verify homepage numbers match detail list

C. State-Driven Workflows (e.g., Inbound)

Must include:

  • Define initial state
  • Define conditions that trigger state transitions
  • Intercept and prompt for invalid states

7) Auto-Iteration Protocol

Trigger an experience update for this Skill when any of the following conditions are met:

  • User explicitly responds with "this is great" or "this isn't working"
  • A build error occurs and is resolved
  • A deviation between the plan and the actual implementation is corrected

Update Structure

evolution.json follows this structure:

{
  "last_updated": "ISO_DATETIME",
  "preferences": [],
  "fixes": [],
  "custom_prompts": ""
}

Principles for Writing Learnings

  • Preferences → write abstract rules (reusable across projects)
  • Fixes → write "trigger condition + resolution approach"
  • Prompts → write instructions that can be pasted directly

8) Recommended Trigger Commands

  • /plan-auto — Generate a standard execution plan
  • /plan-auto deep — Output a full plan including risks and rollback
  • /plan-auto mobile — Focus on mobile page and interaction refactoring
  • /plan-auto evolve — Automatically capture learnings at the end of the session

9) Experience Enhancements (Synced from evolution.json)

This section is auto-updated by the iteration process to prevent experience loss.

User-Learned Best Practices & Constraints

  • Plan conclusions must align with the current implementation scope; avoid overreaching beyond applicable scenarios.
  • Before presenting an implementation plan, cross-check existing code and database state to ensure the plan is logically sound and actionable.
  • Before outputting a plan, clearly define the applicable scope and state transition conditions, and state all necessary prerequisites.
Usage Guidance
This skill appears to be what it says: a planning assistant. Before installing, be aware it will (when invoked) inspect repository files, may run local build/API validations, and will append/update evolution.json and SKILL.md to persist lessons. Ensure the agent only has access to repositories and credentials you trust, review any file changes the skill makes, and avoid providing DB or cloud credentials unless absolutely necessary for a specific validation step. If you want stricter limits, keep it user-invocable (not always-on) and run it in an isolated workspace or with version control so you can review and revert its edits.
Capability Analysis
Type: OpenClaw Skill Name: featureplanningautopilot Version: 1.0.1 The 'Feature Planning AutoPilot' skill is a development workflow tool designed to guide an AI agent through analysis, planning, and implementation phases. It features an 'Auto-Iteration Protocol' that allows the agent to record user preferences and fixes into `evolution.json` and `SKILL.md` to improve future performance. No malicious behaviors, such as data exfiltration or unauthorized command execution, were identified; the self-modification logic is aligned with the stated purpose of learning from interactions.
Capability Assessment
Purpose & Capability
Name/description match the SKILL.md. No unrelated credentials, binaries, or installs are requested. The requested actions (scanning code state, producing step-by-step plans, capturing lessons) are coherent with a planning/Autopilot skill.
Instruction Scope
The runtime instructions explicitly ask the agent to locate relevant pages/APIs/services, list affected files, perform local validations (builds, API checks), and append learnings to evolution.json and the 'Experience Enhancements' section of SKILL.md. These actions stay within the planning purpose but imply reading and writing repository/workspace files and running local checks—make sure the agent has permission and you expect those modifications.
Install Mechanism
Instruction-only skill with no install spec or third-party downloads. Nothing will be written to disk by an installer; any file writes come from the agent following SKILL.md instructions.
Credentials
The skill requests no environment variables or credentials. However, it asks the agent to inspect code, database state, and perform API or build checks; those actions may require additional access or credentials at runtime. The SKILL.md does not request or store secrets itself, which is proportionate, but users should avoid supplying unnecessary credentials to the agent.
Persistence & Privilege
always is false and the skill is user-invocable (expected). The instructions explicitly direct appending to evolution.json and syncing SKILL.md, so the skill will persist 'lessons learned' across sessions by modifying its own files. Modifying its own files is normal but notable—review what gets written and ensure you consent to persistent changes.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install featureplanningautopilot
  3. After installation, invoke the skill by name or use /featureplanningautopilot
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.1
- Updated documentation with a full English version, maintaining core structure and templates. - No functional or file changes; this is a doc translation and clarification update. - Plan templates, workflow phases, quality rules, example mappings, and auto-iteration protocol are clarified in English. - Usage guidance and best practices remain the same, now accessible for an English-speaking audience.
v1.0.0
- Initial release of Feature Planning AutoPilot. - Standardizes "analyze → plan → implement" workflow for a variety of development tasks, including frontend, backend, fullstack, API, SQL, and mobile changes. - Outputs four required planning results: scope definition, executable steps, risk & rollback plan, and a verifiable checklist. - Includes automatic experience capture and iterative improvement after each dialogue. - Provides a reusable plan template and high-quality planning rules to ensure actionable, minimal, and testable steps.
Metadata
Slug featureplanningautopilot
Version 1.0.1
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 2
Frequently Asked Questions

What is FeaturePlanningAutoPilot?

A general-purpose feature development planning skill. Used to generate executable, verifiable, and iterative development plans before coding, and to automati... It is an AI Agent Skill for Claude Code / OpenClaw, with 260 downloads so far.

How do I install FeaturePlanningAutoPilot?

Run "/install featureplanningautopilot" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is FeaturePlanningAutoPilot free?

Yes, FeaturePlanningAutoPilot is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does FeaturePlanningAutoPilot support?

FeaturePlanningAutoPilot is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created FeaturePlanningAutoPilot?

It is built and maintained by NANAME-ZZ (@naname-zz); the current version is v1.0.1.

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