The Excellence of Claude’s Capabilities: Potential to Surpass MCP

# Anthropic Introduces Claude Skills: Modular AI Capabilities
This morning, Anthropic [introduced Claude Skills](https://www.anthropic.com/news/skills), a framework that enables their models to gain **specialized abilities**:
> Claude can now use *Skills* — folders containing instructions, scripts, and resources — to improve performance on specific tasks.
> Skills are only accessed when relevant to the current task, making Claude more effective at specialized operations such as working with Excel spreadsheets or following brand guidelines.
More details can be found in Anthropic’s engineering blog ([Equipping agents for the real world](https://www.anthropic.com/engineering/equipping-agents-for-the-real-world-with-agent-skills)) and their new [GitHub repository](https://github.com/anthropics/skills).
*(I previously previewed this functionality when I reverse‑engineered it and [wrote about it last Friday](https://simonwillison.net/2025/Oct/10/claude-skills/).)*
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## What Are Claude Skills?
Structurally, a **Skill** is:
- A Markdown file describing how to accomplish a task
- Optional supporting documents and pre‑written scripts that the model can execute
Notably, Claude’s recent [document creation features](https://www.anthropic.com/news/create-files) — launched alongside [their code interpreter](https://simonwillison.net/2025/Sep/9/claude-code-interpreter) — are fully implemented as Skills. Anthropic’s repository now includes [document-skills](https://github.com/anthropics/skills/tree/main/document-skills) for formats like `.pdf`, `.docx`, `.xlsx`, and `.pptx`.
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## Token Efficiency
A defining advantage:
At the start of a session, Claude’s harness scans available Skills and reads their brief descriptions from frontmatter YAML.
- **Highly token‑efficient** — a few dozen tokens per Skill
- Full details are only loaded when a relevant task is triggered
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## Broader Implications for AI Workflows
Modular enhancements like Skills make it easier to integrate **specialized workflows** into AI usage. For example, platforms such as [AiToEarn官网](https://aitoearn.ai/) offer open‑source ecosystems for AI content monetization — connecting generation, cross‑platform publishing (Douyin, Kwai, WeChat, Bilibili, Rednote, Facebook, Instagram, LinkedIn, Threads, YouTube, Pinterest, and X), analytics, and model ranking.
---
### Related Links
- [Trying out the slack-gif-creator skill](https://simonwillison.net/2025/Oct/16/claude-skills/#trying-out-the-slack-gif-creator-skill)
- [Skills depend on a coding environment](https://simonwillison.net/2025/Oct/16/claude-skills/#skills-depend-on-a-coding-environment)
- [Claude Code as a General Agent](https://simonwillison.net/2025/Oct/16/claude-skills/#claude-as-a-general-agent)
- [Skills compared to MCP](https://simonwillison.net/2025/Oct/16/claude-skills/#skills-compared-to-mcp)
- [Here come the Skills](https://simonwillison.net/2025/Oct/16/claude-skills/#here-come-the-skills)
- [The simplicity is the point](https://simonwillison.net/2025/Oct/16/claude-skills/#the-simplicity-is-the-point)
---
## Trying Out the `slack-gif-creator` Skill
Example metadata from Anthropic’s [`slack-gif-creator` Skill](https://github.com/anthropics/skills/blob/main/slack-gif-creator/SKILL.md):
> Toolkit for creating animated GIFs optimized for Slack, with validators for size constraints and composable animation primitives.
> Activated when users request animated GIFs or emoji animations for Slack (e.g., “make me a GIF for Slack of X doing Y”).
### AI in Multi‑Platform Creative Ecosystems
This lightweight, metadata‑driven pattern parallels workflows in open ecosystems such as [AiToEarn官网](https://aitoearn.ai/), which integrate AI capabilities into publishing pipelines — enabling instant distribution across Douyin, Kwai, Bilibili, Instagram, YouTube, and X.
---
### Example Use
Steps taken in Claude’s mobile web app (Sonnet 4.5):
1. **Enable Skill**
Via [settings](https://claude.ai/settings/capabilities), turned on `slack-gif-creator`.
2. **Prompt**
> Make me a gif for Slack about how Skills are way cooler than MCPs
3. **Output**
Claude generated a [GIF](https://claude.ai/share/eff7ae7b-b386-417b-9fa0-213fa76ace6e).

---
### Example Python Snippet
Add skill directory to Python path
import sys
sys.path.insert(0, '/mnt/skills/examples/slack-gif-creator')
from PIL import Image, ImageDraw, ImageFont
from core.gif_builder import GIFBuilder
... build the GIF ...
Save output
info = builder.save('/mnt/user-data/outputs/skills_vs_mcps.gif',
num_colors=128,
optimize_for_emoji=False)
print(f"GIF created successfully!")
print(f"Size: {info['size_kb']:.1f} KB ({info['size_mb']:.2f} MB)")
print(f"Frames: {info['frame_count']}")
print(f"Duration: {info['duration_seconds']:.1f}s")
Size validation example:
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)")
---
## Skills Depend on a Coding Environment
**Requirements:**
- Filesystem access
- Tools to navigate it
- Ability to execute commands
This mirrors environments like **ChatGPT Code Interpreter** (2023) and local coding agent tools such as Cursor, Claude Code, Codex CLI, and Gemini CLI.
The dependency differentiates Skills from MCP and ChatGPT Plugins — unlocking significant capability but raising **sandboxing and security** concerns (e.g., prompt injection mitigation).
---
## Claude Code as a General Agent
Initially underestimated, “agents” have flourished in 2025.
[Claude Code](https://www.claude.com/product/claude-code) is better described as a **general agent** — able to automate anything executable via shell commands.
**Example: Data Journalism Agent Skills Collection**
- Sourcing and interpreting US census data
- Ingesting multi‑format data into SQLite or DuckDB with Python
- Publishing datasets as Parquet in S3 or tables in Datasette Cloud
- Building D3 visualizations
Simple folders of Markdown plus scripts can turn into **domain‑specific agents**.
---
## Skills Compared to MCP
[Model Context Protocol](https://modelcontextprotocol.io/) (MCP) gained traction in late 2024 but suffers from **high token consumption** — sometimes tens of thousands for context in GitHub’s implementation.
CLI‑based coding agents are often lighter; LLMs can run `cli-tool --help` when needed instead of loading heavy context.
**Skills** offer the same advantage without building new CLI tools — just a Markdown file and scripts when useful.
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## Here Come the Skills
- **Easy to share** — can be single files or small multi‑component folders
- Anthropic provides an [Agent Skills overview](https://docs.claude.com/en/docs/agents-and-tools/agent-skills/overview) and a [Claude Skills Cookbook](https://github.com/anthropics/claude-cookbooks/tree/main/skills)
- Model‑agnostic — Codex CLI or Gemini CLI can read any Skill folder and execute tasks accordingly
**Prediction:** rapid community growth and a flood of shared Skills.
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## The Simplicity Is the Point
Critics say Skills are “too simple.”
That’s the strength: YAML metadata + Markdown instructions + optional scripts.
Compared to MCP’s complex protocol layers, Skills **put text first** and delegate complexity to the model and environment.
This simplicity aligns with open publishing systems like [AiToEarn](https://aitoearn.ai/) — strip processes to essentials, connect tools, and let AI handle the work.
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