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:

text

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:

python

# 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:

text

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#

text

Search → Content Extraction → Organize and Save

Use Cases: Competitor research, news tracking, data collection

Template 2: Analysis Report Chain#

text

Fetch Data → Analyze and Process → Generate Report → Send Notification

Use Cases: Stock analysis, operational reports, data dashboards

Template 3: Content Creation Chain#

text

Topic Selection → Search Material → Create Content → Review and Publish

Use Cases: Blog posts, social media management

Configuration Options#

Specifiable within a workflow:

OptionDescriptionExampletimeoutTimeout per skill (seconds)30``retryNumber of retry attempts on failure2``continue_on_errorWhether to continue after failuretrue/false``output_formatFinal output formatjson/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.

json

{
  "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.

json

{
  "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.

json

{
  "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