10 Agent Skills Every AI Developer Should Know About

Every AI agent is only as capable as the tools it can use. You can have the most sophisticated reasoning model on the planet, but if it cannot read a PDF, scrape a web page, or validate a data payload, it is effectively stuck — a brain without hands.

The good news: you do not have to build every capability from scratch. The agent skill ecosystem has matured to the point where verified, production-ready tools exist for every common need. The challenge is knowing which skills matter most and where to find trusted versions.

This list covers the 10 agent skills that show up in virtually every serious AI agent deployment. For each skill, we break down what it does, why agents need it, what trust score to look for, and a practical code example you can use today.

1. Web Scraping

What It Does

Web scraping skills extract structured content from web pages. The best implementations handle JavaScript-rendered content, respect robots.txt, and return clean markdown or structured data instead of raw HTML.

Why Agents Need It

Most real-world agent tasks require fresh information from the web. Whether your agent is researching competitors, monitoring prices, or gathering data for a report, web scraping is the foundational capability that connects your agent to live information.

What to Look For

• Trust score of 70+ (Verified tier or higher)
• Support for both static and dynamic pages
• Built-in rate limiting and robots.txt compliance
• Output in multiple formats (text, markdown, structured JSON)

Code Example

from agentnode_sdk import load_tool

scraper = load_tool("web-scraper")
result = scraper.run({
    "url": "https://example.com/pricing",
    "format": "markdown",
    "include_metadata": True
})
print(result["content"])
print(f"Title: {result['title']}, Words: {result['word_count']}")

2. PDF Extraction

What It Does

PDF extraction skills parse PDF documents and return structured text, tables, and metadata. Advanced versions handle scanned PDFs using OCR, extract images, and preserve document structure.

Why Agents Need It

PDFs remain the dominant format for contracts, invoices, research papers, and financial reports. An agent that cannot read PDFs is locked out of most enterprise workflows. PDF extraction turns opaque binary files into text your agent can reason about.

What to Look For

• Trust score of 70+ with passing smoke tests
• Support for both text-based and scanned (OCR) PDFs
• Table extraction that preserves row and column structure
• Page-level extraction for large documents

Code Example

from agentnode_sdk import load_tool

pdf_reader = load_tool("pdf-extractor")
result = pdf_reader.run({
    "file_path": "/data/contract.pdf",
    "pages": "1-5",
    "extract_tables": True
})
for table in result["tables"]:
    print(table["headers"])
    for row in table["rows"]:
        print(row)

3. Code Execution

What It Does

Code execution skills run Python, JavaScript, or shell commands in a sandboxed environment and return the output. They provide agents with the ability to compute, transform data, and test hypotheses programmatically.

Why Agents Need It

Reasoning alone cannot solve every problem. When your agent needs to calculate a compound interest rate, transform a CSV, or test a regex pattern, code execution is the skill that bridges the gap between thinking and doing. It is arguably the single most powerful capability an agent can have.

What to Look For

• Trust score of 80+ (sandboxing is critical for code execution)
• Strict sandbox isolation — no network access, filesystem restrictions
• Timeout enforcement to prevent infinite loops
• Support for multiple languages

Code Example

from agentnode_sdk import load_tool

executor = load_tool("code-executor")
result = executor.run({
    "language": "python",
    "code": "import statistics\ndata = [23, 45, 12, 67, 34, 89, 56]\nprint(f'Mean: {statistics.mean(data):.2f}')\nprint(f'Median: {statistics.median(data)}')\nprint(f'Stdev: {statistics.stdev(data):.2f}')",
    "timeout_seconds": 10
})
print(result["stdout"])

4. Database Queries

What It Does

Database query skills connect to SQL or NoSQL databases, execute queries, and return structured results. The best implementations support read-only mode, query validation, and automatic schema introspection.

Why Agents Need It

Enterprise data lives in databases. An agent that can query a database directly — instead of waiting for a human to export a CSV — is dramatically more useful. Database skills turn your agent into an analyst that can answer business questions in real time.

What to Look For

• Trust score of 75+ with explicit permission declarations
• Read-only mode by default (write access should require explicit opt-in)
• Query parameterization to prevent SQL injection
• Connection string validation and credential handling

Code Example

from agentnode_sdk import load_tool

db_query = load_tool("database-query")
result = db_query.run({
    "connection_string": "postgresql://readonly:***@db.internal:5432/analytics",
    "query": "SELECT product_name, SUM(revenue) as total FROM sales WHERE quarter = $1 GROUP BY product_name ORDER BY total DESC LIMIT 10",
    "params": ["Q1-2026"],
    "read_only": True
})
for row in result["rows"]:
    print(f"{row['product_name']}: ${row['total']:,.2f}")

5. Email Sending

What It Does

Email skills compose and send emails through SMTP or API-based providers. They handle templates, attachments, HTML formatting, and delivery tracking.

Why Agents Need It

Autonomous agents need to communicate results. Whether it is sending a daily report, notifying a team about an anomaly, or following up with a customer, email is the universal communication channel. Email skills give agents the ability to take action in the real world — not just generate text.

What to Look For

• Trust score of 70+ with explicit network permission declaration
• Rate limiting to prevent spam
• Template support for consistent formatting
• Attachment handling with size limits

Code Example

from agentnode_sdk import load_tool

email_sender = load_tool("email-sender")
result = email_sender.run({
    "to": "team@company.com",
    "subject": "Weekly Analytics Report - March 2026",
    "body_html": "<h2>Key Metrics</h2><p>Revenue up 12% week-over-week...</p>",
    "attachments": ["/reports/weekly_chart.png"]
})
print(f"Sent: {result['message_id']}")

6. Image Analysis

What It Does

Image analysis skills process images and extract information — object detection, text recognition (OCR), classification, description generation, and metadata extraction.

Why Agents Need It

The world is not just text. Agents processing insurance claims need to analyze damage photos. Agents managing e-commerce need to classify product images. Agents handling documents need to read handwritten notes. Image analysis is the skill that gives agents visual understanding.

What to Look For

• Trust score of 65+ (many image tools require API credentials, limiting sandbox verification)
• Multiple analysis modes (OCR, classification, object detection)
• Support for common formats (JPEG, PNG, WebP, TIFF)
• Reasonable file size limits and compression handling

Code Example

from agentnode_sdk import load_tool

analyzer = load_tool("image-analyzer")
result = analyzer.run({
    "image_path": "/uploads/receipt.jpg",
    "analysis_type": "ocr",
    "language": "en"
})
print(f"Extracted text: {result['text']}")
print(f"Confidence: {result['confidence']:.2f}")

7. Text Summarization

What It Does

Text summarization skills condense long documents into shorter versions while preserving key information. Implementations range from extractive (selecting important sentences) to abstractive (generating new summary text).

Why Agents Need It

Agents frequently encounter content that exceeds context window limits. A 200-page legal document, a thread of 500 emails, a collection of 50 research papers — summarization skills let agents process information that would otherwise be impossible to handle. They are the skill that scales agent comprehension.

What to Look For

• Trust score of 70+ with consistent output quality
• Configurable summary length (sentence count, word count, or ratio)
• Support for different content types (articles, conversations, technical docs)
• Chunking for documents that exceed model limits

Code Example

from agentnode_sdk import load_tool

summarizer = load_tool("text-summarizer")
result = summarizer.run({
    "text": long_document_text,
    "max_sentences": 5,
    "style": "executive_brief"
})
print(result["summary"])
print(f"Compression ratio: {result['compression_ratio']:.1%}")

8. API Integration

What It Does

API integration skills act as universal HTTP clients with built-in authentication, retry logic, rate limiting, and response parsing. They let agents interact with any REST or GraphQL API without custom code for each service.

Why Agents Need It

Modern software is built on APIs. An agent that can call Stripe to check a payment, Slack to post a message, or GitHub to create an issue becomes exponentially more useful. API integration is the meta-skill that multiplies every other capability by connecting your agent to the broader software ecosystem.

What to Look For

• Trust score of 70+ with explicit network permission declarations
• Built-in authentication support (API keys, OAuth, Bearer tokens)
• Automatic retry with exponential backoff
• Response validation and error handling

Code Example

from agentnode_sdk import load_tool

api_client = load_tool("api-integrator")
result = api_client.run({
    "method": "POST",
    "url": "https://api.slack.com/api/chat.postMessage",
    "headers": {"Authorization": "Bearer ${SLACK_TOKEN}"},
    "json_body": {
        "channel": "#alerts",
        "text": "Anomaly detected in Q1 revenue data."
    },
    "retry_count": 3
})
print(f"Status: {result['status_code']}")

9. File Conversion

What It Does

File conversion skills transform files between formats: CSV to JSON, HTML to PDF, Markdown to DOCX, Excel to CSV, and dozens of other combinations. They handle encoding, formatting preservation, and batch processing.

Why Agents Need It

Data rarely arrives in the format you need. Your agent receives an Excel spreadsheet but needs JSON for analysis. A client sends a Word document but your pipeline expects Markdown. File conversion is the unglamorous but essential skill that eliminates format friction from every workflow.

What to Look For

• Trust score of 70+ with filesystem permission declarations
• Support for common business formats (CSV, JSON, Excel, PDF, DOCX, Markdown)
• Encoding detection and handling (UTF-8, Latin-1, etc.)
• Batch mode for converting multiple files

Code Example

from agentnode_sdk import load_tool

converter = load_tool("file-converter")
result = converter.run({
    "input_path": "/data/quarterly_report.xlsx",
    "output_format": "json",
    "sheet_name": "Revenue",
    "include_headers": True
})
print(f"Converted: {result['output_path']}")
print(f"Rows: {result['row_count']}, Columns: {result['column_count']}")

10. Data Validation

What It Does

Data validation skills check data against schemas, business rules, and constraints. They verify data types, required fields, value ranges, format patterns (emails, phone numbers, URLs), and cross-field consistency.

Why Agents Need It

Agents that process data without validation are agents waiting to fail. A single malformed email address, an out-of-range date, or a missing required field can break an entire downstream pipeline. Data validation is the defensive skill that keeps agents reliable. It is especially critical for agents that write to databases or trigger real-world actions.

What to Look For

• Trust score of 80+ (validation tools should be among the most reliable in your stack)
• JSON Schema and custom rule support
• Detailed error reporting (not just pass/fail)
• Batch validation for datasets

Code Example

from agentnode_sdk import load_tool

validator = load_tool("data-validator")
result = validator.run({
    "data": {
        "name": "Acme Corp",
        "email": "not-an-email",
        "revenue": -5000,
        "founded": "2026-13-45"
    },
    "schema": {
        "name": {"type": "string", "min_length": 1},
        "email": {"type": "email"},
        "revenue": {"type": "number", "min": 0},
        "founded": {"type": "date", "format": "YYYY-MM-DD"}
    }
})
print(f"Valid: {result['is_valid']}")
for error in result["errors"]:
    print(f"  {error['field']}: {error['message']}")

How to Find and Install These Skills

All 10 of these skill categories — and hundreds of specific implementations — are available on AgentNode. Here is how to get started:

1. Browse verified AI agent tools to search by capability, framework, or keyword.
2. Check the trust score and verification tier before installing anything. Gold and Verified packages have passed sandbox testing.
3. Use the SDK to install and load skills programmatically:

pip install agentnode-sdk

from agentnode_sdk import AgentNodeClient
client = AgentNodeClient()

# Install multiple skills at once
client.resolve_and_install([
    "web-scraping",
    "pdf-extraction",
    "data-validation"
])

If you want to explore skills by what they do rather than by name, the discover agent tools by capability page lets you browse the entire catalog organized by function. And for a deeper walkthrough of how search and discovery works, see the tutorial on how to search and discover agent skills on AgentNode.

Comparing Skill Quality Across Platforms

Not all agent tools are created equal. A web scraper from an unverified registry might install malware alongside its dependencies. A PDF extractor without sandbox testing might silently fail on scanned documents. The difference between a verified skill and an unverified one is the difference between production reliability and an incident at 2 AM.

When evaluating where to source your agent skills, compare agent tool platforms side by side. Key factors to consider include verification depth, trust scoring transparency, cross-framework compatibility, and the size of the active publisher community.

Building Your Agent Skill Stack

The 10 skills in this list are not a menu to pick from — they are a foundation to build on. Most production agents use at least five or six of these capabilities in combination. A research agent might chain web scraping, text summarization, and email sending. A data pipeline agent might combine database queries, data validation, and file conversion.

The key insight is that verified, portable skills compose better than custom one-off tools. When every skill follows the same ANP standard, with typed schemas and declared permissions, your agent can chain them together reliably and safely.

Start with the skills your current project needs, and expand from there. The AgentNode catalog grows every week as publishers contribute new capabilities.

LLM Runtime: Let the Model Handle It

If your agent uses OpenAI or Anthropic tool calling, AgentNodeRuntime handles tool registration, system prompt injection, and the tool loop automatically. The LLM discovers, installs, and runs AgentNode capabilities on its own — no hardcoded tool calls needed.

from openai import OpenAI
from agentnode_sdk import AgentNodeRuntime

runtime = AgentNodeRuntime()

result = runtime.run(
    provider="openai",
    client=OpenAI(),
    model="gpt-4o",
    messages=[{"role": "user", "content": "your task here"}],
)
print(result.content)

The Runtime registers 5 meta-tools (agentnode_capabilities, agentnode_search, agentnode_install, agentnode_run, agentnode_acquire) that let the LLM search the registry, install packages, and execute tools autonomously. Works with Anthropic too — just change provider="anthropic" and pass an Anthropic client.

See the LLM Runtime documentation for the full API reference, trust levels, and manual tool calling.