Eric J Ma's Website

I spent time digging through Anthropic's skills repository. These are my first impressions, organized for clarity and future reference.

What the Anthropic Skills repository offers

• Creative & design workflows: algorithmic-art (generative art with p5.js), canvas-design (beautiful PNG/PDF outputs guided by design philosophies), theme-factory (pre-set or on-the-fly themes), and slack-gif-creator (animated GIFs tuned for Slack). These are turnkey “taste plus tooling” bundles that let the model produce high-quality visuals with consistent aesthetics.
• Document skills for real formats: document-skills/ cover pptx, docx, pdf, and xlsx with serious capabilities: layout/templates, tracked changes and comments, text/table extraction, merges/splits, charting, formulas, and formatting preservation. This feels like a pragmatic spec+runtime for working with binary formats—lean instructions up front, heavy lifting when needed.
• Development & technical utilities: artifacts-builder (compose complex Claude HTML artifacts using React/Tailwind/shadcn), webapp-testing (Playwright-driven UI testing), and mcp-builder (guidance for creating high-quality MCP servers). These reduce boilerplate for the “build and test” loop.
• Enterprise & communication: brand-guidelines (apply Anthropic’s official brand colors and typography) and internal-comms (status reports, newsletters, FAQs). These encode editorial and brand guardrails so outputs stay on-message.
• Meta skills and templates: skill-creator and template-skill show how to structure your own skills: a folder per skill with a SKILL.md (YAML front matter for name and description, plus instructions/examples/guidelines), optional scripts, and assets. This is the pattern to replicate.

If you want the source for these examples, it’s viewable in the repo. Start here: https://github.com/anthropics/skills.

How skills are loaded and used

• Minimal prompt footprint: A skill's short description is passed up front. The larger skill.md is only read when the model decides it needs more detail.
• On-demand details: The model can iterate (ReAct loop) to fetch instructions and then execute scripts or read additional files.

This access pattern keeps the initial token budget small and defers detail until it’s actually needed.

Contrast with MCP servers

• MCP call shape: Tool names and descriptions are typically sent on every call. That keeps tools globally discoverable but increases token overhead.
• Skills call shape: A tiny descriptor up front; details fetched lazily. Lower baseline token cost.
• Distribution model:
  • MCP: Centrally hostable (e.g. web server) or vendable (e.g., a Python package). Easy to version, release, and update for many users at once.
  • Skills: Feel local-first. You can drag-and-drop into a Claude workspace. Easy to customize, but harder to standardize and propagate updates across a team.

Given current industry patterns, MCP servers are the widely accepted way to expose functionality to LLMs across tools and vendors. Skills are Anthropic-specific at the moment.

Token efficiency (and why it’s emphasized)

Anthropic’s materials lean into token efficiency. The cost of LLM calls adds up, and repeatedly sending long tool descriptions can be expensive. Skills reduce baseline tokens: spend a handful of tokens to register intent, read detail only when needed, then execute. That’s the economic story.

Practical trade-offs

• Standardization vs customization:
  • MCP servers: Strong for shared, versioned, and centrally updated capabilities.
  • Skills: Great for rapid, local customization without infrastructure.
• Discovery vs cost:
  • MCP: High discoverability; the model always sees the tools. Higher token floor.
  • Skills: Low token floor; details fetched when needed. Requires the model to choose to read more.

Open questions I’m tracking

• How will teams distribute and update skills at scale without a central registry or packaging story?
• Will skills gain cross-vendor support, or remain Anthropic-only?
• What’s the best practice to map a complex skill into smaller, composable units without losing clarity?

Early take

IMO, skills are a clear attempt to lower token costs and streamline task-specific workflows with minimal upfront context. MCP servers remain the well-understood, cross-ecosystem pattern for exposing capabilities. If your goal is a shareable, versioned interface for many users, MCP is still the safer default. If you need quick, local customization inside Claude with a lean prompt footprint, skills are compelling. But this field has been evolving at breawkneck speed anyways, so expect changes.