Our review
This guide describes a structured process to create a new skill for AI agents, including requirements gathering, API research, structuring, and writing.
Strengths
- Adaptive process that asks targeted questions to clarify needs.
- Decision between simple and complex (router) structure based on scope.
- Integration of external API research via tools like Context7.
- Precise directory and file templates for clear organization.
Limitations
- Requires prior understanding of skill structure.
- May be overly procedural for very simple skills.
- Result quality depends on accuracy of user responses.
Use this workflow when you need to create a new skill for an AI agent, whether simple or complex.
Avoid this workflow if you are modifying an existing skill or need a quick solution without formal structure.
Security analysis
SafeThe skill uses bash commands only to create directories and write files under the user's OpenCode configuration path; there are no destructive operations, no data exfiltration, and no obfuscated payloads.
No concerns found
Examples
I want to create a new skill that helps me review pull requests for code style and potential bugs. It should analyze diffs and suggest improvements.Help me build a skill for deploying Docker containers to AWS ECS with health checks and rollback logic.Create a skill that generates API documentation from Python docstrings and publishes to a GitHub Pages site.Workflow: Create a New Skill
<required_reading> Read these reference files NOW:
- references/recommended-structure.md
- references/skill-structure.md
- references/core-principles.md
- references/use-xml-tags.md </required_reading>
If user provided context (e.g., "build a skill for X"): → Analyze what's stated, what can be inferred, what's unclear → Skip to asking about genuine gaps only
If user just invoked skill without context: → Ask what they want to build
Using question
Ask 2-4 domain-specific questions based on actual gaps. Each question should:
- Have specific options with descriptions
- Focus on scope, complexity, outputs, boundaries
- NOT ask things obvious from context
Example questions:
- "What specific operations should this skill handle?" (with options based on domain)
- "Should this also handle [related thing] or stay focused on [core thing]?"
- "What should the user see when successful?"
Decision Gate
After initial questions, ask: "Ready to proceed with building, or would you like me to ask more questions?"
Options:
- Proceed to building - I have enough context
- Ask more questions - There are more details to clarify
- Let me add details - I want to provide additional context
Step 2: Research Trigger (If External API)
When external service detected, ask using question: "This involves [service name] API. Would you like me to research current endpoints and patterns before building?"
Options:
- Yes, research first - Fetch current documentation for accurate implementation
- No, proceed with general patterns - Use common patterns without specific API research
If research requested:
- Use Context7 MCP to fetch current library documentation
- Or use WebSearch for recent API documentation
- Focus on 2024-2026 sources
- Store findings for use in content generation
Step 3: Decide Structure
Simple skill (single workflow, <200 lines): → Single SKILL.md file with all content
Complex skill (multiple workflows OR domain knowledge): → Router pattern:
skill-name/
├── SKILL.md (router + principles)
├── workflows/ (procedures - FOLLOW)
├── references/ (knowledge - READ)
├── templates/ (output structures - COPY + FILL)
└── scripts/ (reusable code - EXECUTE)
Factors favoring router pattern:
- Multiple distinct user intents (create vs debug vs ship)
- Shared domain knowledge across workflows
- Essential principles that must not be skipped
- Skill likely to grow over time
Consider templates/ when:
- Skill produces consistent output structures (plans, specs, reports)
- Structure matters more than creative generation
Consider scripts/ when:
- Same code runs across invocations (deploy, setup, API calls)
- Operations are error-prone when rewritten each time
See references/recommended-structure.md for templates.
Step 4: Create Directory
mkdir -p ~/.config/opencode/skills/{skill-name}
# If complex:
mkdir -p ~/.config/opencode/skills/{skill-name}/workflows
mkdir -p ~/.config/opencode/skills/{skill-name}/references
# If needed:
mkdir -p ~/.config/opencode/skills/{skill-name}/templates # for output structures
mkdir -p ~/.config/opencode/skills/{skill-name}/scripts # for reusable code
Step 5: Write SKILL.md
Simple skill: Write complete skill file with:
- YAML frontmatter (name, description)
<objective><quick_start>- Content sections with pure XML
<success_criteria>
Complex skill: Write router with:
- YAML frontmatter
<essential_principles>(inline, unavoidable)<intake>(question to ask user)<routing>(maps answers to workflows)<reference_index>and<workflows_index>
Step 6: Write Workflows (if complex)
For each workflow:
<required_reading>
Which references to load for this workflow
</required_reading>
<process>
Step-by-step procedure
</process>
<success_criteria>
How to know this workflow is done
</success_criteria>
Step 7: Write References (if needed)
Domain knowledge that:
- Multiple workflows might need
- Doesn't change based on workflow
- Contains patterns, examples, technical details
Step 8: Validate Structure
Check:
- [ ] YAML frontmatter valid
- [ ] Name matches directory (lowercase-with-hyphens)
- [ ] Description says what it does AND when to use it (third person)
- [ ] No markdown headings (#) in body - use XML tags
- [ ] Required tags present: objective, quick_start, success_criteria
- [ ] All referenced files exist
- [ ] SKILL.md under 500 lines
- [ ] XML tags properly closed
Step 9: Create Slash Command
cat > ~/.config/opencode/commands/{skill-name}.md << 'EOF'
---
description: {Brief description}
argument-hint: [{argument hint}]
allowed-tools: Skill({skill-name})
---
Invoke the {skill-name} skill for: $ARGUMENTS
EOF
Step 10: Test
Invoke the skill and observe:
- Does it ask the right intake question?
- Does it load the right workflow?
- Does the workflow load the right references?
- Does output match expectations?
Iterate based on real usage, not assumptions. </process>
<success_criteria> Skill is complete when:
- [ ] Requirements gathered with appropriate questions
- [ ] API research done if external service involved
- [ ] Directory structure correct
- [ ] SKILL.md has valid frontmatter
- [ ] Essential principles inline (if complex skill)
- [ ] Intake question routes to correct workflow
- [ ] All workflows have required_reading + process + success_criteria
- [ ] References contain reusable domain knowledge
- [ ] Slash command exists and works
- [ ] Tested with real invocation </success_criteria>
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