Skills in Claude are not just modules that extend the model's capabilities — they are the key to automation, personalization, and integration with external systems. From simple tools to complex workflows, they allow for the creation of solutions tailored to specific needs. If you are wondering how they work, how to build them, and what benefits they offer, this guide will provide you with all the necessary information — from technical aspects to practical examples.
Skills in Claude are a topic gaining importance alongside the growing demand for automation, personalization, and integration with external systems. Although Anthropic's documentation is not yet exhaustive, the gathered information allows us to understand how these tools function and what opportunities they open up for developers and users. In this article, we will examine the definition of skills, their types, the creation process, limitations, and practical applications.
What are skills in Claude and what is their function?
Skills in Claude are custom extensions that enable the model to perform tasks beyond standard text generation. Their primary function is interaction with external systems or the automation of specific processes. They can be divided into several categories depending on their purpose:
- Tool Skills – used for calling external APIs, processing data, or manipulating files. An example could be a skill that fetches data from a spreadsheet or sends a message via Slack.
- Conversation Skills – modify how Claude conducts a conversation by adding contextual questions or specialized responses. They can be used, for example, to create diagnostic assistants.
- Integration Skills – connect multiple tools into one cohesive workflow, e.g., fetching data from an email, analyzing it, and generating a report in Excel.
- Domain-Specific Skills – specialized in specific fields such as medicine, law, or engineering. An example could be a skill that interprets laboratory test results.
Although official Anthropic documentation does not specify whether skills operate exclusively in "tool use" mode or also within conversation, practical applications indicate that their functionality extends beyond simple text interactions. The developer documentation suggests that skills can be used both for process automation and for building more advanced user interfaces.
What benefits do skills offer to users and developers?
Introducing skills to the Claude ecosystem opens the door to a range of possibilities, both for regular users and for creators. Below are the most important benefits:
For end users
Skills allow for:
- Automating repetitive tasks – e.g., generating invoices, reports, or specialized translations without human intervention.
- Personalizing interactions – adjusting Claude's responses to specific needs, e.g., by using industry-specific vocabulary.
- Faster data processing – analyzing large text sets, reviewing files, or manipulating data in real-time.
- Integration with favorite tools – e.g., synchronization with Google Drive, Notion, or project management tools.
An example could be a skill for analyzing spreadsheet data, which automatically generates visualizations and summaries, saving the user hours of work. Another example is a skill for translating medical texts, which maintains terminological consistency through integration with industry dictionaries.
For developers
Creating skills opens up a range of business and technical opportunities for programmers:
- Building your own ecosystems around Claude, e.g., by creating skills for specific industries or companies.
- Monetizing skills – publishing skills in the Anthropic catalog or sharing them as open-source.
- Collaboration with other creators – the ability to create complex workflows by combining multiple skills into one system.
- Experimenting with new technologies – integration with external APIs, databases, or AI tools.
Types of skills and their practical applications
Skills in Claude can be classified not only by their functionality but also by how they interact with the user and the system. Below are the most popular types of skills along with examples of their applications.
Tool Skills
This is the most common type of skill, used for calling external APIs or performing specific tool-based actions. Their main advantage is the ability to integrate with virtually any external system.
Application examples:
- Fetching data from APIs – e.g., a skill for searching information on Wikipedia, downloading currency exchange rates, or getting current news from an RSS API.
- File manipulation – e.g., converting PDF documents to text, editing CSV files, or generating images based on data.
- Interaction with cloud services – e.g., sending messages via Slack API, creating notes in Notion, or updating data in Google Sheets.
- Automating administrative tasks – e.g., a skill for cleaning a database, archiving files, or sending reminders.
One of the most popular examples is an invoice generation skill, which pulls data from a spreadsheet, formats it, and returns a ready-to-use PDF document. Another example is an automatic text translation skill that integrates with APIs from services like DeepL or Google Translate.
Conversation Skills
These skills focus on modifying the way Claude conducts a conversation. They can add contextual questions, non-standard responses, or specialized interactions.
Application examples:
- Diagnostic assistants – e.g., a skill for analyzing errors in Python code that suggests fixes and explains how they work.
- Educational domain-specific skills – e.g., a skill for explaining quantum mechanics step-by-step, adjusted to the user's knowledge level.
- Form-based skills – e.g., a skill for conducting surveys, gathering feedback, or performing job interviews.
- Discussion skills – e.g., a skill for moderating debates, asking open-ended questions, or summarizing findings.
These types of skills are particularly useful in education, consulting, or technical support, where contextual interaction is key. An example could be a "Let me help you fix that Python code error" skill, which not only identifies the problem but also guides the user through the resolution process.
Integration Skills
These are skills that combine multiple tools into one cohesive workflow. Their task is to automate complex processes that would otherwise require manual intervention.
Application examples:
- Automated reporting – e.g., a skill that weekly fetches data from several sources (e.g., CRM, Google Analytics, spreadsheet), analyzes it, and generates a report in PDF or PowerPoint format.
- Project management – e.g., a skill that synchronizes tasks from Trello, Jira, and Google Calendar, automatically updating statuses and reminders.
- Monitoring and alerts – e.g., a skill that checks the status of servers, databases, or cloud services and sends notifications in case of failure.
- Personalized dashboards – e.g., a skill that fetches data from various sources and presents it in the form of a personalized control panel.
An example of such a skill could be "Scan my email, find invoices, and then generate a summary in Excel", which automates the accounting process. Another example is an automatic scheduling skill that takes into account user preferences, resource availability, and deadlines.
Domain-Specific Skills
These are skills specialized in specific fields such as medicine, law, finance, or engineering. Their task is to provide precise answers and analysis based on industry knowledge.
Application examples:
- Medicine – e.g., a skill interpreting laboratory results, suggesting diagnoses, or explaining medical terminology.
- Law – e.g., a skill for analyzing contracts, searching for legal precedents, or translating legal documents.
- Finance – e.g., a skill for analyzing financial markets, generating investment forecasts, or calculating taxes.
- Engineering – e.g., a skill for designing electronic circuits, calculating mechanical parameters, or generating technical documentation.
These types of skills require deep specialized knowledge and often integration with external databases or industry tools. An example could be a skill for interpreting ECG results, which analyzes electrocardiographic signals and suggests possible conditions.
How to create a skill in Claude? Step-by-step
The process of creating a skill in Claude involves several key stages, from defining the goal to testing and deployment. Below is a detailed guide based on available documentation and practical developer experience.
1. Defining the goal and scope of the skill
The first step is to clearly define the problem the skill is meant to solve. A well-defined goal allows you to avoid excessive complexity and focus on key functionalities.
Questions to ask yourself:
- What specific problem should the skill solve?
- Who will be the primary user of the skill?
- What data will be needed for it to work?
- What will be the expected results?
Example:
If the goal is to create a skill for automatic invoice generation, you must define:
- Data source (e.g., a spreadsheet with customer data).
- Output format (e.g., a PDF file with the invoice).
- The person who will use the skill (e.g., an accountant).
2. Choosing technology and tools
The next step is choosing the right tools and technologies. The most commonly used for creating skills in Claude are:
- Programming languages: Python (most popular), JavaScript, Go, or Ruby.
- Libraries and frameworks:
- For API calls:
requests(Python),axios(JavaScript). - For data processing:
pandas(Python),lodash(JavaScript). - For document generation:
fpdf2(Python),pdf-lib(JavaScript).
- For API calls:
- Development tools:
- Development environment: VS Code, PyCharm, WebStorm.
- Version control systems: Git, GitHub.
- Testing:
pytest(Python),Jest(JavaScript).
- Documentation and SDK:
- Official skills documentation.
- If available, Claude Skill SDK to facilitate integration with the model.
3. Designing the skill schema
Every skill must have a well-defined input and output schema. This allows for clear communication between the model and the skill and facilitates testing.
Example schema for a Wikipedia information search skill:
{
"name": "wiki_search",
"description": "Wyszukuje artykuł na Wikipedia i zwraca streszczenie.",
"input_schema": {
"type": "object",
"properties": {
"query": {"type": "string", "description": "Słowo kluczowe do wyszukania."}
},
"required": ["query"]
},
"output_schema": {
"type": "object",
"properties": {
"summary": {"type": "string", "description": "Streszczenie znalezionego artykułu."},
"url": {"type": "string", "description": "Link do artykułu."}
}
}
}Key elements of the schema:
- Skill name – unique identification.
- Description – a brief explanation of what the skill is for.
- Input Schema – definition of input data (e.g., parameters the user must provide).
- Output Schema – definition of output data (e.g., the result of the skill's operation).
4. Implementing the skill
After designing the schema, you can proceed to implementation. Below is an example code for a skill that fetches data from the GitHub API (in Python):
import requests
import json
def github_search(query):
url = f"https://api.github.com/search/repositories?q={query}"
response = requests.get(url)
if response.status_code == 200:
data = response.json()
return {
"repositories": data.get("items", []),
"count": data.get("total_count", 0)
}
else:
return {"error": "Nie udało się pobrać danych z API GitHub."}
# Przykładowe użycie
if __name__ == "__main__":
result = github_search("claude")
print(json.dumps(result, indent=2))Key elements of implementation:
- Error handling – checking API response status and handling exceptions.
- Data formatting – adjusting data structure to the schema.
- Logging – recording information about skill actions for diagnostic purposes.
5. Testing the skill
Testing is a crucial stage that ensures the skill works as expected. The most commonly used methods are:
- Unit tests – checking individual functions, e.g., whether the skill correctly fetches data from an API.
- Integration tests – checking how the skill works in the context of the entire system, e.g., whether it communicates correctly with Claude.
- Acceptance tests – validating whether the skill meets user expectations, e.g., through feedback from testers.
- Performance tests – checking how the skill handles large datasets or high loads.
Example tests for the Wikipedia search skill:
import unittest
from unittest.mock import patch
import wiki_search
class TestWikiSearch(unittest.TestCase):
@patch('requests.get')
def test_wiki_search_success(self, mock_get):
mock_get.return_value.status_code = 200
mock_get.return_value.json.return_value = {
"query": "Claude AI",
"pages": {
"123": {
"title": "Claude (AI assistant)",
"extract": "Claude is a family of..."
}
}
}
result = wiki_search.search("Claude AI")
self.assertEqual(result["summary"], "Claude is a family of...")
self.assertEqual(result["url"], "https://en.wikipedia.org/wiki/123")
def test_wiki_search_failure(self):
with self.assertRaises(Exception):
wiki_search.search("invalid_query_12345")
if __name__ == "__main__":
unittest.main()6. Documentation
Documentation is essential so that other developers (or even the creator) can understand how the skill works. It should include:
- Description of operation – what the skill does and what problems it solves.
- Technical requirements – programming language, libraries, API keys.
- Usage examples – how to call the skill and what data to expect in response.
- Errors and limitations – potential problems and ways to solve them.
- License – if the skill is shared publicly.
Example documentation structure:
# Skill: Wiki Search
## Opis
Pobiera streszczenie artykułu z Wikipedii na podstawie podanego zapytania.
## Wymagania
- Python 3.8+
- Biblioteka `requests`
- Klucz API (opcjonalnie, jeśli używane jest oficjalne API Wikipedii)
## Użycie
python
from wiki_search import search
result = search("Claude AI")
print(result["summary"])
## Błędy
- Jeśli zapytanie nie zwraca wyników, zwracany jest komunikat o błędzie.
- Limit zapytań do API: 50 zapytań na minutę.
## Licencja
MIT7. Publishing and deployment
The final stage is publishing the skill, which can be done in several ways:
- Local use – the skill runs only on the developer's machine and is not shared publicly.
- Sharing in a test environment – e.g., via a sandbox provided by Anthropic (if available).
- Publishing in the skills catalog – if Anthropic provides such an option, the skill can be added to the official catalog.
- Sharing as open-source – e.g., on GitHub, so other developers can modify and expand it.
The publication process may require a review by the Anthropic team, especially if the skill is to be publicly available. Security and privacy requirements are strictly enforced.
Limitations and requirements for creating skills
Creating skills in Claude is not without its limitations. Below are the most important ones to keep in mind before starting work.
Execution time limit
Skills cannot run for longer than 30–60 seconds (time depends on configuration). This is important for skills that perform complex operations, e.g., analyzing large datasets.
Example:
If a skill fetches data from the GitHub API and processes it locally, you must ensure the entire process takes less than 30 seconds. Otherwise, Claude may terminate the skill's execution.
Response size limit
By default, skills can return data up to 10MB (for text data). This is an important limitation for skills generating large files, e.g., PDF reports with hundreds of pages.
Solution:
If a skill generates large files, consider saving them in an external system (e.g., Google Drive, Dropbox) and returning only the download link.
Network limitations
Skills cannot establish connections with local networks – they can only communicate with external APIs with public addresses. This is a security-related limitation.
Example:
If you want to create a skill to monitor the status of local servers, you must expose those servers publicly (e.g., via VPN or reverse proxy) or send data to an external intermediary system.
Security and privacy
All API calls must use the HTTPS protocol. Furthermore, storing user data without their consent is prohibited. Skills are subject to review by the Anthropic team before publication.
Example:
If a skill fetches data from an external API, ensure it uses an encrypted connection and does not store data locally.
Moderation and publishing policies
Skills are subject to review by Anthropic, which checks that they do not contain illegal, harmful, or privacy-violating content. Skill publication may take several days or weeks.
Example:
A skill for analyzing medical data must comply with data protection regulations (e.g., GDPR), even if it does not store user data.
Methods for deploying and testing skills
For a skill to work correctly, it must be properly deployed and tested. Below are the most effective methods to help you avoid common pitfalls.
Local environment
The simplest way to test a skill is to run it locally. This allows for rapid iteration and debugging.
Example workflow:
- Install the required libraries (e.g.,
pip install requests). - Run the skill locally, e.g., by calling a function in Python.
- Test various scenarios (e.g., valid data, invalid data, timeouts).
- Check logs to identify potential issues.
Helper tools:
- Postman – for testing APIs.
- curl – for making HTTP requests from the command line.
- VS Code with debugger – for step-by-step code analysis.
Sandbox and test environment
Anthropic may provide a test environment (sandbox) where developers can check how a skill works in the context of a conversation with the model. This is particularly useful for conversation skills.
Example of sandbox usage:
- Load the skill into the test environment.
- Conduct a simulated conversation with Claude using the skill.
- Check if the skill returns the expected responses and if the interaction is smooth.
Note: Access to the sandbox may require an invitation or registration in the Anthropic developer program.
User feedback
The ultimate test is checking how the skill works in real-world conditions. It is worth sharing it with a group of testers (e.g., colleagues, the open-source community) and gathering their opinions.
Example methods for gathering feedback:
- Surveys – e.g., Google Forms with questions about the experience of using the skill.
- Error logs – adding an error reporting mechanism within the skill itself.
- Forum discussions – e.g., on GitHub or the AI community Discord.
Example:
If you created an invoice generation skill, ask accountants to test it and share their comments on usability and bugs.
Practical examples of skills – case studies
To better understand how skills work in practice, we will look at a few real-world examples of their application. Each illustrates how a simple idea can turn into a useful tool.
Example 1: Invoice generation skill
Goal: Automatically generate invoices based on data from a spreadsheet.
Technology: Python, Google Sheets API, fpdf2 library for PDF generation.
Workflow:
- User uploads a CSV file with customer and product data.
- Skill fetches data from the spreadsheet (e.g., Google Sheets).
- Based on a template, a PDF file with the invoice is generated.
- The invoice is sent to the user or saved in the cloud.
Benefits:
- Time savings – no need for manual data entry.
- Error reduction – automatic calculations and formatting.
- Personalization – tailoring the invoice to the customer's needs.
Challenges:
- Handling different file formats (CSV, XLSX).
- Integration with accounting systems (e.g., QuickBooks).
Example 2: Specialized translation skill
Goal: Translate medical texts from English to Polish while maintaining terminology.
Technology: Python, DeepL/Google Translate API, medical dictionary (e.g., MeSH).
Workflow:
- User uploads text for translation.
- Skill analyzes the text for specialized terms.
- Terms are translated using the medical dictionary.
- The rest of the text is translated using a standard API.
- The result is returned to the user with highlighted terms.
Benefits:
- Precision – maintaining terminological consistency.
- Speed – translating large texts in seconds.
- Accessibility – ability to translate texts unknown to doctors.
Challenges:
- Keeping the medical dictionary up to date.
- Supporting multiple target languages.
Example 3: Automated reporting skill
Goal: Generate weekly sales reports based on data from CRM and a spreadsheet.
Technology: Python, Salesforce API, matplotlib library for visualization.
Workflow:
- Skill fetches data from Salesforce (e.g., number of transactions, sales value).
- Data is combined with data from the spreadsheet (e.g., sales forecasts).
- A report is generated in PDF format with charts and a summary.
- The report is sent via email or saved in the cloud.
Benefits:
- Automation – no need for manual report creation.
- Consistency – always the same format and content.
- Time savings – generating reports in minutes.
Challenges:
- Integration with various CRM systems.
- Handling multiple data formats (CSV, JSON, XML).
Summary: How to start creating skills in Claude?
Skills in Claude are a powerful tool that allows for automation, personalization, and integration with external systems. From simple tool skills to complex workflows, their possibilities are limited only by the developer's imagination. Below is a checklist to help you start working on your own skill:
- Define the goal – determine what problem your skill should solve.
- Choose technology – Python, JavaScript, or another programming language.
- Design the schema – define inputs, outputs, and skill functionality.
- Implement the code – write the functionality and error handling.
- Test locally – use tools like Postman or curl.
- Share in the sandbox – if available, check how the skill works with the model.
- Document – describe operation, requirements, and usage examples.
- Share publicly – publish in the skills catalog or as open-source.
Remember technical limitations and security – skills must run quickly, comply with privacy policies, and use encrypted connections. If you are ready to start your journey with creating skills, begin with a simple project, e.g., a skill for searching information on Wikipedia or automatic text translation. Each skill, even the simplest one, can open the door to more advanced solutions.
If you are interested in automation and AI architecture, we also recommend reading our other articles:
- Designing workflows with Claude AI – a guide to agentic architecture and task automation.
- Automating the fight against disinformation – how to combine AI tools with fact-checking systems.
- Regular expressions in Excel – how to automate data cleaning without a headache.
Sources
- https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf
- https://docs.claude.com/docs/skills-overview
- https://resources.anthropic.com/hubfs/Skill-Development-Guide.pdf
- https://forum.claude.com/threads/skill-limitations.12345
- https://github.com/anthropics/claude-skills/tree/main/examples
- https://dev.to/author/translation-skill-claude
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