Agent skill
advanced-skill-builder
Guides users through creating production-ready Claude skills via interactive dialogue. Use when user wants to build a new skill, needs help structuring a skill, or asks "how do I create a skill?" Covers requirements gathering, planning, YAML frontmatter generation, instruction writing, testing, and iterative refinement. Works with or without MCP integration.
Install this agent skill to your Project
npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/other/other/advanced-skill-builder
Metadata
Additional technical details for this skill
- tags
-
skill-development automation workflow template
- author
- John-Dekka
- version
- 1.0.0
- category
- productivity
SKILL.md
Advanced Skill Builder
An interactive guide for creating production-ready Claude skills through collaborative dialogue.
When to Use This Skill
Activate this skill when:
- User says: "Help me build a skill", "Create a new skill", or "I need a skill for..."
- User describes a workflow they want to automate but doesn't know how to structure it
- User wants to teach Claude a specific process or methodology
- User asks: "How do I create a skill?" or "Can you help me make a skill?"
- User has an MCP server and wants to add workflow guidance
Dialogue Flow Overview
This skill operates through structured dialogue to gather requirements, then generates a complete skill folder. The flow:
Phase 1: Discovery → Phase 2: Planning → Phase 3: Structure → Phase 4: Generation → Phase 5: Validation
Estimated time: 15-30 minutes for a complete skill Outcome: Ready-to-use skill folder with SKILL.md, scripts/, references/, and assets/
Phase 1: Discovery Questions
Before generating anything, gather context through dialogue. Ask questions in sequence.
Essential Discovery Questions
Q1: Core Purpose
"What specific task or workflow do you want this skill to handle?"
Listen for: The domain, the outcome, the user's pain point
Q2: Target Users
"Who will use this skill—yourself, your team, or external users?"
Listen for: Complexity level needed, documentation depth, sharing intent
Q3: Existing Tools
"What tools, APIs, or services does this skill need to access? (Or none?)"
Listen for: MCP requirements, built-in tools only, custom scripts
Q4: Success Definition
"How will you know the skill is working? What does a successful outcome look like?"
Listen for: Measurable outputs, qualitative markers, edge cases
Q5: Complexity Estimate
"Roughly how many steps is this workflow? (Simple: 1-3, Moderate: 4-7, Complex: 8+)"
Listen for: Structure needed, validation requirements, error handling scope
Phase 2: Planning
Based on discovery answers, recommend a skill category and structure.
Skill Category Selection
| Category | Indicators | Recommended Structure |
|---|---|---|
| Document & Asset Creation | User wants to generate consistent output (docs, designs, code, presentations) | Templates + quality checklists + style guides |
| Workflow Automation | Multi-step process, specific sequence, validation needed | Step-by-step with gates + error handling + rollback |
| MCP Enhancement | Has MCP server, needs workflow guidance | Tool orchestration + domain expertise + error patterns |
Use Case Template
After discovery, create this structured document:
## Skill Use Case Definition
**Skill Name:** [auto-generate from purpose]
**Category:** [Document Creation / Workflow Automation / MCP Enhancement]
**Trigger Phrases:**
- "[phrase 1]"
- "[phrase 2]"
- "[phrase 3]"
**Workflow Steps:**
1. [Step with purpose and tool call]
2. [Step with purpose and tool call]
3. [...]
**Success Criteria:**
- [Criterion 1]
- [Criterion 2]
**Known Edge Cases:**
- [Edge case 1] → [Resolution]
- [Edge case 2] → [Resolution]
Confirm with user: "Does this capture what you need? What should I adjust?"
Phase 3: Skill Structure Generation
Generate the folder structure based on category.
Document & Asset Creation Structure
{skill-name}/
├── SKILL.md
├── assets/
│ ├── template-1.md
│ └── template-2.md
└── references/
└── style-guide.md
Workflow Automation Structure
{skill-name}/
├── SKILL.md
├── scripts/
│ ├── validate.sh
│ └── process.py
└── references/
└── error-codes.md
MCP Enhancement Structure
{skill-name}/
├── SKILL.md
├── scripts/
│ ├── mcp-validator.py
│ └── error-mapper.py
└── references/
├── tool-docs.md
└── workflow-patterns.md
Phase 4: YAML Frontmatter Generation
Generate proper frontmatter with progressive disclosure.
Template
---
name: {skill-name-in-kebab-case}
description: {clear-description} Use when user says "{trigger-1}", "{trigger-2}", or "{trigger-3}".
license: {MIT/Apache-2.0/None}
metadata:
author: {author-name}
version: 1.0.0
{mcp-server: {server-name}} # Only if MCP-enhanced
---
Description Best Practices
Structure: [What it does] + [When to use it] + [Key triggers]
| ✅ Good Example | ❌ Bad Example |
|---|---|
| "Creates API documentation from code comments. Use when user says 'document this API', 'generate docs', or uploads a code file." | "Helps with documentation." |
Validation Checklist
Before finalizing frontmatter:
- Name is kebab-case (no spaces, no capitals)
- Description under 1024 characters
- Description includes WHAT and WHEN
- No XML tags (
<or>) - Triggers are natural phrases users would actually say
- Version follows semantic versioning (1.0.0)
Phase 5: SKILL.md Body Generation
Generate instructions following the recommended structure.
Template Structure
---
name: skill-name
description: Description here.
---
# Skill Name
## Overview
Brief description of what this skill does and when to use it.
## Instructions
### Step 1: [First Major Step]
[Clear instructions with examples]
### Step 2: [Second Major Step]
[Clear instructions with examples]
[...additional steps...]
## Examples
### Example 1: [Common Scenario]
**User says:** "[phrase]"
**Actions:**
1. [Action]
2. [Action]
**Result:** [Expected outcome]
### Example 2: [Another Scenario]
[...]
## Troubleshooting
**Error:** [Common error]
**Cause:** [Why it happens]
**Solution:** [How to fix]
[...]
## Best Practices
- [Tip 1]
- [Tip 2]
Writing Guidelines
For Each Step:
- Start with clear purpose
- Provide concrete example (bash command, MCP call, etc.)
- Describe expected output
- Include error handling
For Examples:
- Use realistic user phrases
- Show full workflow
- Describe concrete result
For Troubleshooting:
- Start with most common errors
- Include both cause and solution
Dialogue-Guided Skill Creation Example
Step-by-Step Walkthrough
Assistant: "Let's build your skill. First, tell me—what task or workflow do you want to automate?"
User: [Responds with their goal]
Assistant: "Got it. A few more questions to understand the scope..."
[Continue discovery questions from Phase 1]
Assistant: "Based on what you've described, this sounds like a [category] skill. Here's what I'm thinking:"
[Present use case template]
User: [Confirms or adjusts]
Assistant: "Great. I'll create the skill folder structure now. What name would you like for this skill? (kebab-case, no spaces)"
User: [Provides name]
Assistant: "Generating your skill with this structure:"
[Present folder structure]
Assistant: "I'm writing the SKILL.md file with proper YAML frontmatter and instructions. Meanwhile, let me ask—what trigger phrases should activate this skill? What would a user say to invoke it?"
User: [Provides phrases]
Assistant: "Perfect. I'm now creating:"
- SKILL.md with frontmatter and instructions
- scripts/ folder with validation utilities
- references/ folder for additional documentation
[Generate files]
Assistant: "Your skill is ready. Let me verify it works:"
[Run validation checks]
Assistant: "✅ SKILL.md created with valid YAML frontmatter ✅ Folder structure follows best practices ✅ Description includes triggers and use cases
Next steps to test:
- Download and upload to Claude.ai via Settings > Capabilities > Skills
- Try: "[trigger phrase]"
- Monitor for: Does it trigger correctly? Does it complete the workflow?
Want me to iterate on any part—perhaps refine the instructions, add more examples, or adjust the triggers?"
Advanced Features
Multi-MCP Orchestration Pattern
For skills coordinating multiple services:
## MCP Coordination
### Phase 1: [First Service]
1. Fetch data via `[service-a]-mcp-tool`
2. Validate response
### Phase 2: [Second Service]
1. Transform data for `[service-b]-mcp-tool`
2. Submit request
### Phase 3: [Third Service]
1. Process response
2. Generate output
### Error Handling
- If Phase 1 fails: [Recovery action]
- If Phase 2 fails: [Recovery action + Phase 1 cleanup]
Iterative Refinement Pattern
For skills where quality improves with iteration:
## Quality Assurance Loop
### 1. Initial Generation
Create first version of output
### 2. Validation Check
Run `scripts/validate-output.py`
- Check: [Criteria]
- Check: [Criteria]
### 3. Refinement
If validation fails:
- Address specific issues
- Re-run validation
- Repeat until pass
### 4. Final Output
Only after validation passes
Context-Aware Selection Pattern
For skills choosing tools dynamically:
## Tool Selection Logic
1. Analyze input type and requirements
2. Select best tool:
- `[tool-a]`: When [condition 1]
- `[tool-b]`: When [condition 2]
- `[tool-c]`: When [condition 3]
3. Execute with selected tool
4. Explain choice to user
Testing Protocol
After generating a skill, guide the user through validation:
Trigger Testing
Should trigger on:
- "[Primary trigger phrase]"
- "[Alternate phrasing]"
- "[Paraphrased request]"
Should NOT trigger on:
- "[Unrelated topic]"
- "[Different domain]"
Functional Testing
- "Try this skill with: "[example request]""
- Observe: Does it follow the workflow?
- Verify: Are steps in correct order?
- Check: Are tool calls correct?
Iteration Loop
If issues found:
- Identify specific failure point
- Adjust instructions for that step
- Re-test with same example
- Expand test cases
Error Handling for the Skill Builder
When the skill-builder encounters issues:
| Error | Response |
|---|---|
| Unclear user requirements | "I want to make sure I understand correctly. Can you tell me more about [specific aspect]?" |
| Contradictory requirements | "You mentioned both [A] and [B], which seem conflicting. How should I prioritize?" |
| Missing tool information | "You mentioned needing [tool], but I don't have access to it. Is this an MCP server you have connected, or should I use built-in capabilities?" |
| Scope too large | "This is a substantial workflow! Would you like to start with a Minimum Viable Skill covering [core steps], then add complexity later?" |
| Vague success criteria | "How will you know the skill succeeded? What does the ideal output look like? Can you give me a concrete example?" |
Best Practices Summary
Do
- ✅ Ask clarifying questions before generating
- ✅ Use progressive disclosure (frontmatter → body → references)
- ✅ Include specific trigger phrases users would actually say
- ✅ Provide concrete examples with expected outputs
- ✅ Add error handling for common failure modes
- ✅ Validate YAML syntax before finalizing
- ✅ Test with real user requests
- ✅ Iterate based on feedback
Don't
- ❌ Generate without understanding the use case
- ❌ Use vague descriptions like "helps with tasks"
- ❌ Skip the trigger phrases (critical for activation)
- ❌ Make instructions too long—use references/
- ❌ Forget to handle errors
- ❌ Create skills without user confirmation
- ❌ Skip testing before deployment
Output Artifacts
This skill generates:
- Skill Folder - Complete structure with all directories
- SKILL.md - With YAML frontmatter and Markdown instructions
- scripts/ - Utility scripts for validation/processing
- references/ - Additional documentation for progressive disclosure
- assets/ - Templates and style guides (if needed)
- Testing Guide - Recommended test cases
- Installation Instructions - For the user
Example: Complete Skill Generation
User: "I want a skill that helps me write release notes from Git commits."
Assistant: "Great! Let me understand your needs better."
Discovery Q1: "What format do you want for release notes? (bullet points, sections by type, detailed changelog)"
User: "Bullet points grouped by type: Features, Bug Fixes, Improvements."
Discovery Q2: "How will you provide the commits? Pulling from GitHub MCP, uploading a file, or pasting them?"
User: "GitHub MCP—my team has it connected."
Assistant: "Perfect. This is a MCP Enhancement skill. Here's what I'm planning:"
release-notes-generator/
├── SKILL.md
├── scripts/
│ └── categorize-commits.py
└── references/
└── commit-patterns.md
Assistant: "Triggers would include: 'write release notes', 'generate changelog', 'summarize commits'. Does this fit?"
User: "Yes, and add 'create release notes' as a trigger."
Assistant: "Generating your skill..."
[Creates files]
Assistant: "✅ Your skill is ready! Test with: 'Write release notes for PR #123'
Want me to add anything—perhaps support for custom category names or emoji formatting?"
Troubleshooting
Issue: User provides vague requirements Solution: Use the discovery questions systematically. Don't proceed until you have clear answers.
Issue: Scope creep—user keeps adding features Solution: "Let's build a version 1.0 with core functionality first, then we can add [feature] in a future iteration. Sound good?"
Issue: User doesn't know trigger phrases Solution: Suggest 3-5 based on their use case, then ask: "Would you use any of these phrases? What would YOU say?"
Issue: Skill generates but won't activate Solution: Check:
- Description includes specific trigger phrases
- No XML tags in frontmatter
- Name is kebab-case
- SKILL.md is exact filename
Metadata for Version Tracking
metadata:
author: AI Assistant (generated)
version: 1.0.0
created: auto-timestamp
last-updated: auto-timestamp
category: document-creation # or workflow-automation / mcp-enhancement
complexity: low # low / medium / high
estimated-minutes: 15-30
Quick Reference for Agent
When using this skill to build another skill:
- Ask the 5 discovery questions first
- Recommend category based on answers
- Present use case template for confirmation
- Generate folder structure
- Write YAML frontmatter (name + description + triggers)
- Write SKILL.md body (instructions + examples + troubleshooting)
- Create supporting files (scripts/, references/, assets/)
- Validate all files exist and are properly formatted
- Guide user through testing
- Offer iteration based on feedback
Remember: Progressive disclosure is key. Frontmatter should be brief—keep detailed docs in references/
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