Agent Skills

skills/
├── financial-analysis/
│   └── SKILL.md
├── code-review/
│   └── SKILL.md
├── data-visualization/
│   └── SKILL.md
└── technical-writing/
    └── SKILL.md

---
name: skill-name
description: Brief description of the skill's purpose and capabilities
---

# Skill Title

Detailed instructions for the skill...

## Methodology

Step-by-step instructions...

## Guidelines

Best practices and rules...

## Examples

Usage examples...

---
name: financial-analysis
description: Perform comprehensive financial analysis including DCF modeling, ratio analysis, and financial statement evaluation for companies and investment opportunities
---

# Financial Analysis Skill

When performing financial analysis, follow these systematic steps to ensure thorough and accurate evaluation:

## Core Methodology

### 1. Data Collection and Verification
- Gather historical financial statements (income statement, balance sheet, cash flow)
- Verify data sources for accuracy and completeness
- Identify any anomalies or missing data points

### 2. Financial Ratio Analysis
Calculate and analyze key financial ratios:
- **Profitability**: EBITDA margin, net profit margin, ROE, ROA
- **Liquidity**: Current ratio, quick ratio, cash ratio
- **Leverage**: Debt-to-equity, interest coverage ratio
- **Efficiency**: Asset turnover, inventory turnover

### 3. Valuation Models
Build appropriate valuation models:
- **DCF Analysis**: Project free cash flows, determine WACC, calculate terminal value
- **Comparable Company Analysis**: Identify peers, analyze multiples (P/E, EV/EBITDA)
- **Precedent Transactions**: Review similar deals for valuation benchmarks

## Guidelines

- Always use conservative assumptions when uncertain
- Cross-validate findings with multiple valuation methods
- Clearly document all assumptions and their rationale
- Present results with appropriate caveats and risk factors

## Key Outputs

Your analysis should produce:
1. Executive summary of findings
2. Detailed financial model with assumptions
3. Valuation range with sensitivity analysis
4. Investment recommendation with risk assessment

from swarms import Agent

agent = Agent(
    agent_name="Versatile-Agent",
    model_name="claude-sonnet-4-6",
    skills_dir="./skills",
    max_loops=1,
)

# Only relevant skills are loaded based on the task
response = agent.run(
    "Analyze the financial statements and create a visualization of the revenue trends"
)
# Loads: financial-analysis and data-visualization skills

from swarms import Agent

agent = Agent(
    agent_name="Financial-Analyst",
    model_name="claude-sonnet-4-6",
    skills_dir="./skills",
    max_loops=1,
)

agent.handle_skills()  # No task -> loads and appends every skill in skills_dir

response = agent.run("Perform a DCF valuation of Tesla")

mkdir -p skills/my-skill

---
name: code-review
description: Perform thorough code reviews focusing on security, performance, maintainability, and best practices
---

# Code Review Skill

When reviewing code, follow this systematic approach:

## Review Checklist

### 1. Security
- [ ] No hardcoded credentials or API keys
- [ ] Input validation for all user inputs
- [ ] Proper authentication and authorization
- [ ] SQL injection prevention
- [ ] XSS protection

### 2. Performance
- [ ] Efficient algorithms and data structures
- [ ] No unnecessary database queries
- [ ] Proper caching where appropriate
- [ ] Optimized loops and iterations

### 3. Code Quality
- [ ] Follows language conventions and style guide
- [ ] Clear and descriptive variable names
- [ ] Functions are single-purpose and small
- [ ] Proper error handling
- [ ] Adequate comments for complex logic

### 4. Testing
- [ ] Unit tests for all functions
- [ ] Edge cases covered
- [ ] Integration tests where needed
- [ ] Test coverage > 80%

## Review Format

Provide feedback in this structure:
1. Summary of findings (high-level overview)
2. Critical issues (must fix)
3. Important suggestions (should fix)
4. Minor improvements (nice to have)
5. Positive observations (what's done well)

## Communication Guidelines

- Be constructive and specific
- Provide examples and alternatives
- Explain the "why" behind suggestions
- Acknowledge good code when you see it

agent = Agent(
    agent_name="Code-Reviewer",
    model_name="claude-sonnet-4-6",
    skills_dir="./skills",
)

code = """
def login(username, password):
    query = f"SELECT * FROM users WHERE username='{username}' AND password='{password}'"
    return db.execute(query)
"""

review = agent.run(f"Review this code:\n{code}")
print(review)

---
name: data-visualization
description: Create effective data visualizations and charts using best practices for data communication
---

# Data Visualization Skill

## Visualization Selection

### Choose the right chart type:
- **Comparison**: Bar charts, grouped bar charts
- **Trend over time**: Line charts, area charts
- **Distribution**: Histograms, box plots
- **Relationship**: Scatter plots, bubble charts
- **Composition**: Pie charts (use sparingly), stacked bar charts
- **Geographic**: Maps, choropleth maps

## Design Principles

1. **Clarity**: Make the message immediately clear
2. **Simplicity**: Remove chart junk and unnecessary elements
3. **Accuracy**: Don't mislead with scale or perspective
4. **Accessibility**: Use colorblind-friendly palettes

## Best Practices

- Start y-axis at zero for bar charts
- Use clear, descriptive labels
- Include data sources
- Provide context with annotations
- Choose appropriate color schemes
- Ensure text is readable

## Code Template

```python
import matplotlib.pyplot as plt
import seaborn as sns

# Set style
sns.set_style("whitegrid")
plt.figure(figsize=(12, 6))

# Create visualization
# ... your code ...

# Add labels and title
plt.xlabel("X Label", fontsize=12)
plt.ylabel("Y Label", fontsize=12)
plt.title("Descriptive Title", fontsize=14)

# Show plot
plt.tight_layout()
plt.show()

### Technical Writing Skill

```markdown
---
name: technical-writing
description: Write clear, accurate technical documentation including API docs, tutorials, and guides
---

# Technical Writing Skill

## Documentation Structure

### 1. Introduction
- What is it?
- Why should I use it?
- Quick example

### 2. Getting Started
- Prerequisites
- Installation
- Basic setup
- "Hello World" example

### 3. Core Concepts
- Key terminology
- Architecture overview
- Main features

### 4. How-To Guides
- Task-oriented instructions
- Step-by-step procedures
- Real-world examples

### 5. API Reference
- Complete API documentation
- Parameters and return values
- Code examples

## Writing Guidelines

- Use active voice
- Be concise and direct
- Use consistent terminology
- Include code examples
- Explain the "why" not just the "how"
- Test all code samples
- Use headings and structure
- Include diagrams where helpful

## Code Documentation Format

```python
def function_name(param1: type1, param2: type2) -> return_type:
    """
    Brief description of what the function does.
    
    More detailed explanation if needed, including:
    - When to use this function
    - Important considerations
    - Edge cases
    
    Args:
        param1: Description of param1
        param2: Description of param2
    
    Returns:
        Description of return value
    
    Raises:
        ErrorType: When and why this error occurs
    
    Examples:
        >>> function_name("value1", "value2")
        "expected output"
    """
    pass

## Skill Loading Internals

### How Skills are Loaded

```python
# skills_dir is only stored during __init__; skills are actually loaded
# and injected into the system prompt inside agent.run(task), once per call
agent = Agent(
    skills_dir="./skills",  # Path to skills directory
    model_name="claude-sonnet-4-6",
)

response = agent.run("Perform a DCF valuation of Tesla")
# On this call: skills metadata is loaded from SKILL.md frontmatter,
# scored against the task by similarity, and relevant skill content
# is appended to the system prompt

from swarms.structs.dynamic_skills_loader import DynamicSkillsLoader

# Manual skill loading
loader = DynamicSkillsLoader(skills_dir="./skills")

# Load skills relevant to a task
relevant_skills = loader.load_relevant_skills(
    "Perform financial analysis and create visualizations"
)

print(f"Loaded {len(relevant_skills)} relevant skills")

# Good - Single, focused skill
---
name: api-design
description: Design RESTful APIs following best practices
---

# Bad - Too broad
---
name: software-engineering
description: All software engineering best practices
---

## Example Use Cases

- **Public Company Valuation**: "Analyze Tesla's financials and provide a DCF valuation"
- **Private Investment**: "Evaluate this startup's unit economics and runway"
- **M&A Analysis**: "Assess the financial implications of this acquisition"

## Core Methodology

### 1. Data Collection
- Gather historical financial statements
- Verify data sources
- Identify anomalies

### 2. Analysis
- Calculate key ratios
- Build valuation models
- Perform sensitivity analysis

## Guidelines

- Always use conservative assumptions when uncertain
- Cross-validate findings with multiple methods
- Clearly document all assumptions
- Present results with appropriate caveats

# Copy skills to another project
cp -r skills/financial-analysis ../other-project/skills/

# Or create a skills library repository
git clone https://github.com/myorg/agent-skills-library.git skills