from praisonaiagents import Agent

# Create an agent with specific skills
agent = Agent(
    name="PDF Assistant",
    instructions="You are a helpful assistant.",
    skills=["./skills/pdf-processing"],  # Direct skill paths
)

# Or discover skills from directories
agent = Agent(
    name="Multi-Skill Agent",
    instructions="You are a versatile assistant.",
    skills_dirs=["./skills"],  # Scan for skill subdirectories
)

from praisonaiagents import SkillManager

# Create a skill manager
manager = SkillManager()

# Discover skills from directories
manager.discover(["./skills"])

# Generate prompt XML for system prompt injection
prompt_xml = manager.to_prompt()
print(prompt_xml)

---
name: pdf-processing
description: Process and extract information from PDF documents. Use this skill when the user asks to read, analyze, or extract data from PDF files.
license: Apache-2.0
compatibility: Works with PraisonAI Agents
metadata:
  author: your-org
  version: "1.0"
allowed-tools: Read Write  # Optional: space-delimited tool list
---

# PDF Processing Skill

## Overview

This skill enables agents to process PDF documents...

## Instructions

1. First, verify the PDF file exists
2. Use appropriate tools to read the PDF content
3. Extract text while preserving structure

my-skill/
├── SKILL.md          # Required: Skill definition
├── scripts/          # Optional: Executable code
├── references/       # Optional: Additional documentation
└── assets/           # Optional: Templates, data files

praisonai skills list
praisonai skills list --dirs ./my-skills ./other-skills

praisonai skills validate --path ./my-skill

praisonai skills create --name my-new-skill --description "A custom skill"

praisonai skills prompt --dirs ./skills

from praisonaiagents import SkillManager

manager = SkillManager()

# Discover skills
count = manager.discover(["./skills"], include_defaults=True)

# Add a single skill
skill = manager.add_skill("./path/to/skill")

# Get a skill by name
skill = manager.get_skill("pdf-processing")

# Activate a skill (load instructions)
manager.activate_by_name("pdf-processing")

# Get instructions
instructions = manager.get_instructions("pdf-processing")

# Generate prompt XML
prompt = manager.to_prompt()

from praisonaiagents.skills import SkillLoader

loader = SkillLoader()

# Level 1: Load metadata only
skill = loader.load_metadata("./my-skill")

# Level 2: Activate (load instructions)
loader.activate(skill)
print(skill.instructions)

# Level 3: Load resources
scripts = loader.load_scripts(skill)
references = loader.load_references(skill)
assets = loader.load_assets(skill)

from praisonaiagents.skills import validate, validate_metadata

# Validate a skill directory
errors = validate("./my-skill")
if errors:
    for error in errors:
        print(f"Error: {error}")
else:
    print("Skill is valid!")

# Validate metadata dict
errors = validate_metadata({"name": "test", "description": "Test skill"})

"""
Agent Skills with OpenAI API using gpt-4o-mini.
Flat file code - no functions or classes.
"""

import os
from pathlib import Path
import yaml
from openai import OpenAI

# Initialize OpenAI client
client = OpenAI()

# Step 1: Discover skills from a directory
skill_dirs = ["./.praison/skills"]
skills = []

for dir_path in skill_dirs:
    dir_path = Path(dir_path).expanduser()
    if not dir_path.exists():
        continue
    
    for skill_path in dir_path.iterdir():
        if not skill_path.is_dir():
            continue
        
        skill_md = skill_path / "SKILL.md"
        if skill_md.exists():
            content = skill_md.read_text()
            
            # Extract frontmatter
            if content.startswith("---"):
                parts = content.split("---", 2)
                frontmatter = yaml.safe_load(parts[1])
                instructions = parts[2].strip()
                
                skills.append({
                    "name": frontmatter["name"],
                    "description": frontmatter["description"],
                    "instructions": instructions
                })

print(f"Discovered {len(skills)} skills")

# Step 2: Generate XML for system prompt
xml_parts = ["<available_skills>"]
for skill in skills:
    xml_parts.append(f"""  <skill>
    <name>{skill['name']}</name>
    <description>{skill['description']}</description>
  </skill>""")
xml_parts.append("</available_skills>")
skills_xml = "\n".join(xml_parts)

# Step 3: Create system prompt with skills
system_prompt = f"""You are a helpful AI assistant.

{skills_xml}

When a request matches a skill, respond with: "Using [skill-name] skill"
"""

# Step 4: Make API call with gpt-4o-mini
messages = [
    {"role": "system", "content": system_prompt},
    {"role": "user", "content": "Extract text from document.pdf"}
]

response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=messages
)

assistant_msg = response.choices[0].message.content
print(f"Assistant: {assistant_msg}")
messages.append({"role": "assistant", "content": assistant_msg})

# Step 5: If skill mentioned, activate it
activated = False
for skill in skills:
    if skill["name"] in assistant_msg.lower():
        print(f"Activating {skill['name']} skill")
        messages.append({
            "role": "user",
            "content": f"<skill_context>\n{skill['instructions']}\n</skill_context>"
        })
        activated = True
        break

# Step 6: Continue conversation with skill context
if activated:
    messages.append({"role": "user", "content": "Now help me with this task"})
    
    response = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=messages
    )
    
    print(f"Assistant: {response.choices[0].message.content}")

# Show token usage
print(f"Total tokens: {response.usage.total_tokens}")

"""
Agent Skills with Anthropic Claude API.
Flat file code using Claude Sonnet 4.
"""

import os
from pathlib import Path
import yaml
from anthropic import Anthropic

# Initialize Anthropic client
client = Anthropic()

# Step 1: Discover skills from directory
skill_dirs = ["./.praison/skills"]
skills = []

for dir_path in skill_dirs:
    dir_path = Path(dir_path).expanduser()
    if not dir_path.exists():
        continue
    
    for skill_path in dir_path.iterdir():
        if not skill_path.is_dir():
            continue
        
        skill_md = skill_path / "SKILL.md"
        if skill_md.exists():
            content = skill_md.read_text()
            
            # Extract frontmatter
            if content.startswith("---"):
                parts = content.split("---", 2)
                frontmatter = yaml.safe_load(parts[1])
                instructions = parts[2].strip()
                
                skills.append({
                    "name": frontmatter["name"],
                    "description": frontmatter["description"],
                    "instructions": instructions
                })

print(f"Discovered {len(skills)} skills")

# Step 2: Generate XML for system prompt
xml_parts = ["<available_skills>"]
for skill in skills:
    xml_parts.append(f"""  <skill>
    <name>{skill['name']}</name>
    <description>{skill['description']}</description>
  </skill>""")
xml_parts.append("</available_skills>")
skills_xml = "\n".join(xml_parts)

# Step 3: Create system prompt with skills
system_prompt = f"""You are a helpful AI assistant.

{skills_xml}

When a request matches a skill, respond with: "Using [skill-name] skill"
"""

# Step 4: Make API call with Claude
messages = [
    {"role": "user", "content": "Extract text from document.pdf"}
]

response = client.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=1024,
    system=system_prompt,
    messages=messages
)

assistant_msg = response.content[0].text
print(f"Assistant: {assistant_msg}")
messages.append({"role": "assistant", "content": assistant_msg})

# Step 5: If skill mentioned, activate it
activated = False
for skill in skills:
    if skill["name"] in assistant_msg.lower():
        print(f"Activating {skill['name']} skill")
        messages.append({
            "role": "user",
            "content": f"<skill_context>\n{skill['instructions']}\n</skill_context>"
        })
        activated = True
        break

# Step 6: Continue conversation with skill context
if activated:
    messages.append({"role": "user", "content": "Now help me with this task"})
    
    response = client.messages.create(
        model="claude-sonnet-4-20250514",
        max_tokens=1024,
        system=system_prompt,
        messages=messages
    )
    
    print(f"Assistant: {response.content[0].text}")

# Show token usage
print(f"Total tokens: {response.usage.input_tokens + response.usage.output_tokens}")

from praisonaiagents import Agent

# Agent with skill management capabilities
agent = Agent(
    name="Learning Assistant",
    instructions="Learn from user interactions and create skills for future use.",
    tools=["skill_manage", "skills_list", "skill_view"]
)

# Agent can now call:
# skill_manage(action="create", name="user-preference", content="Remember user likes...")
# skill_manage(action="edit", name="user-preference", content="Updated preferences...")
# skills_list() - to see all available skills
# skill_view("user-preference") - to examine skill details

<available_skills>
  <skill>
    <name>pdf-processing</name>
    <description>...</description>
    <location>/path/to/SKILL.md</location>
  </skill>
</available_skills>

def format_skill_for_prompt(skill: SkillMetadata) -> str:
    return f"""  <skill>
    <name>{name}</name>
    <description>{description}</description>
    <location>{location}</location>
  </skill>"""

skill-name/
├── SKILL.md           # Required ✅
├── scripts/           # Optional ✅
├── references/        # Optional ✅
└── assets/            # Optional ✅