AgentSkillsCN

ai-factory.feature

开启一项新功能的开发。根据功能描述，以逻辑名称创建 Git 分支，随后调用 /task 生成实施计划。适用于用户说“new feature”、“start feature”、“implement feature”或“add feature”时使用。

SKILL.md
--- frontmatter
name: ai-factory.feature
description: Start a new feature development. Creates a git branch with a logical name based on feature description, then invokes /task to create an implementation plan. Use when user says "new feature", "start feature", "implement feature", or "add feature".
argument-hint: <feature description>
allowed-tools: Bash(git *) Read Write Skill AskUserQuestion
disable-model-invocation: true

Feature - New Feature Workflow

Start a new feature by creating a branch and planning implementation.

Workflow

Step 0: Load Project Context

FIRST: Read .ai-factory/DESCRIPTION.md if it exists to understand:

  • •Tech stack (language, framework, database)
  • •Project architecture
  • •Existing conventions

This context informs branch naming, task planning, and implementation.

Step 0.1: Ensure Git Repository

Check if git is initialized. If not, initialize it.

bash
git rev-parse --is-inside-work-tree 2>/dev/null || git init

Step 1: Parse Feature Description

From $ARGUMENTS, extract:

  • •Core functionality being added
  • •Key domain terms
  • •Type (feature, enhancement, fix, refactor)

Step 2: Generate Branch Name

Create a descriptive branch name:

code
Format: <type>/<short-description>

Examples:
- feature/user-authentication
- feature/stripe-checkout
- feature/product-search
- fix/cart-total-calculation
- refactor/api-error-handling

Rules:

  • •Lowercase with hyphens
  • •Max 50 characters
  • •No special characters except hyphens
  • •Descriptive but concise

Step 3: Ask About Testing

IMPORTANT: Always ask the user before proceeding:

code
Before we start, a few questions:

1. Should I write tests for this feature?
   - [ ] Yes, write tests
   - [ ] No, skip tests

2. Any specific requirements or constraints?

Store the testing preference - it will be passed to /ai-factory.task and /ai-factory.implement.

Step 4: Create Branch

bash
# Ensure we're on main/master and up to date
git checkout main
git pull origin main

# Create and switch to new branch
git checkout -b <branch-name>

If branch already exists, ask user:

  • •Switch to existing branch?
  • •Create with different name?

Step 5: Invoke Task Planning with Branch Context

Plan file will be named after the branch:

code
Branch: feature/user-authentication
Plan file: .ai-factory/features/feature-user-authentication.md (NOT .ai-factory/PLAN.md!)

Convert branch name to filename:

  • •Replace / with -
  • •Add .md extension

Call /ai-factory.task with explicit context:

code
/ai-factory.task $ARGUMENTS

CONTEXT FROM /ai-factory.feature:
- Plan file: .ai-factory/features/feature-user-authentication.md (use this name, NOT .ai-factory/PLAN.md)
- Testing: yes/no
- Logging: verbose/standard/minimal

IMPORTANT: Pass the exact plan filename to /task. This distinguishes feature-based work from direct /task calls.

Pass along:

  • •Full feature description
  • •Exact plan file name (based on branch, e.g., .ai-factory/features/feature-user-authentication.md)
  • •Testing preference
  • •Logging preference
  • •Any constraints

The plan file allows resuming work based on current git branch:

bash
git branch --show-current  # → feature/user-authentication
# → Look for .ai-factory/features/feature-user-authentication.md

Examples

User: /feature Add user authentication with email/password and OAuth

Actions:

  1. •Parse: authentication feature, email/password + OAuth
  2. •Generate branch: feature/user-authentication
  3. •Ask about testing preference
  4. •Create branch: git checkout -b feature/user-authentication
  5. •Call: /ai-factory.task Add user authentication with email/password and OAuth

User: /feature Fix cart not updating quantities correctly

Actions:

  1. •Parse: bug fix, cart quantities
  2. •Generate branch: fix/cart-quantity-update
  3. •Ask about testing
  4. •Create branch
  5. •Call /ai-factory.task

Important

  • •Always ask about testing before creating the plan
  • •Never assume testing preference - always ask explicitly
  • •Pass testing preference to downstream skills
  • •If git operations fail, report clearly and don't proceed
  • •Don't create branch if one with same purpose exists (ask first)

CRITICAL: Logging Preference

When asking about testing, also ask about logging:

code
Before we start:

1. Should I write tests for this feature?
   - [ ] Yes, write tests
   - [ ] No, skip tests

2. Logging level for implementation:
   - [ ] Verbose (recommended) - detailed DEBUG logs for development
   - [ ] Standard - INFO level, key events only
   - [ ] Minimal - only WARN/ERROR

3. Any specific requirements or constraints?

Default to verbose logging. AI-generated code benefits greatly from extensive logging because:

  • •Subtle bugs are common and hard to trace without logs
  • •Users can always remove logs later
  • •Missing logs during development wastes debugging time

Logging must always be configurable:

  • •Use LOG_LEVEL environment variable
  • •Implement log rotation for file-based logs
  • •Ensure production can run with minimal logs without code changes

Pass the logging preference to /ai-factory.task along with testing preference.