Anthropic Supercharges Claude with ‘Agent Skills’

By Adam Pease

Anthropic Supercharges Claude with ‘Agent Skills’

Anthropic has significantly upgraded its AI assistant, Claude, by introducing a new framework called Agent Skills. Announced on October 16, 2025, this release aims to transform Claude from a powerful conversationalist into a specialized “doer” capable of executing complex, multi-step tasks. This new architecture allows developers and users to “onboard” Claude with specific expertise and tools, making it a more reliable and capable partner for enterprise wo

rkflows. You can read the full technical deep-dive on Anthropic’s engineering blog. This move signals a major strategic push into the “agentic AI” space, where models don’t just talk but act by using tools, running code, and interacting with software.

What Was Announced

This is a new feature release that enhances the entire Claude platform. “Skills” are modular, reusable packages of expertise that Claude can automatically discover and use when needed. Instead of putting all instructions into a single massive prompt, users can provide Claude with a library of skills to draw from.

The core innovations of Skills are:

Composable: Skills are designed to stack together. Claude can automatically identify which skills are needed for a task and coordinate their use in a chain.

Portable: A skill built once can be used everywhere—across Claude.ai (for Pro, Max, Team, and Enterprise users), Claude Code, and the developer API.

Efficient: Claude scans available skills and loads only the minimal information and files required for the task at hand, ensuring speed and performance.

Powerful: This is the key differentiator. Skills can include executable code. This allows Claude to perform tasks where traditional programming is more reliable than pure token generation, such as creating complex Excel spreadsheets with formulas, generating PowerPoint presentations, or filling out PDF forms.

Anthropic is also simplifying adoption with a “skill-creator” skill, which interactively guides users through the process of building their own custom skills.

Why Agent Skills?

Anthropic announced Skills to address a fundamental limitation of large language models: reliability in specialized, real-world tasks. While models like Claude are excellent at reasoning and generation, they can struggle with procedural, domain-specific, or high-stakes tasks that require perfect precision (like financial modeling or code deployment).

By allowing Skills to contain executable code, Anthropic is blending the reasoning power of its LLM with the deterministic reliability of traditional software. This move directly targets enterprise customers who need AI to integrate deeply into specific, custom workflows. It also positions Claude as a more direct competitor to platforms like OpenAI’s GPTs and Actions, shifting the battleground from model-versus-model to ecosystem-versus-ecosystem.

Analysis: Strengths and Challenges
Strengths:

Enterprise-Ready: The “build once, use everywhere” portability and the ability to package internal expertise (like company-specific coding practices or financial models) into skills is a powerful draw for businesses.

Reliability through Code: Integrating executable code via the Code Execution Tool is a significant strength. It offloads tasks that LLMs are bad at (like math or precise file manipulation) to a tool that is perfect at them, increasing trust and reliability.

Developer-Friendly: The API-first approach, a new /v1/skills endpoint for programmatic management, and the “skill-creator” tool show a strong focus on developer experience and adoption.

Efficiency: The “progressive disclosure” model, where Claude only loads what it needs, is a smart design that prevents context windows from being needlessly cluttered, maintaining performance.

Challenges:

Security: While skills run in a “secure environment,” allowing an AI to trigger executable code is an inherent security risk. Enterprises will need to rigorously audit custom skills and understand Anthropic’s sandboxing to prevent potential misuse.

Adoption and Ecosystem: The feature’s success depends on developers and users actually building a rich library of skills. It faces stiff competition from OpenAI’s more established GPT Store and developer ecosystem.

Complexity: As the number of available skills grows, managing dependencies, potential conflicts, and discovery could become a significant challenge for organizations.

Impact on the Market