What Are Claude Skills and Why Do They Matter?
Claude Skills represent a breakthrough approach to extending AI capabilities through simple Markdown files containing instructions, scripts, and resources that Claude loads only when needed for specific tasks. Unlike the complex Model Context Protocol (MCP) that requires extensive protocol specifications covering hosts, clients, servers, and multiple transports, Skills operate on an elegantly simple principle: drop a Markdown file with YAML metadata and optional scripts into a folder, and Claude automatically knows how to use it. This token-efficient design means each skill consumes only dozens of tokens rather than the tens of thousands required by protocols like GitHub's MCP implementation. Tech expert Simon Willison argues that Skills may be “a bigger deal than MCP” precisely because of this simplicity—they harness LLMs' natural ability to understand text instructions while relying on coding environments to handle execution, making them far easier to create, share, and implement than complex protocol-based solutions.
The Fundamental Concept: Simplicity as Power
What Makes a Skill
Skills are conceptually straightforward, which is precisely their strength:
Core Component: A Markdown file (SKILL.md) containing instructions explaining how to perform specific tasks
Optional Additions:
- Supporting documents providing reference information
- Pre-written scripts the model can execute to accomplish tasks
- Example code demonstrating best practices
- Resource files necessary for task completion
Frontmatter Metadata: Brief YAML descriptions that allow Claude to understand when each skill is relevant without loading the full content
This architecture achieves remarkable token efficiency. At session start, Claude scans available skills and reads only the short frontmatter explanations—consuming just a few dozen tokens per skill. Full details load only when users request tasks requiring that specific skill.
The Token Efficiency Advantage
Token consumption has emerged as one of MCP's most significant limitations. GitHub's official MCP implementation alone famously consumes tens of thousands of context tokens. Add a few more MCPs, and little space remains for the LLM to perform useful work.
Skills solve this problem elegantly:
- Initial Load: Dozens of tokens per skill (frontmatter only)
- Active Use: Full content loads only when needed
- Scalability: Hundreds of skills can exist without consuming significant context
- Progressive Disclosure: Information appears precisely when relevant
Real-World Example: The Slack GIF Creator Skill
Metadata and Discovery
Anthropic's slack-gif-creator skill demonstrates the system in action. Its frontmatter reads:
“Toolkit for creating animated GIFs optimized for Slack, with validators for size constraints and composable animation primitives. This skill applies when users request animated GIFs or emoji animations for Slack from descriptions like ‘make me a GIF for Slack of X doing Y'.”
This concise description enables Claude to recognize when the skill is relevant without consuming significant tokens.
Practical Testing
Simon Willison tested this skill in the Claude mobile web app using Sonnet 4.5:
Setup:
- Enabled slack-gif-creator skill in Claude settings
- Issued prompt: “Make me a gif for slack about how Skills are way cooler than MCPs”
Result: Claude generated a complete animated GIF (admittedly “almost epilepsy inducing” according to Willison, but functional)
Generated Code Analysis
The Python script Claude wrote demonstrates several clever patterns:
# Skill directory added to Python path for imports
import sys
sys.path.insert(0, '/mnt/skills/examples/slack-gif-creator')
from PIL import Image, ImageDraw, ImageFont
# Leveraging skill's pre-built utilities
from core.gif_builder import GIFBuilder
# ... GIF construction code ...
# Saving with optimization
info = builder.save('/mnt/user-data/outputs/skills_vs_mcps.gif',
num_colors=128,
optimize_for_emoji=False)
# Built-in validation for Slack's 2MB limit
passes, check_info = check_slack_size('/mnt/user-data/outputs/skills_vs_mcps.gif',
is_emoji=False)
if passes:
print("✓ Ready for Slack!")
else:
print(f"⚠ File size: {check_info['size_kb']:.1f} KB (limit: {check_info['limit_kb']} KB)")
Key Insights:
- Skills can include helper libraries and utilities
- Built-in validation functions enable iterative improvement
- If initial output exceeds size limits, the model can automatically retry with different parameters
The Critical Dependency: Coding Environments
Why Skills Need Execution Environments
The skills mechanism operates entirely dependent on the model having:
- Access to a filesystem for reading skill files
- Tools to navigate directory structures
- Ability to execute commands and scripts
- Writable output locations for generated files
This requirement represents the biggest difference between Skills and previous LLM capability expansion attempts like MCP and ChatGPT Plugins.
Historical Context
This pattern has precedents:
- ChatGPT Code Interpreter (early 2023): First major example of giving LLMs filesystem and execution access
- Cursor, Claude Code, Codex CLI, Gemini CLI: Extended the pattern to local machines
The Power-Safety Tradeoff
The coding environment dependency is significant, but it unlocks enormous new capabilities. However, this creates urgent security challenges:
The Safety Imperative: We need robust sandboxing mechanisms ensuring that attacks like prompt injections are limited to acceptable damage levels.
The word “safe” in “safe coding environments” does heavy lifting. Without proper isolation, malicious skills or compromised prompts could cause serious harm.
Claude Code: The Misnamed General Agent
Beyond Coding: General Computer Automation
Simon Willison argues that Claude Code suffers from poor naming. It's not merely a coding tool—it's a general agent capable of automating anything achievable by typing commands into a computer.
Skills make this reality obvious and explicit. With appropriate skills, Claude Code can:
- Analyze census data and identify newsworthy patterns
- Scrape websites and extract structured information
- Generate reports combining multiple data sources
- Publish visualizations and interactive dashboards
- Automate complex multi-step workflows
Data Journalism Example
Consider a skill collection for data journalism:
Census Data Skill: Instructions on accessing US census data APIs and understanding data structure
Data Loading Skill: Guidance on importing various formats into SQLite or DuckDB using Python libraries
Publishing Skill: Methods for publishing data as Parquet files in S3 or tables on Datasette Cloud
Story Finding Skill: Experienced reporter advice on discovering interesting narratives in datasets
Visualization Skill: Best practices for creating clean, readable D3 visualizations
Result: A “data journalism agent” capable of discovering and publishing stories from fresh census data drops—built entirely from Markdown files and example scripts.
The potential applications are dizzying. Any domain-specific workflow can be captured in skills and immediately leveraged by the AI.
Skills vs. MCP: A Philosophical Divide
The MCP Complexity Problem
Model Context Protocol attracted enormous buzz since its November 2024 release. Every company recognized they needed an “AI strategy,” and building or announcing MCP implementation provided an easy checkbox.
However, limitations have emerged:
Token Consumption: GitHub's official MCP alone consumes tens of thousands of tokens, leaving little room for actual work
Complexity Overhead: MCP is a comprehensive protocol specification covering:
- Hosts and clients
- Servers and resources
- Prompts and tools
- Sampling mechanisms
- Multiple transports (stdio, streamable HTTP, SSE)
- Roots and elicitation patterns
Implementation Burden: Each MCP server requires significant engineering effort to build and maintain
The CLI Tool Alternative
Willison's interest in MCPs waned after embracing coding agents seriously. Almost everything achievable with an MCP can be handled by a CLI tool instead.
Key Insight: LLMs already know how to call cli-tool --help, meaning you don't spend many tokens describing usage—the model figures it out when needed.
Skills extend this advantage: no need to implement new CLI tools. Drop a Markdown file describing the task, add helper scripts only if they improve reliability or efficiency.
Why Simplicity Wins
Some observers dismiss Skills as “so simple they're hardly a feature at all.” This criticism misses the point entirely.
Established Patterns: Yes, people have experimented with dropping instructions into Markdown files. AGENTS.md is a well-established pattern.
The Core Difference: Anthropic formalized the pattern, optimized it for token efficiency through progressive disclosure, and created an ecosystem around it.
The Spirit of LLMs: Skills feel closer to how LLMs actually work—throw in text and let the model figure it out, rather than requiring rigid protocol adherence.
They outsource hard parts to the LLM harness and computing environment. Given everything learned about LLMs' tool-running abilities over recent years, this represents a sensible strategy.
The Coming Cambrian Explosion
Easy to Create and Share
One of Skills' most exciting aspects is how easily they can be shared:
Simple Skills: Implemented as a single Markdown file Complex Skills: A folder containing a few files Distribution: GitHub repos, documentation sites, community marketplaces
Official Resources
Anthropic provides comprehensive support:
Document Creation Skills
Claude's document creation abilities—announced alongside the September code interpreter feature—are entirely implemented using Skills. These are now publicly available:
- PDF Skill: Creating and manipulating PDF documents
- DOCX Skill: Microsoft Word document generation
- XLSX Skill: Excel spreadsheet creation
- PPTX Skill: PowerPoint presentation development
These skills demonstrate production-quality implementations that others can learn from and adapt.
Model-Agnostic Design
Nothing prevents Skills from working with other models. You can:
- Grab a skills folder
- Point Codex CLI or Gemini CLI at it
- Say “read pdf/SKILL.md and create me a PDF describing this project”
It will work despite those tools having no baked-in knowledge of the skills system. The Markdown instructions are sufficiently clear that any capable LLM can follow them.
Prediction: Surpassing MCP Adoption
Willison expects “a Cambrian explosion in Skills which will make this year's MCP rush look pedestrian by comparison.”
Factors Driving Adoption:
- Dramatically lower creation barrier
- No protocol specification to learn
- Works across different AI platforms
- Easy to iterate and improve
- Natural fit with how LLMs process information
Practical Applications Across Domains
Software Development
Plugin Development: Skills describing how to build plugins for specific platforms (e.g., Datasette plugins)
Code Review: Best practices for reviewing pull requests in your organization's style
Testing Strategies: Comprehensive testing approaches for different code types
Deployment Procedures: Step-by-step deployment workflows with rollback procedures
Content Creation
Brand Guidelines: Company-specific style guides and voice requirements
SEO Optimization: Instructions for creating search-optimized content
Social Media: Platform-specific content formatting and best practices
Translation: Consistent translation approaches maintaining brand voice
تحليل البيانات
Statistical Methods: When and how to apply different statistical tests
Visualization Standards: Creating publication-ready charts and graphs
Data Cleaning: Common patterns for handling messy datasets
Report Generation: Structured approaches to presenting findings
Business Operations
Email Management: Sorting, prioritizing, and drafting responses
Meeting Scheduling: Considering timezone preferences and availability patterns
Expense Reporting: Company-specific categorization and approval workflows
Onboarding: Step-by-step guides for bringing new team members up to speed
Looking Forward: The Skills Ecosystem
Community Development
The Skills ecosystem will likely evolve through:
Public Marketplaces: Searchable repositories of community-contributed skills
Domain Specialists: Experts packaging professional knowledge into shareable skills
Enterprise Collections: Organizations developing proprietary skill libraries
Quality Standards: Emerging best practices for skill documentation and testing
Integration with Existing Tools
Skills will increasingly integrate with:
- Version control systems for skill management
- CI/CD pipelines for automated testing
- Documentation platforms for discovery and browsing
- Package managers for easy installation
The Agent Revolution Continues
Simon Willison admits his January 2025 prediction that “agents would once again fail to happen” was completely wrong. 2025 has genuinely been the year of agents, regardless of which definition you adopt.
Skills make the agent revolution more accessible by removing technical barriers. Anyone who can write clear instructions in Markdown can now extend AI capabilities in powerful ways.
Why This Matters: The Bigger Picture
Democratizing AI Capabilities
Skills democratize AI enhancement:
Before: Extending AI required protocol expertise, API knowledge, and significant engineering resources
After: Domain experts can capture their knowledge in Markdown files that immediately benefit AI users
Sustainable Scaling
As AI capabilities expand, sustainable scaling requires:
- Token-efficient architectures
- Progressive information disclosure
- Easy modification and improvement
- Clear separation between capability definition and execution
Skills embody all these principles.
The Power of Standards Without Standardization
Skills achieve something remarkable: standardizing an approach without imposing rigid standards. The pattern is clear enough for ecosystem formation, but flexible enough for creative experimentation.
Conclusion: Simplicity as Revolutionary Force
The most profound innovations often appear deceptively simple in retrospect. Skills exemplify this pattern—a Markdown file, some YAML metadata, optional scripts. Yet this simplicity unlocks enormous potential.
By aligning with how LLMs naturally process information rather than forcing them into complex protocols, Skills may indeed prove bigger than MCP. Time will tell, but the early indicators are compelling:
- Lower creation barriers
- Superior token efficiency
- Model-agnostic design
- Natural extensibility
- Growing ecosystem momentum
As Willison notes, “The core simplicity of the skills design is why I'm so excited about it.” Sometimes the most powerful solutions are the ones that get out of the way and let the underlying technology shine.
The Cambrian explosion has begun. The question isn't whether Skills will transform how we enhance AI capabilities, but how quickly and completely.
