A sophisticated behavioral framework that enables AI agents to handle code modifications ranging from simple UI tweaks to complex architectural refactoring.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install feature-modification-workflow
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install feature-modification-workflow using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Feature Modification Workflow?
The Feature Modification Workflow is a standardized protocol designed to guide AI coding agents through the lifecycle of modifying existing software. It balances speed and safety by categorizing requests into specific scenarios, ensuring that minor adjustments remain fast while high-risk changes undergo rigorous analysis and user verification. This skill is a cornerstone of the Openclaw Skills ecosystem, providing a reliable structure for incremental improvements without breaking core system logic.
By implementing this workflow, developers can ensure their AI collaborators act conservatively during optimization, preserving established data flows while systematically addressing performance bottlenecks and technical debt. Whether you are performing a simple color change or a multi-module refactor, this skill provides the necessary guardrails for high-quality code evolution.
Feature Modification Workflow Use Cases
- Rapid UI/UX adjustments such as modifying colors, fonts, or layout spacing.
- Strategic bug fixing where root cause analysis is required before code execution.
- Large-scale feature optimizations that require preserving existing data flows.
- Architecture refactoring involving multiple files and complex module dependencies.
- Performance tuning where documentation checks are necessary for specific API usage.
How Feature Modification Workflow Works
- Scenario Determination: The agent first classifies the request as a Simple Issue, Bug Fix, Complex Issue, or Refactoring.
- Context Gathering: For complex tasks, the agent uses Glob to map the project structure and identify core dependencies.
- Current State Analysis: The AI evaluates the existing implementation to identify strengths, weaknesses, and bottlenecks.
- Optimization Proposal: A detailed plan is generated that focuses on incremental improvements rather than high-risk rewrites.
- User Confirmation: The agent presents a step-by-step development plan and waits for explicit user approval.
- Sequential Execution: Once confirmed, the agent executes tasks one by one, providing progress updates after each completed step.
Feature Modification Workflow Setup
To integrate this workflow into your AI environment, ensure the skill definition is accessible to your agent's system prompt. Use the following structure to initialize the environment:
# Example of ensuring the skill is registered in your local Openclaw Skills library
openclaw install feature-modification-workflow
Ensure that the agent has access to standard file-system tools (like Glob) and documentation resolution tools (like Context7) to fully leverage the 5-step workflow.
Feature Modification Workflow Data Schema & Taxonomy
The workflow produces structured outputs at various stages to maintain transparency and alignment with the developer:
| Output Type | Format | Key Components |
|---|---|---|
| Project Overview | Markdown | Project type, Core modules, Data flow mapping |
| Current State | Markdown | Implementation analysis, Bottlenecks, Weaknesses |
| Optimization Plan | Markdown | Change scope, Affected modules, Risk assessment |
| Development Plan | Task List | Checkbox-style steps ([ ]) and confirmation prompts |
| Documentation | Reference | API usage guidelines and technical specifications |
Feature Modification Workflow Advanced Features
- Dynamic Workflow Branching: Automatically switches between Simplified and Full workflows based on task complexity.
- Documentation Trigger Logic: Conditional logic to determine when to use Context7 or WebSearch for external technical validation.
- Conservative Optimization Guardrail: Hard constraints that prevent the AI from altering core logic during performance improvements.
- Sequential Progress Tracking: Integration with TodoWrite to maintain state across long-running modification tasks.
SKILL.md
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