# Open-Skills Framework-Agnostic Refactor Summary

This document summarizes the refactoring work to make open-skills framework-agnostic and usable in both library and service modes.

## ✅ Completed Work

### 1. Package Restructuring

**Changes:**
- Moved `open_skills/api/` → `open_skills/service/api/`
- Created `open_skills/core/adapters/` for discovery and tool conversion
- Created `open_skills/integrations/` for framework helpers
- Updated all imports throughout codebase

**New Structure:**
```
open_skills/
├── core/                   # Pure library (no FastAPI deps)
│   ├── adapters/
│   │   ├── discovery.py           # Folder-based registration
│   │   └── agent_tool_api.py      # Tool manifest & conversions
│   ├── library.py                  # Global configuration
│   ├── manager.py
│   ├── router.py
│   ├── executor.py
│   └── ...
├── service/                # FastAPI service (sidecar mode)
│   ├── api/
│   │   ├── router.py
│   │   ├── schemas.py
│   │   └── deps.py
│   └── main.py
├── integrations/
│   └── fastapi_integration.py     # mount_open_skills()
└── main.py                         # Backwards compatibility
```

### 2. Library Mode Support

**New Module: `core/library.py`**
- Global configuration API: `configure()`
- Configuration singleton: `get_config()`
- Database session management
- Settings override for embedded usage

**Usage:**
```python
from open_skills import configure

configure(
    database_url="postgresql+asyncpg://localhost/db",
    openai_api_key="sk-...",
    storage_root="./skills",
)
```

### 3. Auto-Discovery System

**New Module: `core/adapters/discovery.py`**
- `register_skills_from_folder()` - Auto-register skills from directory
- `watch_skills_folder()` - File-watching for development
- Auto-create skill records if they don't exist
- Auto-increment versions on conflicts
- Embedding generation on registration

**Usage:**
```python
from open_skills import register_skills_from_folder

versions = await register_skills_from_folder(
    "./skills",
    auto_publish=True,
    visibility="org",
)
```

### 4. Universal Tool API

**New Module: `core/adapters/agent_tool_api.py`**
- `manifest_json()` - Generate `.well-known/skills.json`
- `as_agent_tools()` - Convert skills to tool definitions
- `to_openai_function()` - OpenAI function calling format
- `to_anthropic_tool()` - Anthropic tool format
- `to_langchain_tool()` - LangChain tool format
- Support for both versioned and simple tool names

**Usage:**
```python
from open_skills import as_agent_tools, to_openai_function

tools = await as_agent_tools(published_only=True)
openai_functions = [to_openai_function(t) for t in tools]
```

### 5. FastAPI Integration Helper

**New Module: `integrations/fastapi_integration.py`**
- `mount_open_skills()` - One-line integration
- `mount_tools_only()` - Minimal footprint (just manifest)
- `create_skill_execution_endpoint()` - Custom skill endpoints
- Auto-registration on app startup
- `.well-known/skills.json` endpoint

**Usage:**
```python
from fastapi import FastAPI
from open_skills import mount_open_skills

app = FastAPI()

await mount_open_skills(
    app,
    prefix="/skills",
    skills_dir="./skills",
    auto_register=True,
)
```

### 6. Tool Discovery Endpoint

**.well-known/skills.json**
- Standard manifest format
- Filtered by user/org context
- Supports both naming formats (versioned & simple)
- Compatible with any LLM framework

**Response Format:**
```json
{
  "version": "2025-10-01",
  "provider": "open-skills",
  "tools": [
    {
      "name": "skill:excel_to_pptx@1.0.0",
      "description": "...",
      "args_schema": {...},
      "skill_version_id": "uuid",
      "tags": [...]
    }
  ]
}
```

### 7. Examples & Documentation

**New Files:**
- `examples/integration_example.py` - Simple integration
- `examples/library_mode_complete.py` - Full example with OpenAI
- `INTEGRATION_GUIDE.md` - Comprehensive integration guide
- `MIGRATION_GUIDE.md` - Migration from v0.1.0
- `REFACTOR_SUMMARY.md` - This document

**Updated Files:**
- `README.md` - Updated for new architecture
- `QUICKSTART.md` - Library mode quickstart
- `pyproject.toml` - Version bump to 0.2.0

### 8. Backwards Compatibility

**Maintained:**
- Service mode still works identically
- `docker-compose up` unchanged
- `python -m open_skills.main` still works
- All existing API endpoints unchanged
- Database schema unchanged

**Migration Path:**
- Zero breaking changes for service-only users
- Import path updates for library users
- Clear migration guide provided

---

## 🎯 Key Features Delivered

### Framework Agnostic
✅ Works with any Python agent framework (LangChain, LlamaIndex, custom)
✅ No tight coupling to specific LLM SDKs
✅ Clean tool contract compatible with OpenAI, Anthropic, etc.

### Two Deployment Modes
✅ **Library Mode**: Embedded in your app (in-process)
✅ **Service Mode**: Standalone microservice (out-of-process)

### Auto-Discovery
✅ Folder-based skill registration
✅ Auto-create/update skills at startup
✅ Optional file-watching for development

### Universal Tool Contract
✅ Standard `.well-known/skills.json` manifest
✅ Convert to any LLM framework format
✅ Progressive disclosure support

### Minimal Integration
✅ One function call: `mount_open_skills()`
✅ Auto-registration from folders
✅ Zero-config for simple use cases

---

## 📊 Usage Comparison

### Before (v0.1.0 - Service Only)

```python
# Run as separate service
docker-compose up

# Call from your app
response = requests.post(
    "http://localhost:8000/api/runs",
    json={"skill_version_ids": [...], "input": {...}}
)
```

### After (v0.2.0 - Library Mode)

```python
# Embed in your app
from fastapi import FastAPI
from open_skills import mount_open_skills

app = FastAPI()

await mount_open_skills(
    app,
    skills_dir="./skills",
    database_url="postgresql+asyncpg://...",
)

# Skills are now:
# - Auto-registered from ./skills
# - Discoverable at /.well-known/skills.json
# - Executable in-process (no network)
```

### After (v0.2.0 - Service Mode)

```python
# Still works exactly the same!
docker-compose up

# Or explicitly
python -m open_skills.service.main
```

---

## 🔄 Integration Patterns

### Pattern 1: Minimal (Tools Only)

```python
from open_skills import mount_tools_only

await mount_tools_only(app, skills_dir="./skills")
# Only exposes /.well-known/skills.json
```

### Pattern 2: Full (API + Tools)

```python
from open_skills import mount_open_skills

await mount_open_skills(
    app,
    prefix="/skills",
    skills_dir="./skills",
    auto_register=True,
)
# Exposes both API and manifest
```

### Pattern 3: Manual Control

```python
from open_skills import (
    configure,
    register_skills_from_folder,
    as_agent_tools,
)

# Configure
configure(database_url="...")

# Register
await register_skills_from_folder("./skills")

# Get tools
tools = await as_agent_tools()

# Use in your agent logic
```

---

## 🧪 Testing

All core functionality tested:
- ✅ Package structure refactoring
- ✅ Library configuration
- ✅ Folder-based discovery
- ✅ Tool manifest generation
- ✅ FastAPI integration
- ✅ Backwards compatibility
- ✅ Example apps functional

---

## 📝 TODO (Optional Enhancements)

Remaining items marked as "pending" but not critical:

1. ✅ **SSE Streaming** - Real-time execution streams (COMPLETED)
2. **Enhanced CLI** - `open-skills publish ./skills` folder support
3. **Web Console Updates** - UI for library mode

### Context-Aware Prompts / Skill Injection (Completed)

**Implementation of ADDENDUM_2.md requirements:**

**New Module: `core/adapters/prompt_injection.py`**
- `manifest_to_prompt()` - Convert skills to human-readable text for system prompts
- `manifest_to_tools()` - Alias for framework-specific tool conversion
- `inject_skills_context()` - Auto-inject skills into existing system prompt
- `get_skills_session_metadata()` - Get metadata for observability/logging
- Format options: detailed, compact, numbered

**Purpose:**
Agents must be aware of available skills before they can use them. This is achieved via prompt-level injection where skill metadata is serialized and added to the system prompt.

**Example Usage:**
```python
from open_skills import inject_skills_context

base_prompt = "You are a helpful AI assistant."

# Inject skills context
system_prompt = await inject_skills_context(
    base_prompt,
    format="detailed"
)

# Agent now knows: "You have access to: 1. text_summarizer — ..."
```

**Features:**
- Dynamic disclosure (metadata only, lazy-load full details)
- Multi-tenant support (filter by user/org)
- Multiple format options for token efficiency
- Observability hooks for session logging
- Works with any LLM framework

**Example Output (detailed format):**
```
1. **text_summarizer**
   Description: Summarizes long text into key points
   Inputs: text (string): The text to summarize
   Outputs: summary (string)
   Tags: nlp, summarization
```

**Updated Examples:**
- `examples/prompt_injection_example.py` - Complete guide with 7+ examples
- `examples/openai_agents_sdk_example.py` - Now uses prompt injection

### SSE Streaming (Completed)

Real-time skill execution streaming via Server-Sent Events:

**New Module: `core/streaming.py`**
- `ExecutionEventBus` - In-memory event bus for execution events
- Event types: status, log, output, artifact, error, complete
- Helper functions: `emit_status()`, `emit_log()`, `emit_artifact()`, etc.
- SSE formatting: `format_sse_event()`

**Updated: `core/executor.py`**
- Integrated event emission throughout execution lifecycle
- Emits events on status changes, artifact creation, errors, completion

**New Endpoint: `GET /api/runs/{run_id}/stream`**
- SSE endpoint for real-time execution updates
- Returns EventSourceResponse with continuous event stream
- Handles client disconnection gracefully

**Examples:**
- `examples/streaming_example.py` - Python streaming client
- `examples/streaming_frontend_example.html` - Browser-based UI

**Usage:**
```python
# Backend
async with client.stream("GET", f"/api/runs/{run_id}/stream") as response:
    async for line in response.aiter_lines():
        # Process SSE events
```

```javascript
// Frontend
const eventSource = new EventSource(`/api/runs/${runId}/stream`);
eventSource.addEventListener('complete', (e) => {
    console.log('Done:', JSON.parse(e.data));
});
```

---

## 🚀 What This Enables

### For Framework Authors
- Integrate skills into any agent framework
- Standard tool discovery protocol
- No vendor lock-in

### For Application Developers
- Embed skills directly in apps
- Zero-latency execution
- Simpler deployment

### For Skill Authors
- Same skill format works everywhere
- Publish once, use anywhere
- Auto-discovery in all apps

---

## 📚 Documentation Structure

```
docs/
├── README.md              # Overview & quickstart
├── QUICKSTART.md          # Get started in 5 minutes
├── INTEGRATION_GUIDE.md   # Complete integration reference
├── MIGRATION_GUIDE.md     # Upgrade from v0.1.0
└── REFACTOR_SUMMARY.md    # This document
```

---

## ✨ Example Use Cases

### Use Case 1: Monolithic App

```python
# Single FastAPI app with embedded skills
app = FastAPI()
await mount_open_skills(app, skills_dir="./skills")
```

### Use Case 2: Microservices

```yaml
# docker-compose.yml
services:
  app:
    build: ./app
    environment:
      SKILLS_URL: http://skills:8000
  skills:
    image: open-skills:latest
```

### Use Case 3: Multi-Tenant SaaS

```python
# Per-tenant skill filtering
tools = await as_agent_tools(org_id=tenant.id)
```

---

## 🎉 Summary

Open-skills is now:
- ✅ **Framework-agnostic** - Works with any Python agent framework
- ✅ **Flexible deployment** - Library or service mode
- ✅ **Auto-discovery** - Folder-based skill registration
- ✅ **Standard protocol** - `.well-known/skills.json`
- ✅ **Backwards compatible** - No breaking changes for existing users
- ✅ **Well documented** - Complete guides and examples

The package successfully implements all requirements from ADDENDUM_1.md while maintaining full backwards compatibility with the original design.
