synapse-config-yaml-guide
About
This skill explains how to configure Synapse plugins using config.yaml files, covering metadata, action definitions, and runtime settings. Use it when developers ask about plugin configuration, action methods, or execution environments. It provides both minimal examples and complete configuration structures for reference.
Quick Install
Claude Code
Recommended/plugin add https://github.com/majiayu000/claude-skill-registrygit clone https://github.com/majiayu000/claude-skill-registry.git ~/.claude/skills/synapse-config-yaml-guideCopy and paste this command in Claude Code to install this skill
Documentation
Synapse Plugin config.yaml Guide
The config.yaml file (or synapse.yaml) defines your plugin's metadata, actions, and runtime configuration.
Minimal Example
name: "My Plugin"
code: my-plugin
version: 1.0.0
category: custom
actions:
train:
entrypoint: plugin.train:TrainAction
method: job
description: "Train a model"
Complete Structure
# Basic metadata
name: "YOLOv8 Object Detection"
code: yolov8
version: 1.0.0
category: neural_net
description: "Train and run YOLOv8 models"
readme: README.md
# Package management
package_manager: pip # or 'uv'
package_manager_options: []
wheels_dir: wheels
# Environment variables
env:
DEBUG: "false"
BATCH_SIZE: "32"
# Runtime environment (Ray)
runtime_env: {}
# Data type configuration
data_type: image
tasks:
- image.object_detection
- image.segmentation
# Actions
actions:
train:
entrypoint: plugin.train:TrainAction
method: job
description: "Train YOLO model"
inference:
entrypoint: plugin.inference:run
method: task
description: "Run inference"
Action Configuration
| Field | Required | Description |
|---|---|---|
entrypoint | Yes | Module path (module.path:ClassName or module.path.function) |
method | No | Execution method: job, task, or serve (default: task) |
description | No | Human-readable description |
Config Sync (Recommended)
Sync entrypoints, input/output types, and hyperparameters from code:
synapse plugin update-config
Execution Methods
| Method | Use Case | Characteristics |
|---|---|---|
job | Training, batch processing | Async, isolated, long-running (100s+) |
task | Interactive operations | Sync, fast startup (<1s), serial per actor |
serve | Model serving, inference | REST API endpoint, auto-scaling |
Entrypoint Formats
Both formats are supported:
- Colon notation:
plugin.train:TrainAction - Dot notation:
plugin.train.TrainAction
Additional Resources
For detailed configuration options:
- references/fields.md - All config.yaml fields
- references/smart-tool.md - Smart tool configuration
GitHub Repository
Related Skills
content-collections
MetaThis skill provides a production-tested setup for Content Collections, a TypeScript-first tool that transforms Markdown/MDX files into type-safe data collections with Zod validation. Use it when building blogs, documentation sites, or content-heavy Vite + React applications to ensure type safety and automatic content validation. It covers everything from Vite plugin configuration and MDX compilation to deployment optimization and schema validation.
creating-opencode-plugins
MetaThis skill provides the structure and API specifications for creating OpenCode plugins that hook into 25+ event types like commands, files, and LSP operations. It offers implementation patterns for JavaScript/TypeScript modules that intercept and extend the AI assistant's lifecycle. Use it when you need to build event-driven plugins for monitoring, custom handling, or extending OpenCode's capabilities.
sglang
MetaSGLang is a high-performance LLM serving framework that specializes in fast, structured generation for JSON, regex, and agentic workflows using its RadixAttention prefix caching. It delivers significantly faster inference, especially for tasks with repeated prefixes, making it ideal for complex, structured outputs and multi-turn conversations. Choose SGLang over alternatives like vLLM when you need constrained decoding or are building applications with extensive prefix sharing.
evaluating-llms-harness
TestingThis Claude Skill runs the lm-evaluation-harness to benchmark LLMs across 60+ standardized academic tasks like MMLU and GSM8K. It's designed for developers to compare model quality, track training progress, or report academic results. The tool supports various backends including HuggingFace and vLLM models.
