Build agent skills for Claude to turn a general AI into a focused helper that understands your tasks, files, and tools. This guide explains the skill format, shows how skills work with the context window and code execution, and gives a step-by-step process to design, test, and ship stable, scalable skills. Modern AI can use filesystems and run code. But real work needs reliable procedures, not just good guesses. Skills let you package those procedures in a clean, reusable format. Think of a skill as an onboarding pack for a new teammate: clear rules, references, and scripts that unlock specific abilities on demand. With a few files and a simple structure, you can scale your best workflows across teams and projects.

Why build agent skills for Claude

Skills help you capture your domain knowledge in a way the agent can actually use while it works. Instead of hardcoding a single-purpose agent, you empower one agent with many portable skills it can load only when needed. This reduces token waste, keeps guidance organized, and helps you iterate faster when tasks change. Three practical benefits stand out:

Repeatability: A skill encodes steps that work the same way every time.

Composability: You can install many skills and let the agent pick the right one.

Scalability: You can grow a skill by linking more context without blowing the context window.

The skill folder, explained

A skill is a directory with a SKILL.md file and any extra references or scripts. The SKILL.md starts with a small YAML header that gives the skill a name and a short description. The agent loads this metadata into its system prompt at startup, which lets it decide when a skill is relevant.

Progressive disclosure in three levels

The design principle is simple: reveal more context only when needed.

Level 1: Metadata. The agent sees the skill name and description. This helps it decide whether to open the skill.

Level 2: SKILL.md body. If relevant, the agent reads the full instructions in SKILL.md.

Level 3: Linked files. If the task requires more detail, the agent opens extra files referenced in SKILL.md (like forms.md, reference.md, or playbooks).

This structure keeps the core skill lean and gives you room to add deep guidance in separate files. Because the agent can read files on demand and run tools, the total context you can store in a skill is practically unlimited.

What to put in SKILL.md

Use SKILL.md to give the agent a clear mission and safe, step-by-step procedures:

Purpose and scope: When to use this skill and when not to.

Inputs and outputs: What the agent needs and what it should produce.

Core procedures: Simple steps in order. Use bullet points and short sentences.

Linked references: Name the files the agent should read for special cases.

Tool usage: Which scripts to run, with parameters or examples.

Safety notes: Things to check before taking actions, especially with files or network calls.

How the context window changes when a skill is used

When a user sends a message, the agent starts with its system prompt and the metadata for all installed skills. If it detects that a skill matches the task, it opens SKILL.md. If SKILL.md mentions more files, the agent reads those files next. It then continues the task with the extra context. This flow keeps token use low, but gives the agent deep, precise help when needed.

A simple sequence to picture

Agent sees the user request and the skill metadata in the prompt.

Agent opens the skill’s SKILL.md to load the main instructions.

Agent follows links to extra files for special tasks.

Agent executes steps, runs tools if needed, and returns results.

Adding executable tools to your skill

Some work is better done with code than with tokens. Sorting lists, parsing PDFs, converting files, calling APIs, or validating data are good examples. When you bundle scripts with a skill, the agent can run them directly. This gives you speed, determinism, and consistent results.

When to run code vs. read code

Run code when you want exact, repeatable behavior (parsing, conversion, math, search).

Read code as a reference when the code teaches a method the agent should follow by reasoning (e.g., style guides or naming rules).

Tell the agent in SKILL.md which scripts are tools and when to run them.

Example use case

A PDF skill can include a Python script that lists form fields. The agent runs the script, gets a structured result, and fills the form. It never needs to copy a full PDF into context, so the interaction is both cheap and reliable.

Step-by-step guide to design your first skill

To build agent skills for Claude, start small. Focus on one real workflow your team repeats often. Then expand.

1) Discover the gap

List tasks where the agent needed extra prompts, context, or switching between tools.

Collect example inputs and desired outputs.

Note any failures and how a human fixed them.

2) Define the skill’s purpose

Write a one-line goal in plain language.

State when to use the skill and when not to.

List required inputs and the final output format.

3) Draft SKILL.md

Add YAML frontmatter with name and description.

Write a clear, numbered procedure in short lines.

Add a Troubleshooting section with common errors and fixes.

Link extra files for rare or advanced cases.

4) Add tools (optional but powerful)

Bundle small, focused scripts with clear names (e.g., extract_fields.py).

Describe exactly when to run each script and how to pass parameters.

Prefer deterministic code. Log outputs for easier debugging.

5) Test on real tasks

Run a dozen representative cases end-to-end.

Observe when the agent opens the skill and whether it reads linked files.

Log failures and update the steps or tools to remove ambiguity.

6) Ship and monitor

Version your skill folder and record changes.

Collect feedback from teammates.

Refine names and descriptions so the agent triggers the skill at the right time.

Writing tips that improve agent behavior

Small phrasing changes can make big differences in reliability.

Names and descriptions matter

Keep the name concrete (e.g., PDF Form Filler, not Document Helper).

Describe the intent and triggers in one sentence (e.g., “Use this when you need to extract or fill PDF form fields”).

Use clear patterns the agent can learn

Start each step with a verb: “Open file,” “Run script,” “Validate output.”

Give explicit checklists for safety and validation.

Point to linked files with their exact filenames.

Reduce token waste

Move rare scenarios into separate files and link them.

Keep examples short and focused.

Prefer running a script over generating long intermediate steps.

Testing, evaluation, and iteration

As you build agent skills for Claude, test with real data and edge cases. Watch how the agent navigates the skill, not just the final answer.

Evaluate in four dimensions

Trigger accuracy: Does the agent open the skill when it should? Does it avoid it when it should not?

Procedure fidelity: Does it follow each step in order?

Tool usage: Does it run scripts with the right parameters and interpret outputs correctly?

Output quality: Does the result match the format and quality you expect?

Use the agent as your co-author

Ask the agent to summarize what steps worked and add them to SKILL.md.

Ask it to reflect on failures and propose fixes to the text or tools.

Iterate until the agent stops drifting and produces stable outputs across runs.

Security checklist for Skills

Skills carry instructions and executable code, so treat them like software.

Before installing a skill

Trust the source or perform a full audit.

Read SKILL.md and all linked files to confirm intent.

Check for external network calls and confirm they are safe and required.

Review dependencies for known risks and license issues.

Scan bundled scripts and images for hidden payloads.

Before running tools

Sandbox execution when possible.

Restrict access to sensitive files and environment variables.

Log inputs and outputs for traceability.

Validate outputs before downstream actions.

Common pitfalls and how to avoid them

Too much in SKILL.md

Symptom: The agent gets lost or you hit context limits.

Fix: Move rare content to linked files and keep the core steps short.

Vague descriptions

Symptom: The agent does not trigger the skill or triggers it at the wrong time.

Fix: Rewrite the name and description with explicit triggers and scope.

Ambiguous tool instructions

Symptom: The agent reads a script as text instead of running it, or runs it with wrong args.

Fix: State “Run SCRIPT with ARGs when CONDITION” inside SKILL.md and include examples.

Silent failure paths

Symptom: Partial outputs, missing files, or skipped steps.

Fix: Add validation steps and error handling rules in the procedure.

How skills fit with tool ecosystems and MCP

Skills work well with broader tool bridges like Model Context Protocol (MCP). MCP connects agents to external systems. Skills teach the agent how to use those systems to finish a task. Together, they cover both access (via MCP servers) and method (via SKILL.md and linked files). This pairing lets you grow from single-folder skills to robust, end-to-end workflows that span many tools and services.

Real-world examples you can model

Document work

Extract and fill PDF forms using a parser script and a step-by-step filling guide.

Summarize reports with linked style rules and template outputs.

Data preparation

Clean CSVs with a validation script, then generate a summary report.

Map fields between schemas using a reference file and tests.

Software tasks

Read logs, run a diagnostic script, and propose fixes using a linked playbook.

Generate changelogs from commit messages with formatting rules.

Maintenance and versioning

Treat your skill like a living asset.

Keep a CHANGELOG.md with meaningful notes.

Version your folder so teams can pin to stable releases.

Retire old steps and remove dead links quickly.

Add small, focused tests that the agent can run to confirm the skill still works.

From one skill to a library

Once a single skill works well, create a library of small, focused skills instead of one giant folder. Give each skill a clear name and scope, then group them by domain (documents, data, devops). This improves discoverability and reduces accidental triggers. Over time, your organization builds a shared playbook that any agent can use on day one.

Conclusion

You can build agent skills for Claude to capture your best practices, cut token waste, and boost reliability. Start with one workflow, write a clear SKILL.md, link supporting files, and add small tools where they make sense. Test on real tasks, lock in repeatable steps, and keep improving. With skills, a single agent can learn to handle the work that matters—again and again.

(Source: https://www.anthropic.com/engineering/equipping-agents-for-the-real-world-with-agent-skills)

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