ReddRadar
Agent skill
synapse-runtime-context-api
Explains how to use Synapse RuntimeContext API. Use when the user asks about "RuntimeContext", "ctx.", "logging", "progress tracking", "set_progress", "set_metrics", "log_message", "BaseStepContext", "TrainContext", "ExportContext", "UploadContext", "InferenceContext", "AddTaskDataContext", or needs help with synapse plugin context and logging.
Stars
163
Forks
31
Install this agent skill to your Project
npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/data/runtime-context-api
SKILL.md
Synapse RuntimeContext API
RuntimeContext provides logging, progress tracking, and client access for plugin actions.
Quick Reference
python
from synapse_sdk.plugins.context import RuntimeContext
def train(params: TrainParams, ctx: RuntimeContext) -> dict:
# Progress tracking
ctx.set_progress(0, 100)
# Metrics recording
ctx.set_metrics({'loss': 0.05}, 'training')
# User-facing message
ctx.log_message('Training started', 'info')
# Event logging
ctx.log('checkpoint', {'epoch': 5}, '/path/to/file')
# Debug logging
ctx.log_dev_event('Debug info', {'data': 'value'})
# Signal completion
ctx.end_log()
return {'status': 'completed'}
Context Attributes
| Attribute | Type | Description |
|---|---|---|
ctx.logger |
BaseLogger | Logger instance |
ctx.env |
PluginEnvironment | Environment variables |
ctx.job_id |
str | None | Job tracking ID |
ctx.client |
BackendClient | None | API client |
ctx.agent_client |
AgentClient | None | Ray operations client |
ctx.checkpoint |
dict | None | Pretrained model info |
Progress Tracking
python
# Basic progress
ctx.set_progress(current=50, total=100)
# Progress with category (multi-phase)
ctx.set_progress(5, 10, category='download')
ctx.set_progress(3, 100, category='training')
Metrics Recording
python
ctx.set_metrics(
value={'loss': 0.05, 'accuracy': 0.95},
category='training'
)
Logging Methods
| Method | Description |
|---|---|
log(event, data, file) |
Log event with data |
log_message(message, context) |
User-facing message |
log_dev_event(message, data) |
Debug/dev event |
end_log() |
Signal completion |
Message Contexts
python
ctx.log_message('Success!', 'success') # Green
ctx.log_message('Warning', 'warning') # Yellow
ctx.log_message('Error', 'danger') # Red
ctx.log_message('Info', 'info') # Blue (default)
Environment Access
python
# Get environment variable
api_key = ctx.env.get('API_KEY', '')
debug_mode = ctx.env.get('DEBUG', 'false') == 'true'
Checkpoint Info
python
if ctx.checkpoint:
model_path = ctx.checkpoint.get('path')
category = ctx.checkpoint.get('category') # 'base' or fine-tuned
Specialized Step Contexts
For step-based workflows, specialized contexts extend BaseStepContext:
python
from synapse_sdk.plugins.actions.train import TrainContext
from synapse_sdk.plugins.actions.export import ExportContext
from synapse_sdk.plugins.actions.upload import UploadContext
from synapse_sdk.plugins.actions.inference import InferenceContext, DeploymentContext
from synapse_sdk.plugins.actions.add_task_data import AddTaskDataContext
| Context | Purpose | Key Attributes |
|---|---|---|
TrainContext |
Training workflows | dataset, model_path, model |
ExportContext |
Export workflows | results, exported_count, output_path |
UploadContext |
Upload workflows | uploaded_files, data_units |
InferenceContext |
Inference workflows | model, results, processed_count |
DeploymentContext |
Deployment workflows | serve_app_id, deployed |
AddTaskDataContext |
Pre-annotation workflows | task_ids, success_count, failures |
All step contexts include:
runtime_ctx- Reference to RuntimeContextset_progress()/set_metrics()- Auto-uses step name as categorylog()- Event logging
See step-workflow skill for details.
Additional Resources
For advanced patterns:
- references/logging-patterns.md - Advanced logging
- references/specialized-contexts.md - Step context details
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