fliiq-skillet 0.0.1

HTTP-native, OpenAPI-first framework for packaging and running reusable skills

Fliiq Skillet 🍳

Skillet is an HTTP-native, OpenAPI-first framework for packaging and running reusable skills (micro-functions) that Large-Language-Model agents can call over the network.

"Cook up a skill in minutes, serve it over HTTPS, remix it in a workflow."

---

Why another spec?

Current community standard MCP servers are great for quick sandbox demos inside an LLM playground, but painful when you try to ship real-world agent workflows:

MCP Pain Point	Skillet Solution
Default stdio transport; requires local pipes and custom RPC	Pure HTTP + JSON with an auto-generated OpenAPI contract
One bespoke server per repo; Docker mandatory	Single-file Skillfile.yaml → deploy to Cloudflare Workers, AWS Lambda or raw FastAPI
No discovery or function manifest	Registry + /openapi.json enable automatic client stubs & OpenAI function-calling
Heavy cold-start if each agent needs its own container	Skills are tiny (≤ 5 MB) Workers; scale-to-zero is instant
Secrets baked into code	Standard .skillet.env + runtime injection
Steep learning curve for non-infra devs	pip install fliiq-skillet → skillet new hello_world → done

Key Concepts

• Skilletfile.yaml ≤50 lines — declarative inputs/outputs, runtime & entry-point
• skillet dev — hot-reload FastAPI stub for local testing
• skillet deploy — one-command deploy to Workers/Lambda (more targets soon)
• Registry — browse, star and import skills; share community "recipes" in the Cookbook
• Cookbook — visual builder that chains skills into agent workflows

Quick start (Python)

pip install fliiq-skillet
skillet new fetch_html --runtime python
cd fetch_html
skillet dev          # Swagger UI on http://127.0.0.1:8000

Examples

The examples/ directory contains reference implementations of Skillet skills:

• anthropic_fetch - Fetches HTML content from URLs. A Skillet-compatible implementation of the Anthropic fetch MCP.
• anthropic_time - Returns the current time in any timezone. A Skillet-compatible implementation of the Anthropic time MCP.
• anthropic_memory - A stateful skill that provides a simple in-memory key-value store. A Skillet-compatible implementation of the Anthropic memory MCP.

Each example includes:

• A complete Skilletfile.yaml configuration
• API documentation and usage examples
• An automated test.sh script to verify functionality

Testing the Examples Using the Automated Test Scripts

To test any example, you'll need two terminal windows:

1. First terminal - Start the server:

cd examples/[example_name]  # e.g., anthropic_fetch, anthropic_time, anthropic_memory
pip install -r requirements.txt
uvicorn skillet_runtime:app --reload

2. Second terminal - Run the tests:

cd examples/[example_name]  # same directory as above
./test.sh

A successful test run will show:

• All test cases executing without errors
• Expected JSON responses for successful operations
• Proper error handling for edge cases
• Server logs in the first terminal showing request handling

For example, a successful time skill test should show:

--- Testing Time Skillet ---
1. Getting current time in UTC (default)...
{"iso_8601":"2025-06-12T04:46:33+00:00", ...}

2. Getting current time in America/New_York...
{"iso_8601":"2025-06-12T00:46:33-04:00", ...}

3. Testing with an invalid timezone...
{"detail":"400: Invalid timezone: 'Mars/Olympus_Mons'..."}

See each example's specific README for detailed API usage instructions and expected responses.

Tutorials: Using Skillet in Your Applications

The tutorials/ directory contains example applications that demonstrate how to integrate Skillet skills into your own applications. These tutorials show real-world usage patterns and best practices for developers who want to use Skillet skills in their projects.

Available Tutorials

• openai_time_demo - Shows how to use OpenAI's GPT models with the Skillet time skill. This tutorial demonstrates:
  • Setting up OpenAI function calling with Skillet endpoints
  • Making HTTP requests to Skillet services
  • Handling responses and errors
  • Building an interactive CLI application

Each tutorial includes:

• Complete working code with comments
• Clear setup instructions
• Dependencies and environment configuration
• Best practices for production use

Using the Tutorials

1. Choose a tutorial that matches your use case
2. Follow the README instructions in the tutorial directory
3. Use the code as a template for your own application

The tutorials are designed to be minimal yet production-ready examples that you can build upon. They demonstrate how to:

• Make API calls to Skillet skills
• Handle authentication and environment variables
• Process responses and handle errors
• Structure your application code

For example, to try the OpenAI + Skillet time demo:

# First, start the Skillet time service
cd examples/anthropic_time
pip install -r requirements.txt
uvicorn skillet_runtime:app --reload

# In a new terminal, run the OpenAI demo
cd tutorials/openai_time_demo
pip install -r requirements.txt
python main.py