---
title: "A practical guide to building Claude Skills | TauX"
description: "Turning an SOP or a piece of expert knowledge into a reusable automated workflow: folder structure, the YAML rules that decide whether it loads, testing, and release."
url: "https://taux.io/en-US/claude-skills-guide"
locale: "en-US"
alternates:
  ja-JP: "https://taux.io/ja-JP/claude-skills-guide"
  ko-KR: "https://taux.io/ko-KR/claude-skills-guide"
  zh-Hans-CN: "https://taux.io/zh-Hans-CN/claude-skills-guide"
  zh-Hant-TW: "https://taux.io/zh-Hant-TW/claude-skills-guide"
---

# Claude Skills in Practice

The Complete Guide to Building Skills for Claude  
  
A step-by-step handbook for developers and teams:  
how to turn an SOP and expert knowledge into an automated workflow.

## What a Skill actually is

A Skill is a folder holding instructions and a workflow. It lets Claude follow your process exactly, without you re-explaining the background in every conversation.

A standard Skill folder contains:

*   SKILL.md (required): the core instructions, with the YAML front matter that decides when it fires.
*   scripts/ (optional): executable scripts — Python, Bash.
*   references/ (optional): reference material Claude reads only when it needs to.

**Why bother:** teach it once and it holds, and everyone on the team produces the same shape of output.

## The staircase

### Step 1. Scope it

Define the use case, decide what triggers it and what done looks like, and work out whether it needs external tools (MCP).

### Step 2. Build the structure

Create the folder, write the required SKILL.md, and follow kebab-case naming exactly.

### Step 3. Write the instructions

Precise YAML triggers, and Markdown instructions written with progressive disclosure in mind.

### Step 4. Test and ship

Start with one task by hand. Once the API calls and behaviour check out, zip it and release it to the team.

## MCP and Skills together

The Connectivity

MCP

Connectivity to the outside  
(What Claude can do)

### The part that knows how to use the tools

If MCP gives Claude limbs — read Notion, create a Linear issue — Skills are what tell it how to use those limbs properly.

**MCP without a Skill:** a toolbox and no instructions. Every task starts with a complicated prompt written from scratch.  
  
**MCP with one:** the workflow starts itself, with the good practice already baked into every interaction.

## Three things people build

### 1\. Documents and assets

Consistent output at a known quality. Brand guidelines, template structure and a pre-publication checklist can all live inside it. No external tools needed.

### 2\. Workflow automation

Multi-step processes run in order, with verification built in. Usually coordinates several MCP servers.

### 3\. Hardening an MCP server

Built to strengthen a server you already have: inject domain knowledge (Sentry triaging a bug automatically), supply context, and head off the API mistakes people keep making.

## The YAML rules — where most Skills die

### Naming, strictly

Claude is unforgiving about folder and file names. A small mistake means the Skill simply does not load.

*   The file must be named exactly SKILL.md — case-sensitive, and skill.md will not do.
*   The folder and the `name:` field must both be kebab-case.
*   **No** spaces, capitals or underscores (`Notion_Setup` is wrong).
*   No `README.md` in the Skill root.

### The description, and what triggers it

The description is the only thing Claude uses to decide whether to invoke the Skill. Under 1024 characters.

*   It has to say both **what it does** and **when to use it**, with concrete trigger phrases.
*   For example: "Analyses design files. Use when the user uploads a .fig file or asks for design specifications."
*   **Security rule:** never put XML tags `< >` in the YAML — that is a prompt-injection vector.

## Writing the instructions

### Concrete and executable

Avoid vague direction. Not "validate the data properly" but "run `scripts/validate.py`; if it errors, check for a missing required field." Code is more definite than prose. 

### Progressive disclosure

To keep token cost down, keep `SKILL.md` lean — the core steps only. Move detailed API specs and large templates into `references/`, for Claude to read when it needs them.

### Handle the failures up front

Anticipate what breaks and say what to do. A short troubleshooting section — "if the connection is refused, ask the user to reconnect under Settings > Extensions" — removes most of the occasions a human has to step in.

## Worked example 1: a code review assistant

### The standard shape for a single task

A standard `SKILL.md` that needs no external MCP tools. What matters is stating the **preconditions** and the **guardrails**. 

*   The YAML block: defines exactly when it fires.
*   Role: gives Claude a point of view to answer from.
*   Workflow: numbered, so the order is not optional.
*   Rules: says what not to do, so it does not overreach.

```
---
name: strict-pr-reviewer
description: Fires when the user pastes code or asks for a code review .
---

# Role
You are a strict, senior backend architect.

# Workflow
When invoked, work through these in order:
1. **Security scan** — look for SQL injection and hard-coded credentials.
2. **Performance** — flag anything worse than O(N^2).
3. **Report** — present the suggested changes as a Markdown table.

# Rules
- Do **not** hand back a full rewrite.
- Give specific changes and the reasoning behind them.
```

## Worked example 2: automation across MCP

```
---
name: linear-bug-reporter
description: Turns an error log in the conversation into a Linear issue.
---

# Objective
Analyse the error log and create a bug ticket via the `linear` MCP.

# Instructions
1. Extract from the log: the code, the timestamp, the likely cause.
2. Call the `linear_create_issue` tool.
3. Set the title to `[Bug] {error code}` .

# Error Handling (Fallbacks)
- If `linear` is missing or the connection fails, **do not apologise**.
- Fall back: produce the bug report as Markdown for the user to paste in.
```

### Multiple steps, and what to do when one fails

This is Claude calling external APIs the way an RPA bot would, with a recovery path of its own.

*   Objective: one sentence saying what this is ultimately for.
*   Naming the tool: name the tool explicitly (`linear_create_issue`).
*   Fallback: **this is the important one.** Assume the MCP connection can drop. Tell Claude what to produce in plain text when the tool is unavailable, so the process does not simply stop.

* * *

## Testing

"Get Claude doing one task perfectly first. Then extract that pattern and wrap it as a Skill."

## Three dimensions to test

| Dimension       | What you are checking                                                   | How you know                                                                      |
| --------------- | ----------------------------------------------------------------------- | --------------------------------------------------------------------------------- |
| **Triggering**  | It loads when it should, and stays quiet when the subject is unrelated. | 90% of relevant requests invoke it without being asked to; unrelated ones do not. |
| **Functional**  | The workflow produces the right result and handles the edge cases.      | Zero unhandled API errors; every tool call completes; the output keeps its shape. |
| **Performance** | The Skill beats the baseline of a person steering it by hand.           | Fewer turns, fewer tokens, and no corrections needed mid-run.                     |

## Five patterns worth knowing

*   **Sequential workflow:** steps 1 through N, strictly. For work with real dependencies — create the customer, set up payment, send the welcome mail.
*   **Multi-MCP:** orchestration across services. Export from Figma (one server), file it in Google Drive (a second), open a task in Linear (a third).
*   **Iterative refinement:** draft, validate with a script, correct, repeat — a closed loop that only exits when the output is good enough.
*   **Context-aware:** give Claude a decision tree. "Over 10MB, use the cloud storage server; if it is code, use GitHub."
*   **Domain knowledge:** before the API call, run the compliance or risk checks a senior practitioner would run without being asked.

## Packaging and release

**Packaging:** once tested, zip the folder as `.zip`. Individuals upload it on Claude.ai; an administrator can deploy it across a whole workspace, where it updates itself.

**Open source and API:** host it on GitHub with a clear `README.md` covering installation. Application developers can invoke it programmatically through the API's `container.skills` parameter.

**How to pitch it:** lead with the outcome, not the mechanism. "This configures the project in seconds" lands; an architecture diagram does not.

## Ready to build one?

"Knowledge drives the tools, and automation becomes the team default."

Use the built-in skill-creator to get moving.