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

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

Mental Model

Role	How It Works
Reminder System	Flows run on schedules (daily, hourly, cron) and ping the agent
Task Manager	Queues hold task backlog; each run pops the next task
Workflow Executor	Pipeline steps execute work (AI generation, publishing, API calls)

Architecture Overview

Execution Model

code

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

Type	Purpose	Has Queue
`fetch`	Import data (RSS, Sheets, Files, Reddit)	No
`ai`	Process with AI (multi-turn, tools)	Yes
`publish`	Output (WordPress, Twitter, Discord)	No
`update`	Modify existing content	No
`agent_ping`	Webhook to external agents	Yes

Scheduling Options

Configure via scheduling_config in the flow:

Interval	Behavior
`manual`	Only runs when triggered via UI or CLI
`daily`	Runs once per day
`hourly`	Runs once per hour
`{"cron": "0 9 * * 1"}`	Cron expression (e.g., Mondays at 9am)

Working with Flows

List Flows

bash

wp datamachine flows list

Get Flow Details

bash

wp datamachine flows get <flow_id>

Run a Flow Manually

bash

wp datamachine flows run <flow_id>

Check Job Status

bash

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

bash

# 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:

•Execute the immediate task
•Queue the next logical task (if continuation needed)
•Let the cycle continue

code

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:

code

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:

Selection	Behavior
`skip`	Don't assign this taxonomy
`ai_decides`	AI provides values via tool parameters
`<term_id\|name\|slug>`	Pre-select specific term

AI Decides Mode

When ai_decides is set:

•TaxonomyHandler generates a tool parameter for that taxonomy
•AI provides term names in its tool call
•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:

code

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:

bash

# 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.

bash

# 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

bash

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

Failed Jobs

bash

wp datamachine jobs list --status=failed

Scheduled Actions

bash

# List pending actions
wp action-scheduler run --hooks=datamachine --force

# Check cron
wp cron event list

Common Patterns

Self-Improving Content Pipeline

code

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

code

Daily Flow:
  → Agent Ping: "Check for failed jobs, investigate issues"

Weekly Flow:
  → Agent Ping: "Review analytics, identify optimization opportunities"

Multi-Phase Project Execution

code

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 and is used in production, but it's not yet available on WordPress.org — no auto-updates.

Installation Source

Data Machine is installed from GitHub:

code

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

Updating

To get the latest version:

bash

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:

•Check existing issues: https://github.com/Extra-Chill/data-machine/issues
•
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:

•Fork the repository
•Create a feature branch
•Make your changes (follow existing code patterns)
•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 edge 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.