name: data-machine description: "Self-scheduling execution layer for autonomous task orchestration. Use for queuing tasks, chaining pipeline executions, scheduling recurring work, and 24/7 autonomous operation via Agent Ping webhooks." compatibility: "WordPress 6.9+ with Data Machine plugin. WP-CLI required for queue management." metadata: version: "1.0.1"

Data Machine Skill

A self-scheduling execution layer for AI agents. Not just content automation — it's how agents schedule themselves to achieve goals autonomously.

When to Use This Skill

Use this skill when:

• Setting up automated workflows (content generation, publishing, notifications)
• Creating self-scheduling patterns (reminders, recurring tasks)
• Building multi-phase projects with queued task progression
• Configuring Agent Ping webhooks to trigger external agents

Core Philosophy

Data Machine is designed with AI agents as primary users. It functions as a reminder system + task manager + workflow executor all in one.

Three Key Concepts

1. Flows operate on schedules — Configure "ping me at X time to do Y"
2. Step-level prompt queues — Each ping can be a different task instruction
3. Multiple purpose-specific flows — Separate flows for separate concerns

Mental Model

Architecture Overview

Execution Model

Pipeline (template) → Flow (instance) → Job (execution)

• Pipeline: Reusable workflow template with steps
• Flow: Instance of a pipeline with specific configuration and schedule
• Job: Single execution of a flow

Step Types

Scheduling Options

Configure via scheduling_config in the flow:

Working with Flows

List Flows

wp datamachine flows list

Get Flow Details

wp datamachine flows get <flow_id>

Run a Flow Manually

wp datamachine flows run <flow_id>

Check Job Status

wp datamachine jobs list --limit=10

Prompt Queues

Both AI and Agent Ping steps support queues via QueueableTrait. If the configured prompt is empty and queue_enabled is true, the step pops from its queue.

This enables varied task instructions per execution — not the same prompt every time.

Queue Management

# Add to queue
wp datamachine flows queue add <flow_id> "Task instruction here"
 
# List queue contents
wp datamachine flows queue list <flow_id>
 
# Clear queue
wp datamachine flows queue clear <flow_id>

Chaining Pattern

When an agent receives a ping, it should:

1. Execute the immediate task
2. Queue the next logical task (if continuation needed)
3. Let the cycle continue

Ping: "Phase 1: Design the architecture"
  → Agent designs, writes DESIGN.md
  → Agent queues: "Phase 2: Implement schema per DESIGN.md"
  
Ping: "Phase 2: Implement schema per DESIGN.md"  
  → Agent implements
  → Agent queues: "Phase 3: Build API endpoints"

The queue becomes the agent's persistent project memory — multi-phase work is tracked in the queue, not held in context.

Purpose-Specific Flows

Critical pattern: Don't try to do everything in one flow. Create separate flows for separate concerns:

Flow: Content Generation (queue-driven)
  → AI Step (pops topic from queue) → Publish → Agent Ping
 
Flow: Content Ideation (daily)  
  → Agent Ping: "Review analytics, add topics to content queue"
 
Flow: Weekly Review (cron: Monday 9am)
  → Agent Ping: "Analyze last week's performance"
 
Flow: Coding Tasks (manual, queue-driven)
  → Agent Ping (pops from queue): specific coding task instructions

Each flow has its own:

• Schedule: When it runs
• Queue: Task backlog specific to that workflow
• Purpose: Single responsibility, clear scope

Agent Ping Configuration

Agent Ping steps send webhooks to external agent frameworks (OpenClaw, LangChain, custom handlers).

Handler Configuration

• webhook_url: Where to send the ping
• prompt: Static prompt, or leave empty to use queue
• queue_enabled: Whether to pop from queue when prompt is empty

Webhook Payload

The ping includes:

• Flow and job context
• The prompt (from config or queue)
• Any data from previous steps

Note: Data Machine is agent-agnostic. It sends webhooks — whatever listens on the URL handles the prompt.

Taxonomy Handling (Publishing)

When publishing WordPress content, taxonomies can be handled three ways:

AI Decides Mode

When ai_decides is set:

1. TaxonomyHandler generates a tool parameter for that taxonomy
2. AI provides term names in its tool call
3. Handler assigns terms (creating if needed)

• Hierarchical taxonomies (category): expects single string
• Non-hierarchical (tags): expects array of strings

Best Practice: AI taxonomy selection works for simple cases. For complex categorization, use skip and assign programmatically after publish.

Key AI Tools

skip_item

Allows AI to skip items that shouldn't be processed:

Before generating content:
1. Search for similar existing posts
2. If duplicate found, use skip_item("duplicate of [existing URL]")

The tool marks the item as processed and sets job status to agent_skipped.

local_search

Search site content for duplicate detection:

# Search by title
local_search(query="topic name", title_only=true)

Tip: Search for core topic, not exact title. "pelicans dangerous" catches "Are Australian Pelicans Dangerous?"

CLI Reference

Note: If running WP-CLI as root, add --allow-root to commands.

# Settings
wp datamachine settings list
wp datamachine settings get <key>
wp datamachine settings set <key> <value>
 
# Flows
wp datamachine flows list
wp datamachine flows get <flow_id>
wp datamachine flows run <flow_id>
 
# Queues
wp datamachine flows queue add <flow_id> "prompt"
wp datamachine flows queue list <flow_id>
wp datamachine flows queue clear <flow_id>
 
# Jobs
wp datamachine jobs list [--status=<status>] [--limit=<n>]
wp datamachine jobs get <job_id>

Debugging

Check Logs

tail -f wp-content/uploads/datamachine-logs/datamachine-pipeline.log

Failed Jobs

wp datamachine jobs list --status=failed

Scheduled Actions

# List pending actions
wp action-scheduler run --hooks=datamachine --force
 
# Check cron
wp cron event list

Common Patterns

Self-Improving Content Pipeline

1. Fetch topics (RSS, manual queue, or AI ideation)
2. AI generates content with local_search to avoid duplicates
3. Publish to WordPress
4. Agent Ping to notify agent for image addition / promotion

Autonomous Maintenance

Daily Flow:
  → Agent Ping: "Check for failed jobs, investigate issues"
 
Weekly Flow:
  → Agent Ping: "Review analytics, identify optimization opportunities"

Multi-Phase Project Execution

Queue tasks in sequence:
  "Phase 1: Research and planning"
  "Phase 2: Implementation"
  "Phase 3: Testing"
  "Phase 4: Documentation"
 
Flow runs daily, pops next phase, agent executes and queues follow-up if needed.

Code Locations

For contributors working on Data Machine itself:

• Steps: inc/Core/Steps/
• Abilities: inc/Abilities/
• CLI: inc/Cli/
• Taxonomy Handler: inc/Core/WordPress/TaxonomyHandler.php
• Queueable Trait: inc/Core/Steps/QueueableTrait.php
• React UI: inc/Core/Admin/Pages/Pipelines/assets/react/

Development Status & Contributing

Data Machine is in active development. It works well (Sarai uses it daily), but it's not yet available on WordPress.org — no auto-updates.

Installation Source

Data Machine is installed from GitHub:

https://github.com/Extra-Chill/data-machine

Updating

To get the latest version:

cd /path/to/wp-content/plugins/data-machine
git pull origin main

Check for breaking changes in the CHANGELOG before updating production sites.

Reporting Issues

If you encounter bugs or unexpected behavior:

1. Check existing issues: https://github.com/Extra-Chill/data-machine/issues
2. Open a new issue with:

• What you were trying to do
• What happened instead
• Steps to reproduce
• Relevant logs (wp-content/uploads/datamachine-logs/)

Agents filing clear, detailed issues are incredibly helpful for development.

Contributing

PRs are welcome! If you've fixed a bug or added a feature:

1. Fork the repository
2. Create a feature branch
3. Make your changes (follow existing code patterns)
4. Submit a PR with clear description

For agents: If you hit a limitation or bug, consider fixing it and submitting a PR rather than working around it. Permanent fixes beat workarounds.

Expectations

• Features may change between versions
• Some edges cases may not be handled yet
• The maintainer (Extra Chill) is responsive to issues
• This is production-ready but evolving

This skill teaches AI agents how to use Data Machine for autonomous operation. For contributing to Data Machine development, see AGENTS.md in the repository root.