Skill Workflow Orchestrator
Overview
This skill orchestrates multiple sub-skills into automated pipelines. A complete skill chain can be triggered through natural language descriptions, supporting sequential execution, conditional branching, and error handling.
Use Cases
Automatically triggers when user descriptions involve multi-step tasks, for example:
- "Search for the latest AI news, generate a summary report, and then email it to me."
- "Check the stock price; if it rises more than 5%, remind me."
- "Read the PDF file, extract the content, summarize it, and save it to notes."
Workflow Definition
1. Parse User Intent
Parse the user's natural language description into a structured skill chain:
User: "Search AI news → Summarize → Send email"
→ Parsed into:
[
{"skill": "multi-search-engine", "task": "Search latest AI news"},
{"skill": "content-summarizer", "task": "Generate summary"},
{"skill": "email-skill", "task": "Send email"}
]
2. Sequential Execution
Invoke each skill in order, with the output of the previous skill serving as the input for the next:
# Pseudocode example
results = []
for step in workflow:
skill = load_skill(step.skill)
input_data = results[-1] if results else None
result = skill.execute(step.task, input_data)
results.append(result)
3. Conditional Branching
Supports if/else logic:
If [condition] → Execute [Skill A]
Else → Execute [Skill B]
Supported comparison operators:
- Numeric comparison:
>,<,>=,<=,==,!= - String containment:
contains,startswith,endswith - Boolean checks:
is_true,is_false,exists
4. Error Handling
- Retry Mechanism: Automatically retry failed steps up to 2 times
- Skip and Continue: Optionally continue executing subsequent steps when a step fails
- Fallback Execution: Support defining an alternative skill chain on failure
Built-in Templates
Template 1: Information Gathering Chain
Search → Content Extraction → Organize and Save
Use Cases: Competitor research, news tracking, data collection
Template 2: Analysis Report Chain
Fetch Data → Analyze and Process → Generate Report → Send Notification
Use Cases: Stock analysis, operational reports, data dashboards
Template 3: Content Creation Chain
Topic Selection → Search Material → Create Content → Review and Publish
Use Cases: Blog posts, social media management
Configuration Options
Specifiable within a workflow:
| Option | Description | Example |
|---|---|---|
timeout |
Timeout per skill (seconds) | 30 |
retry |
Number of retry attempts on failure | 2 |
continue_on_error |
Whether to continue after failure | true/false |
output_format |
Final output format | json/markdown/text |
Usage Examples
Example 1: Simple Chain
User: Search for the latest developments in quantum computing, generate a summary, and save it to notes.
{
"steps": [
{"skill": "multi-search-engine", "task": "Latest developments in quantum computing"},
{"skill": "content-summarizer", "task": "Generate summary"},
{"skill": "ima-skill", "task": "Save to notes"}
]
}
Example 2: With Conditional Branching
User: Check the price of BTC; if it drops below $50,000, remind me to sell.
{
"steps": [
{"skill": "neodata-financial-search", "task": "BTC price"},
{
"condition": "price < 50000",
"then": [{"skill": "message", "task": "Remind to sell"}],
"else": []
}
]
}
Example 3: With Error Handling
User: Read this PDF, extract the table data; if it fails, send me an email notification.
{
"steps": [
{"skill": "pdf", "task": "Read PDF", "retry": 3},
{"skill": "xlsx", "task": "Extract table data"}
],
"on_error": {
"skill": "email-skill",
"task": "Send error notification"
}
}
Notes
- Total skill chain length is recommended not to exceed 10 steps
- Complex workflows should be split into multiple simpler chains
- Sensitive operations (e.g., sending emails, messages) require user confirmation
- Periodically check the validity and latest versions of all sub-skills