Skill Best Practices

Skill Best Practices

Reference: https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf

Instructions

Step 1: Identify your use case category

Determine which type of skill you're building:

• Document & Asset Creation — consistent output (docs, designs, code)
• Workflow Automation — multi-step processes with consistent methodology
• MCP Enhancement — workflow guidance on top of MCP tool access

Define 2–3 concrete use cases before writing anything (see Planning section below).

Step 2: Create the folder and SKILL.md

• Name the folder in kebab-case (e.g. my-skill-name)
• Create exactly SKILL.md (case-sensitive) inside it
• Write YAML frontmatter with name and description (see Technical requirements)

Step 3: Write the description — this is the most critical part

The description controls when Claude loads your skill. It must include:

• WHAT the skill does
• WHEN to use it (specific trigger phrases)
• Optional: negative triggers ("Do NOT use for...")

See "Writing effective descriptions" for good/bad examples.

Step 4: Write the body instructions

Follow the recommended template: ## Instructions → numbered steps → ## Examples → ## Troubleshooting. Be specific and actionable. Move detailed docs to references/ and link to them.

Step 5: Update CLAUDE.md and README.md

After creating or modifying any skill in this repository, always update the skill tables in both files:

• CLAUDE.md — skill table under "Included Skills" (Trigger column: one-line description of when it fires)
• README.md — skill table under "Enthaltene Skills" (Beschreibung column: German one-liner)

Both files must stay in sync. This step is mandatory and must not be skipped.

Also invoke the cc-best-practices skill when working on skills in this repository to ensure context and session management follow project standards.

Step 6: Validate YAML and skills CLI compatibility

Run the validation script from the repository root before testing or committing:

bash .claude/skills/skill-best-practices/scripts/validate-skills.sh

Fix any FAIL lines before continuing. Common issues:

• description uses block scalar (> or |) → replace with a quoted single-line string
• Sub-keys under a parent mapping key not indented → add two-space indent

After pushing, also run the remote check to confirm npx skills add --list finds all skills:

bash .claude/skills/skill-best-practices/scripts/validate-skills.sh --remote

Step 7: Test triggering and functional behavior

Run 10–20 test queries. Target: skill triggers on ~90% of relevant queries and never on unrelated topics. Iterate on the description until triggering is reliable (see Testing approach).

Step 7: Iterate based on signals

• Undertriggering → add more trigger phrases to description
• Overtriggering → add negative triggers, narrow scope
• Instructions ignored → move critical steps to top, use explicit language

Examples

Example 1: Building a new skill from scratch

User says: "Help me create a skill that plans sprints in Linear"

Actions:

1. Identify category: Workflow Automation + MCP Enhancement
2. Define use case: trigger = "plan sprint", "create sprint tasks"; steps = fetch Linear status → analyze velocity → create tasks
3. Create folder linear-sprint-planner/SKILL.md
4. Write description: "Manages Linear sprint planning workflows. Use when user says 'plan sprint', 'create sprint tasks', or 'set up iteration'."
5. Write step-by-step instructions with Linear MCP tool calls
6. Test with 10 trigger phrases; adjust description if skill doesn't auto-load

Result: Functional skill that auto-triggers on sprint planning requests and executes the full workflow without user re-explaining the steps each time.

Example 2: Reviewing an existing skill

User says: "Review my SKILL.md and suggest improvements"

Actions:

1. Read the SKILL.md frontmatter — check name (kebab-case?), description (WHAT + WHEN? under 1024 chars? trigger phrases present?)
2. Check body — is it under 5,000 words? Are instructions specific and actionable? Is there a Troubleshooting section? Examples?
3. Simulate triggering — would the description cause Claude to load this skill for the right queries?
4. Report findings as: PASS / WARN / FAIL per criterion

Result: Prioritized list of improvements with specific fixes for each issue.

Example 3: Troubleshooting a skill that doesn't trigger

User says: "My skill never loads automatically, I always have to invoke it manually"

Actions:

1. Read the description field — is it too generic? ("Helps with projects" won't work)
2. Check for missing trigger phrases — does it include words users would actually say?
3. Ask Claude: "When would you use the [skill name] skill?" — Claude quotes the description back; gaps become obvious
4. Rewrite description to add specific trigger phrases and retest

Result: Updated description with concrete triggers; skill auto-loads on relevant queries.

What is a skill?

A skill is a folder containing:

• SKILL.md (required): Instructions in Markdown with YAML frontmatter
• scripts/ (optional): Executable code (Python, Bash, etc.)
• references/ (optional): Documentation loaded as needed
• assets/ (optional): Templates, fonts, icons used in output

Core design principles

Progressive Disclosure — three levels:

1. YAML frontmatter: always in system prompt; tells Claude when to load the skill
2. SKILL.md body: loaded when relevant; full instructions
3. Linked files in references/: loaded on demand

Composability — skills work alongside others; don't assume exclusivity.

Portability — works identically across Claude.ai, Claude Code, and API.

Planning: Start with use cases

Before writing, define 2–3 concrete use cases:

Use Case: <name>
Trigger: User says "<phrase>" or "<phrase>"
Steps:
  1. ...
  2. ...
Result: <expected outcome>

Ask yourself:

• What does the user want to accomplish?
• What multi-step workflow is required?
• Which tools are needed (built-in or MCP)?
• What domain knowledge should be embedded?

Three skill categories

Technical requirements

File & folder naming

• Folder: kebab-case only (notion-project-setup) — no spaces, underscores, or capitals
• File: exactly SKILL.md (case-sensitive) — no variations
• No README.md inside the skill folder (put docs in SKILL.md or references/)

YAML frontmatter

Minimal required format:

---
name: your-skill-name
description: What it does. Use when user asks to [specific phrases].
---

name (required):

• kebab-case, no spaces or capitals
• Must match folder name

description (required):

• MUST include BOTH: what the skill does AND when to use it (trigger conditions)
• Under 1024 characters
• No XML tags (< or >)
• Include specific trigger phrases users would actually say
• Mention file types if relevant

Optional fields:

license: MIT
compatibility: "Requires Python 3.10+"
metadata:
  author: Your Name
  version: 1.0.0
  mcp-server: server-name

Security restrictions — forbidden in frontmatter:

• XML angle brackets (< >)
• Names containing "claude" or "anthropic" (reserved)

Writing effective descriptions

Structure: [What it does] + [When to use it] + [Key capabilities]

Good examples:

# Specific and actionable
description: Analyzes Figma design files and generates developer handoff docs.
  Use when user uploads .fig files, asks for "design specs", "component
  documentation", or "design-to-code handoff".

# Includes trigger phrases
description: Manages Linear project workflows including sprint planning and
  task creation. Use when user mentions "sprint", "Linear tasks", or asks
  to "create tickets".

Bad examples:

# Too vague
description: Helps with projects.

# Missing triggers
description: Creates sophisticated multi-page documentation systems.

# Too technical, no user triggers
description: Implements the Project entity model with hierarchical relationships.

Writing instructions (SKILL.md body)

Recommended structure:

# Your Skill Name

## Instructions

### Step 1: [First Major Step]

Clear explanation of what happens.

### Step 2: ...

## Examples

### Example 1: [Common scenario]

User says: "..."
Actions:

1. ...
   Result: ...

## Troubleshooting

### Error: [Common error message]

**Cause:** Why it happens
**Solution:** How to fix

Best practices for instructions

Be specific and actionable:

# Good
Run `python scripts/validate.py --input {filename}` to check data format.
If validation fails, common issues:
- Missing required fields (add to CSV)
- Invalid date formats (use YYYY-MM-DD)

# Bad
Validate the data before proceeding.

Include error handling — document common errors with cause and solution.

Reference bundled resources clearly:

Before writing queries, consult `references/api-patterns.md` for:
- Rate limiting guidance
- Pagination patterns

Use progressive disclosure — keep SKILL.md focused on core instructions; move detailed docs to references/ and link to them. Keep SKILL.md under 5,000 words.

For critical validations, prefer a bundled script over language instructions — code is deterministic, language interpretation isn't.

Testing approach

1. Triggering tests

Run 10–20 queries. Skill should trigger on ~90% of relevant queries and NOT trigger on unrelated topics.

Should trigger:
- "Help me set up a new ProjectHub workspace"
- "I need to create a project in ProjectHub"

Should NOT trigger:
- "What's the weather?"
- "Help me write Python code"

Debugging: Ask Claude "When would you use the [skill name] skill?" — it will quote the description back.

2. Functional tests

• Valid outputs generated
• API calls succeed
• Error handling works
• Edge cases covered

3. Performance comparison

Compare token count, tool calls, and back-and-forth messages with vs. without the skill.

Pro tip: Iterate on a single challenging task until Claude succeeds, then extract the winning approach into a skill.

Troubleshooting

Skill won't upload

Skill doesn't trigger (undertriggering)

• Description too generic
• Missing trigger phrases users actually say
• Missing relevant file type mentions

Fix: Add more specific keywords and phrases to the description.

Skill triggers too often (overtriggering)

Add negative triggers and narrow the scope:

description: Advanced data analysis for CSV files. Use for statistical modeling,
  regression, clustering. Do NOT use for simple data exploration.

Instructions not followed

1. Too verbose — keep concise, use bullet points, move details to references/
2. Instructions buried — put critical instructions at top, use ## Critical headers
3. Ambiguous language — be explicit: "CRITICAL: Before calling X, verify: ..."
4. Model laziness — add to user prompts (more effective than SKILL.md): "Take your time, quality over speed, do not skip validation steps"

Large context / slow responses

• Move detailed docs to references/
• Keep SKILL.md under 5,000 words
• Reduce simultaneous enabled skills (evaluate if you have more than 20–50)

Workflow patterns

Five patterns cover most skill types: Sequential orchestration, Multi-MCP coordination, Iterative refinement, Context-aware tool selection, and Domain-specific intelligence.

For detailed examples and implementation templates for each pattern, consult references/patterns.md.

Quick checklist

Before you start:

• Identified 2–3 concrete use cases
• Tools identified (built-in or MCP)
• Planned folder structure

During development:

• Folder named in kebab-case
• SKILL.md exists (exact spelling, case-sensitive)
• YAML frontmatter has --- delimiters
• name: kebab-case, no spaces, no capitals
• description includes WHAT and WHEN
• No XML tags (< >) anywhere
• Instructions clear and actionable
• Error handling included
• Examples provided
• References clearly linked

Repository sync (mandatory for this repo):

• CLAUDE.md skill table updated
• README.md skill table updated
• cc-best-practices skill was loaded during this session
• validate-skills.sh run — no FAIL lines
• After push: validate-skills.sh --remote run — all skills found by npx skills

Before upload:

• Triggers on obvious tasks
• Triggers on paraphrased requests
• Does NOT trigger on unrelated topics
• Functional tests pass

After upload:

• Test in real conversations
• Monitor for under/over-triggering
• Iterate on description and instructions