Claude Skills and the Art of Teaching AI to Remember How to Work

There's a point in working seriously with AI where you stop thinking about individual prompts and start thinking about systems. You've written the same detailed instructions enough times — the tone, the format, the step-by-step process — that you begin to ask: why am I explaining this again?

That question leads to one of the most useful developments in AI tooling: reusable capability systems. The idea that the way you work with AI can itself be codified, stored, and reloaded on demand. Claude calls them Skills. Other platforms have their own versions. Understanding what these tools are — and what distinguishes them from each other — is fundamental to working with AI at scale.

The Problem They Solve

Every complex workflow you run with AI starts with setup. You establish the context, define the output format, explain the constraints, reference the relevant standards. For a one-off task that's fine. But when you're running the same type of work repeatedly — security reviews, document drafts, data analysis, brand content — that setup overhead compounds quickly.

Even more problematic: without a reusable system, quality is inconsistent. The instructions you give on Monday are slightly different from Thursday's. The output reflects that. Over time, what should be a reliable workflow becomes an unpredictable one.

Reusable capability tools solve both problems. Codify the process once. Run it consistently every time.

Claude Skills

Claude Agent Skills, launched by Anthropic in October 2025, are structured folders containing instructions, scripts, and resources that Claude discovers and loads dynamically when relevant to a task. The design principle is progressive disclosure — Claude reads a small amount of metadata at startup to know a Skill exists, then loads the full instructions only when the task calls for it.

This is a technically elegant approach. Rather than front-loading your entire instruction library into every session (which would contribute to context rot), Skills stay lean by default. A session might have dozens of Skills available without any of them consuming significant context — until they're needed.

A Skill is built around a SKILL.md file: a structured markdown document that defines what the Skill does, what input it expects, what output it should return, and what rules it must follow. Think of it as an onboarding guide for a very specific task. Additional files — reference documents, scripts, templates — can be bundled alongside and are only loaded when directly relevant.

Crucially, Claude's routing of Skills is done through pure language model reasoning — not algorithmic keyword matching or intent classifiers. Claude reads the Skill metadata, understands what the task is, and decides which Skills apply. Multiple Skills can be composed together automatically when a task spans more than one domain.

In December 2025, Anthropic published Agent Skills as an open standard, meaning Skills are portable across compatible platforms including Cursor, VS Code, and others — giving them a reach beyond Claude alone. The vision Anthropic has articulated is ambitious: agents that can eventually create, edit, and evaluate their own Skills, codifying successful patterns of behaviour into reusable capabilities without human intervention.

ChatGPT Custom GPTs

OpenAI's equivalent has been around longer. Custom GPTs launched in November 2023 and were the first widely available tool of this kind. They remain the most widely deployed, largely because of the GPT Store — a browsable marketplace of thousands of published GPTs that has no equivalent on other platforms.

A Custom GPT is best understood as a packaged chat experience: a pre-configured version of ChatGPT with its own instructions, uploaded knowledge files, and optionally, connections to external APIs via Actions. You configure it once, and everyone who uses it gets a consistent starting point.

The key distinction from Claude Skills is conceptual. A Custom GPT is product-like — a front door for users. You choose which GPT to open before you begin. Claude Skills are system-like — they activate based on what you're doing, without requiring you to navigate to them explicitly. The workflow difference is meaningful: Skills work with you; Custom GPTs require you to work with them.

Custom GPTs have practical limitations too. Instructions are capped at around 8,000 characters. Knowledge retrieval relies on OpenAI's chunked RAG approach, which can produce confident but incomplete results when the knowledge base is large. And for teams with complex, multi-step procedural workflows, the instruction cap becomes a genuine constraint.

That said, the GPT Store's scale and the ability to connect external APIs remain genuine advantages, particularly for consumer-facing applications and cross-organisational sharing.

Google Gemini Gems

Google's Gems are the simplest of the three implementations. A Gem is a saved configuration with custom instructions and optional file context — essentially a persistently saved prompt persona. Creation is fast, often under five minutes, which makes Gems accessible to non-technical users.

The trade-offs are real, though. Gems lack Claude's progressive disclosure architecture and the depth of instruction that Skills support. They also have no sharing ecosystem comparable to the GPT Store or the open portability of Claude's Skills standard. For Workspace-embedded tasks — summarisation, research, document drafting within Google's ecosystem — Gems perform well. For complex procedural workflows, they run out of room quickly.

Microsoft Copilot Agents

Microsoft's approach to reusable capabilities sits within Copilot Studio and the broader M365 ecosystem. Declarative Agents allow organisations to create custom versions of Copilot with their own instructions, knowledge sources, and tool access — analogous to Custom GPTs but governed by Microsoft's enterprise compliance and access control framework.

The significant difference is integration depth. A Copilot Agent can draw on SharePoint, OneDrive, Teams, and Outlook as native knowledge sources, and can connect to over 1,400 external systems through Model Context Protocol (MCP) and Power Platform connectors. For organisations already running on M365, this means reusable AI capabilities can be grounded in real organisational data without any additional plumbing.

The complexity cost is correspondingly higher. Building and maintaining Copilot Agents requires engagement with Microsoft's tooling and licensing stack in ways that Claude Skills or Custom GPTs do not.

How They Compare

Building a Skill Library

The most practical benefit of Skills — regardless of platform — is the shift from ad hoc prompting to engineered consistency. Once you've run a workflow enough times to know what good looks like, encoding it into a reusable form pays dividends quickly.

A useful rule of thumb: if you find yourself typing the same instructions across multiple sessions, that's a Skill waiting to be written. If a process has defined inputs, defined outputs, and defined quality criteria, it's a candidate for codification.

The skills built in this project — for producing documents, diagrams, slide decks, and research outputs — are a direct application of this principle. Each one captures the accumulated trial and error of running those tasks repeatedly, distilled into a reusable form that any session can load. The result is less time spent on setup and more consistent output quality.

The broader implication is that your skill library becomes an asset. Each Skill represents captured knowledge about how to work. Over time, a well-maintained library compounds — newer work builds on the foundations laid by earlier work, and the quality floor rises.

That's a different relationship with AI than one-off prompting. It's closer to building an institutional knowledge base that happens to be executable.

Posted by Envision8 · envision8.com