Skills

skills/custom/data-analysis/
├── SKILL.md              # Main skill definition
├── scripts/
│   └── analyze.py      # Analysis script
├── references/
│   └── sql_schema.md   # Database schema
└── assets/
    └── template.html   # Report template

---
name: data-analysis
description: Analyze datasets with pandas, create visualizations, and generate reports. Use when working with CSV/Excel files, statistical analysis, or data visualization tasks.
license: MIT
allowed-tools:
  - bash
  - read_file
  - write_file
---

# Data Analysis Skill

## Overview
This skill provides comprehensive data analysis capabilities...

## Quick Start
Load a CSV file:
```python
import pandas as pd
df = pd.read_csv('/mnt/user-data/uploads/data.csv')
print(df.describe())

### Frontmatter Fields

<ParamField path="name" type="string" required>
  Unique skill identifier (kebab-case)
</ParamField>

<ParamField path="description" type="string" required>
  When to use this skill. Include specific triggers and use cases.
</ParamField>

<ParamField path="license" type="string">
  License for the skill (e.g., MIT, Apache-2.0)
</ParamField>

<ParamField path="allowed-tools" type="array">
  Tools the skill is allowed to use. Leave empty for all tools.
</ParamField>

## Progressive Loading

Skills are loaded in two stages:

### Stage 1: Skill List (Always Loaded)

The agent receives a list of available skills with their descriptions:

This helps the agent decide which skill to load.

### Stage 2: Full Skill Content (On-Demand)

When the agent decides to use a skill, it loads the full `SKILL.md` content:

```python
# Agent decision
"I need to analyze this CSV file. Let me load the data-analysis skill."

# System loads SKILL.md content
full_skill = load_skill("data-analysis")

# Agent now has detailed instructions
"Now I know how to use pandas for this analysis..."

curl -X POST http://localhost:8001/api/skills/install \
  -F "[email protected]"

cp -r my-skill skills/custom/

cd skills/custom/my-skill
zip -r ../my-skill.skill .

curl -X PUT http://localhost:8001/api/skills/data-analysis \
  -H "Content-Type: application/json" \
  -d '{"enabled": false}'

from src.client import DeerFlowClient

client = DeerFlowClient()
client.update_skill("data-analysis", enabled=False)

def load_skills() -> list[Skill]:
    skills = []
    
    # Scan public and custom directories
    for base_dir in ["skills/public", "skills/custom"]:
        for skill_dir in os.listdir(base_dir):
            skill_md = f"{base_dir}/{skill_dir}/SKILL.md"
            
            if os.path.exists(skill_md):
                # Parse frontmatter and body
                skill = parse_skill(skill_md)
                
                # Check enabled status
                skill.enabled = get_enabled_status(skill.name)
                
                skills.append(skill)
    
    return skills

Run the analysis script:
```bash
python /mnt/skills/custom/data-analysis/scripts/analyze.py data.csv

### References

**Location**: `references/`

**Purpose**: Documentation loaded on-demand

**Example**:
```markdown references/sql_schema.md
# Database Schema

## Users Table
- id: INTEGER PRIMARY KEY
- name: TEXT
- email: TEXT UNIQUE

For database schema details, see [sql_schema.md](references/sql_schema.md)

/mnt/skills/
├── public/
│   ├── deep-research/
│   └── data-analysis/
└── custom/
    └── my-skill/

python /mnt/skills/custom/my-skill/scripts/process.py