agentskills

🥇Gold

Agent Skills provides a simple, open format for enhancing agent capabilities. Operations teams can create and share reusable skills, improving agent performance across tasks. Skills integrate with supported agents like Claude, streamlining workflows and reducing training time.

13,6904200Updated 3w ago

Intermediate30min to implementautomation

Saves ~30 min per use

Quick InstallView Source

git clone https://github.com/agentskills/agentskills.git

Works with:

Claude

Overview

About This Skill

The agentskills automation skill focuses on the specification and documentation necessary for creating and managing AI agent skills. This intermediate-level skill allows developers and product managers to automate data processing tasks by writing scripts that agents can execute on demand. With a time to implement of just 30 minutes, this skill provides a quick way to enhance the capabilities of AI agents, making them more efficient and effective in various operational contexts. One of the key benefits of using agentskills is its ability to facilitate workflow automation. By enabling agents to access knowledge bases and respond to customer queries efficiently, teams can significantly reduce response times and improve customer satisfaction. Additionally, agentskills supports the integration of various APIs, allowing seamless data exchange and task automation. This means that common tasks can be automated with reusable scripts, which minimizes the configuration required for complex workflows, ultimately saving time and resources. This skill is particularly beneficial for developers, product managers, and AI practitioners who are looking to enhance their teams' productivity. By sharing and adapting these skills across different projects, collaboration becomes easier, allowing teams to leverage existing resources and expertise. Whether you are in a tech startup or a larger organization, agentskills can help streamline your processes and enhance the performance of your AI agents. While the implementation difficulty is rated as intermediate, the practical value it offers makes it worthwhile. With no known time savings quantified, the real benefits lie in the operational efficiencies gained through automation. As businesses increasingly adopt AI-first workflows, integrating agentskills into your automation strategy will position your team to better respond to the demands of modern data-driven environments.

How to Use

[{"step":"Define the skill's scope and inputs","action":"Identify the specific task (e.g., 'ticket summarization') and list all required inputs (e.g., ticket_id, customer_query, metadata).","tip":"Start with a narrow, well-defined task to ensure the skill is reusable. Use the input_schema to enforce consistency."},{"step":"Design the output structure","action":"Determine what outputs are needed (e.g., summary, key issues, recommended action) and their formats. Use the output_schema to standardize responses.","tip":"Include a confidence_score in outputs to help downstream systems evaluate the skill's reliability."},{"step":"Implement execution logic","action":"Break the task into discrete steps (e.g., entity extraction, classification, summarization) and document each step in execution_steps. Specify tools/models to use (e.g., spaCy, BERT).","tip":"Use modular steps so individual steps can be updated without rewriting the entire skill."},{"step":"Add error handling","action":"Define fallback behaviors for missing data, low confidence, or model failures in error_handling. Include retry logic where applicable.","tip":"Test edge cases (e.g., empty inputs, ambiguous queries) to ensure robust error handling."},{"step":"Validate and share the skill","action":"Test the skill with sample inputs, validate outputs against expected results, and share it with your team via the Agent Skills framework.","tip":"Use tools like LangSmith or custom validation scripts to test the skill at scale before deployment."}]

Use Cases

Automate data processing tasks by creating scripts that agents can execute on demand.

Enhance customer support agents with skills that allow them to access knowledge bases and respond to queries efficiently.

Develop skills for agents to integrate with various APIs, enabling seamless data exchange and task automation.

Create reusable scripts for common tasks, allowing agents to perform complex workflows with minimal configuration.

Setup & Installation

Quick Install

No install command available. Check the GitHub repository for manual installation instructions.

Alternative Install (Git Clone)

git clone https://github.com/agentskills/agentskills

Requirements

Claude Code or compatible AI agent
Works with: Claude

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

Use the Agent Skills framework to create a reusable skill for [TASK_NAME] that includes: 1) a clear objective, 2) input/output structure, 3) step-by-step execution logic, and 4) error handling. Format the skill as a JSON object with fields: 'name', 'description', 'input_schema', 'output_schema', 'execution_steps', and 'error_handling'. Example task: 'summarizing customer support tickets'.

Example Output

```json
{
  "name": "ticket_summarizer_v1",
  "description": "Automatically generates concise summaries of customer support tickets for faster triage and response.",
  "input_schema": {
    "type": "object",
    "properties": {
      "ticket_id": {"type": "string", "description": "Unique identifier for the support ticket"},
      "customer_query": {"type": "string", "description": "Full text of the customer's issue"},
      "ticket_metadata": {
        "type": "object",
        "properties": {
          "priority": {"type": "string", "enum": ["low", "medium", "high", "critical"]},
          "category": {"type": "string", "description": "Issue category (e.g., billing, technical, account access)"},
          "created_at": {"type": "string", "format": "date-time"}
        }
      }
    },
    "required": ["ticket_id", "customer_query"]
  },
  "output_schema": {
    "type": "object",
    "properties": {
      "summary": {
        "type": "string",
        "description": "Concise summary of the ticket (max 150 characters)"
      },
      "key_issues": {
        "type": "array",
        "items": {"type": "string"},
        "description": "List of 2-3 primary issues identified"
      },
      "recommended_action": {
        "type": "string",
        "enum": ["assign_to_team", "escalate", "auto_reply", "pending_customer_info"]
      },
      "confidence_score": {
        "type": "number",
        "minimum": 0,
        "maximum": 1,
        "description": "AI's confidence in the summary and classification"
      }
    },
    "required": ["summary", "key_issues", "recommended_action", "confidence_score"]
  },
  "execution_steps": [
    {
      "step": 1,
      "action": "Extract and normalize key entities from the customer_query using NLP techniques.",
      "details": {
        "entities": ["product_name", "issue_type", "urgency_indicators"],
        "method": "spaCy with custom entity recognition model"
      }
    },
    {
      "step": 2,
      "action": "Classify the ticket into predefined categories using a fine-tuned BERT model.",
      "details": {
        "categories": ["billing", "technical", "account_access", "feature_request", "complaint"],
        "threshold": 0.85
      }
    },
    {
      "step": 3,
      "action": "Generate summary and key issues using a summarization model (e.g., PEGASUS).",
      "details": {
        "max_length": 150,
        "include_metadata": true
      }
    },
    {
      "step": 4,
      "action": "Determine recommended action based on category, priority, and extracted entities.",
      "details": {
        "rules": [
          "If category = 'billing' and priority = 'high', recommend 'escalate'",
          "If category = 'technical' and urgency_indicators present, recommend 'assign_to_team'",
          "If customer_query contains 'refund', recommend 'escalate' regardless of category"
        ]
      }
    },
    {
      "step": 5,
      "action": "Calculate confidence score based on model probabilities and entity extraction success.",
      "details": {
        "formula": "(entity_extraction_score + classification_score + summarization_score) / 3"
      }
    }
  ],
  "error_handling": {
    "missing_data": {
      "action": "Use default values for missing metadata fields (e.g., priority = 'medium').",
      "fallback": "If customer_query is empty, return error with ticket_id only."
    },
    "low_confidence": {
      "threshold": 0.6,
      "action": "Flag for human review with confidence score in output.",
      "suggestion": "Add 'pending_human_review' to recommended_action"
    },
    "model_failure": {
      "action": "Return raw ticket data with error message if summarization/classification fails.",
      "retry": "Attempt once with fallback model (e.g., DistilBERT instead of BERT)"
    }
  }
}
```