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create-skill

Create or update Claude Code skills following Anthropic's official skill standard. Use when user says "create a skill", "new skill", "write a skill", "update a skill", "skill standard", or any time a new SKILL.md file needs to be created. Also use when reviewing existing skills for standards compliance.

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Skill Creation Guide

Reference for creating skills that follow the Anthropic skill standard (github.com/anthropics/skills).

Skill Anatomy

skill-name/
├── SKILL.md (required)
│   ├── YAML frontmatter (name, description required)
│   └── Markdown instructions (<500 lines)
└── Bundled Resources (optional)
    ├── scripts/    - Executable code for deterministic tasks
    ├── references/ - Docs loaded into context as needed
    └── assets/     - Templates, icons, fonts for outputs

Progressive Disclosure

Skills load in three layers:

  1. Metadata (name + description) - Always in context (~100 words)
  2. SKILL.md body - Loaded when skill triggers (<500 lines)
  3. Bundled resources - Loaded as needed (unlimited size)

Keep SKILL.md under 500 lines. If approaching the limit, move content to references/ with clear pointers about when to read each file.

YAML Frontmatter

Two required fields:

---
name: skill-name
description: What the skill does AND when to trigger it. All "when to use" context goes here, not in the body.
---

Description is the Trigger

The description field is the primary mechanism for skill invocation. Claude tends to undertrigger skills, so descriptions should be slightly pushy.

Weak: "Generate LinkedIn posts for professional audiences."

Strong: "Write high-performing LinkedIn posts. Use when user says 'write a LinkedIn post', 'LinkedIn content', 'post for LinkedIn', 'professional audience post', or wants to share insights, lessons, or updates with a professional network - even if they don't explicitly say LinkedIn."

Include:

  • What the skill does (1 sentence)
  • Explicit trigger phrases users might say
  • Adjacent contexts where the skill applies ("even if they don't explicitly say X")

Body Structure

Every skill body should include these components:

1. Overview (optional, 1-2 paragraphs)

What this skill does and the critical distinction that shapes how it works. Name the book or methodology the skill is grounded in.

2. Workflow (required)

Step-by-step process. Use ### Step N: headings. Each step should be actionable and specific.

3. Anti-Patterns (required)

What NOT to do. Format as Bad/Good pairs so the model can calibrate:

## Anti-Patterns

**1. Descriptive name**
Bad: "example of wrong approach"
Good: "example of correct approach"

4. Quality Checklist (required)

Checkbox list the model uses to verify output before delivering:

## Quality Checklist

- [ ] Check 1
- [ ] Check 2

Writing Style

Explain the "Why" Instead of Bare Directives

The model follows instructions better when it understands the reasoning. Heavy-handed MUSTs without context are less effective than explaining why something matters.

Weak: "Never exceed 5 bullets per slide."

Strong: "Keep slides to 3-5 bullets. Beyond 5, the audience reads instead of listening - the slide competes with the speaker instead of supporting them."

Use Imperative Form

Write instructions as commands: "Open with a personal admission" not "You should open with a personal admission."

Generalize, Don't Overfit

Write instructions that work across varied inputs. Avoid narrow tweaks targeting only specific examples - look for underlying patterns.

Examples Are Valuable

Include concrete examples. Format with Input/Output or Bad/Good pairs. Examples calibrate the model's output better than abstract rules.

Ground Every Skill in a Book or Methodology

Skills grounded in a proven framework (book, methodology, model) are more credible, consistent, and unique. Name the source in the Overview section.

Good examples: Radical Candor (feedback), The Pyramid Principle (exec summaries), SPIN Selling (discovery calls), How Google Tests Software (risk-based testing).

Scaffolding a New Skill

Use init_skill.py to create the folder and register in skills.json in one step:

python3 scripts/init_skill.py <skill-name> --role <role> --description "<one-liner>" --tags "<tag1,tag2>"

Valid roles: pm, engineer, qa, designer, content-creator, devops, shared, leadership, program-delivery-manager, customer-success, recruiter, consultant, sales, marketing, founder, data-scientist, data-engineer, security

This creates skills/by-role/<role>/<skill-name>/SKILL.md and adds an entry to skills.json. Always scaffold first, then write the SKILL.md content.

After writing, validate:

python3 scripts/validate_skill.py

Placeholders (for this repo)

When creating skills for the claude-skills public repo, replace all personal/project references:

ContextPlaceholder
Personal name[your name]
Newsletter or blog[your newsletter]
Website domain[your-domain]
Professional niche[your niche]
Subject area[your topic]
Target audience[your audience]
Company name[your company]
Team name[your team]
Client name[your client]
Customer name[customer name]
Program or project[your program]

Creation Checklist

Before finalizing any skill:

  • Scaffolded with init_skill.py (creates folder + registers in skills.json)
  • YAML frontmatter has name and description
  • Description is pushy with multiple trigger phrases
  • Body is under 500 lines
  • Overview names the book or methodology the skill is grounded in
  • Workflow has clear numbered steps with ### Step N: headings
  • Anti-patterns section exists with Bad/Good pairs
  • Quality checklist exists
  • Directives explain "why", not just "what"
  • Examples are concrete and specific
  • No personal/project-specific content (if for public repo)
  • Placeholder brackets used for any context-specific references
  • Overflow content moved to references/ with clear pointers
  • python3 scripts/validate_skill.py passes

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한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

qa-aman/claude-skills212026년 10월 19일 업데이트

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한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

qa-aman/claude-skills212026년 10월 19일 업데이트

Design statistically rigorous experiments to test data science hypotheses. Use when the user says "design an A/B test", "run an experiment", "test this hypothesis", "is this difference significant", "how many samples do I need", "randomized controlled trial", "causal inference", "uplift test", "holdout group", "significance test", "power calculation", "avoid p-hacking", or needs to determine whether an observed effect is real before making a product or model decision. For lean build-measure-learn product experiments without the statistics, use experiment-design instead.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

qa-aman/claude-skills212026년 10월 19일 업데이트

Write measurable acceptance criteria for requirements or user stories. Use when the user says "write acceptance criteria", "definition of done", "how do we test this", "fit criteria", "given when then", "Gherkin scenarios", "what does done look like", "testable conditions", "how will we know this works" - even if they don't explicitly say "acceptance criteria".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

qa-aman/claude-skills212026년 10월 19일 업데이트

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한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

qa-aman/claude-skills212026년 10월 19일 업데이트

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한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

qa-aman/claude-skills212026년 10월 19일 업데이트

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