Agent Skills are folders of instructions, scripts, and resources that agents can discover and use on demand. They’re an open standard that works across Claude, GitHub Copilot, VS Code, Cursor, and OpenAI’s Codex. A skill is just a directory with a SKILL.md file at its root, optionally accompanied by scripts, reference docs, and templates. That’s it. No fine-tuning, no model retraining, no API integration work. You write instructions in Markdown, put them in a folder, and your agent gets a new capability.
A custom skill will do more for your daily output than any model upgrade. Agents are only as good as their context, and a frontier model with no knowledge of your codebase, your team’s conventions, or your deployment process produces generic output. Skills close that gap. They give agents procedural knowledge specific to your situation: not just “what” but “how.”
The design is clever in a way that’s easy to overlook. Skills use progressive disclosure. The agent only sees a skill’s name and one-line description until it determines the skill is relevant. Then it reads the full SKILL.md instructions. Scripts and reference files within the skill directory are loaded only when actually needed during execution. This means you can have dozens or hundreds of skills available without burning your context window. The agent picks up only what it needs, when it needs it.
I’ve been building my own skills and the experience has made me think about how I use agents completely differently. Take my PR review skill, which I adapted from Anthropic’s quasi-official example to fit my own review priorities. Instead of hoping the agent catches everything when I ask it to review a pull request, the skill spins up six specialized review agents covering code quality, simplification opportunities, documentation gaps, test coverage, error handling, and type design. Each agent scores its own applicability to the PR before running, so a documentation-only change doesn’t waste time on type design review. The output is structured, prioritized feedback organized into critical, important, and suggestion tiers. This is not the kind of nuanced, multi-pass review you get from a bare “review this PR” prompt.
My localization skill is another example. It auto-detects your project’s localization framework (React i18n, iOS .strings, Android XML, Rails YAML, Django PO, and others), then uses a dual-translation approach with two independent translator agents and an adjudicator to verify quality. It preserves placeholders, HTML tags, pluralization rules, and ICU message formats. I built it once and now I can type /localizer to translate any project I’m working on. The alternative would be re-explaining this entire workflow to the agent every single time, which means it would never actually happen.
This is the key insight: skills let you encode workflows that are too complex to type out on demand but too specific to be built into the model. Every team has these. Your deployment checklist. Your code review standards. Your data migration process. Your onboarding documentation generator. These are all skills waiting to be written.
The portability angle is worth noting too. Because skills are an open standard, a skill you write for Claude Code works in Copilot, Cursor, and Codex. You’re not locked into one vendor’s plugin system. You’re writing portable procedural knowledge.
If you’re using AI agents and you haven’t looked at skills yet, you’re leaving the most practical productivity gains on the table. The models are good enough. The differentiator now is the context and procedures you give them.