Skill Workflow Orchestrator

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:

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:

4. Error Handling

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

1. Total skill chain length is recommended not to exceed 10 steps 2. Complex workflows should be split into multiple simpler chains 3. Sensitive operations (e.g., sending emails, messages) require user confirmation 4. Periodically check the validity and latest versions of all sub-skills

1. Total skill chain length is recommended not to exceed 10 steps 2. Complex workflows should be split into multiple simpler chains 3. Sensitive operations (e.g., sending emails, messages) require user confirmation 4. Periodically check the validity and latest versions of all sub-skills