skill-creator（技能创建器）

Skill Creation Standards

Create and manage Agent Skills for Antigravity/Gemini CLI.

About Agent Skills

Skills are modular packages that extend Antigravity with specialized expertise and workflows. They follow the open Agent Skills specification.

Skill Locations:

• Workspace skills: .agent/skills/ - project-specific, committed to version control
• Global skills: ~/.gemini/skills/ - personal skills across all workspaces

Skill Structure

skill-name/
├── SKILL.md          # Required - instructions and metadata
├── scripts/          # Optional - executable scripts
├── references/       # Optional - documentation to load as needed
└── assets/           # Optional - templates, images, data files

SKILL.md Format

Frontmatter (required)

---
name: skill-name # Required: 1-64 chars, lowercase, hyphens only
description: What skill does # Required: 1-1024 chars, include trigger keywords
license: MIT # Optional: license identifier
compatibility: Requires git # Optional: 1-500 chars, environment requirements
metadata: # Optional: custom key-value pairs
  author: example-org
  version: "1.0"
allowed-tools: Bash(git:*) Read # Experimental: pre-approved tools
---

Name rules:

• Lowercase alphanumeric and hyphens only (a-z, 0-9, -)
• No starting/ending hyphens, no consecutive hyphens (--)
• Must match parent directory name

Description tips:

• Include WHEN to use: specific scenarios, file types, or tasks
• Add keywords that help agents identify relevant tasks

Body Content

Markdown instructions for the agent. Keep under 5000 tokens for optimal context usage.

Include:

• Tool Usage Standards: Standard patterns for using Antigravity tools (run_command, read_file) within this domain.
• Step-by-step workflows (Procedures, not Lifecycle Workflows)
• Code examples
• Edge cases and troubleshooting

Skill Design Principles

Strict Separation of Concerns:

• Skill = Knowledge: Capabilities, Standards, Best Practices, Reference Data.
• Workflow = Process: Steps, Sequences, Lifecycles, Timelines.

[!WARNING] Do NOT embed Workflows in Skills. - ❌ Bad: A backend-developer skill defining a "Feature Implementation" lifecycle. - ✅ Good: A backend-developer skill defining "API Design Standards" and asking the user to use a generic Workflow to execute it.

Progressive Disclosure

Skills use three-level loading to manage context efficiently:

1. Metadata (~100 tokens): name + description loaded at startup
2. Instructions (<5000 tokens): Full SKILL.md body when skill activates
3. Resources (as needed): scripts/, references/, assets/ loaded on demand

Creating a Skill

Step 1: Clarify Scope (Required)

Always ask the user to clarify the skill's scope before proceeding:

• What specific tasks should this skill handle?
• What tools, APIs, or frameworks does it involve?
• What are the expected inputs and outputs?
• Are there any existing workflows or patterns to follow?

Step 2: Research & Analysis

Use available tools to understand the full picture:

1. Search tools: Use search_web, read_url_content to research official documentation
2. Codebase analysis: Use grep_search, find_by_name to understand existing patterns

Analyze step-by-step:

• What are the core capabilities needed?
• What standard Antigravity tools are most relevant? (e.g., read_file for docs, run_command for CLI)
• What are common use cases and edge cases?
• What scripts or references would be reusable?

Step 3: Initialize

python scripts/init_skill.py <skill-name> --path .agent/skills

Step 4: Edit SKILL.md

1. Complete the frontmatter metadata with accurate description
2. Write clear, actionable instructions based on research
3. Add scripts/references/assets as identified in analysis

Step 5: Validate

python scripts/quick_validate.py .agent/skills/<skill-name>

Resource Directories

scripts/

Executable code (Python/Node/Bash) for automation tasks.

• Include error handling and helpful messages
• Document dependencies in requirements.txt

references/

Documentation loaded into context when needed.

• API references, schemas, detailed guides
• Keep files focused (<5000 tokens each)

assets/

Files used in output, not loaded into context.

• Templates, images, fonts, boilerplate code

Expert Questioning Framework

When a user requests to create or upgrade a skill, the agent MUST gather requirements through 5 phases.

[!IMPORTANT] Do NOT just provide a generic questionnaire. You must generate domain-specific questions based on: - The skill's domain (frontend, backend, AI, etc.) - The user's specific request - Your expert knowledge in that domain

Workflow:

1. Analyze the request - Identify the skill domain and user's goal
2. Generate custom questionnaire - Create an Artifact for the user to fill out.

- MUST customize questions for the specific domain/task. - Use the template as a base but rewrite questions to be specific.

1. Notify User - Use notify_user with PathsToReview pointing to the artifact.
2. Wait for Input - User will edit the artifact directly.
3. Read & Proceed - Read the filled artifact and start implementation.

Example - Designer Skill Upgrade:

Instead of generic "What constraints?", ask specific questions like:

• "Should the skill focus on Web, Mobile, or both?"
• "Which design systems to reference? (Material, Ant, custom)"
• "Accessibility standards: WCAG 2.1 AA or AAA?"
• "Dark mode support required?"

Phase 1: Discovery (Understand WHY & WHO)

• What specific problem needs to be solved?
• Who will use this skill? (agent, human, or both)
• What triggers the need to create/upgrade this skill?

Phase 2: Context (Understand WHAT & WHERE)

• What is the specific domain? (frontend, backend, devops, AI, etc.)
• What tech stack or frameworks are involved?
• What standard tool patterns apply? (e.g., File system ops vs Command line ops)
• What constraints must be followed? (time, resources, team size)

Phase 3: Scope (Define Boundaries)

[!CAUTION] ⚠️ NEVER decide scope on your own - always ask user for confirmation

• What is IN scope? What is OUT of scope?
• Minor update or Major upgrade?
• Dependencies with other skills/tools?

Phase 4: Quality (Define Quality Standards)

• What level of expertise to embody? (junior, senior, expert 20+ years)
• Which best practices must be followed?
• What edge cases need to be handled?

Phase 5: Validation (Define Success)

• What are the acceptance criteria?
• How do we know the skill works correctly?
• What metrics measure improvement?

Key Principles:

• 🔍 Research before asking - use tools to understand context
• 🎯 Purpose-driven questions - each question leads to a specific decision
• 📝 Document answers - record for reference during implementation

Upgrading Existing Skills

Step 1: Clarify User Intent

Apply the Expert Questioning Framework above. Position yourself as an expert in the skill's domain to ask guiding questions.

Step 2: Analyze Current Skill

# Backup current skill
cp -r .agent/skills/<skill-name> .agent/skills/<skill-name>.backup

# View structure
ls -la .agent/skills/<skill-name>/

Analyze:

• Read SKILL.md: frontmatter, body, sections
• Check scripts/, references/, assets/
• Count tokens, sections, capabilities

Step 3: Gap Analysis

Compare current state vs new requirements:

If duplicate: Notify user and ask if they want to enhance further

Step 4: Propose Changes

Present proposal and ask user for confirmation:

• Minor Update: Add notes, small fixes in SKILL.md
• Major Upgrade: Restructure, add scripts/references
• Restructure: Change architecture

Step 5: Implement & Report

After implementation, run:

python scripts/compare_skill.py .agent/skills/<skill-name>.backup .agent/skills/<skill-name>

Change Report Template

After upgrading a skill, report using this format:

## Skill Upgrade Report: [skill-name]

### Summary

- **Type**: Minor Update / Major Upgrade / Restructure
- **Date**: YYYY-MM-DD

### Changes Made

| File           | Action   | Description            |
| -------------- | -------- | ---------------------- |
| SKILL.md       | Modified | Added X, Y, Z sections |
| scripts/new.py | Added    | Script for ...         |

### Before vs After

| Metric   | Before | After | Change |
| -------- | ------ | ----- | ------ |
| Tokens   | X      | Y     | +Z%    |
| Sections | X      | Y     | +Z     |

### Validation

- [ ] `quick_validate.py` passed
- [ ] Structure verified
- [ ] User reviewed