Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Sarai-Chinwag/wp-openclaw --skill data-machinegit clone --depth 1 https://github.com/Sarai-Chinwag/wp-openclawWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/sarai-chinwag/wp-openclaw/data-machine)<a href="https://agentmods.dev/skills/sarai-chinwag/wp-openclaw/data-machine"><img src="https://agentmods.dev/badge/skills/sarai-chinwag/wp-openclaw/data-machine/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/sarai-chinwag/wp-openclaw/data-machine"><img src="https://agentmods.dev/badge/skills/sarai-chinwag/wp-openclaw/data-machine.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00041 | $0.02412 |
| Opus 5.5 | $0.00016 | $0.00965 |
| Sonnet 5.5 | $0.00008 | $0.00482 |
| Haiku 4.5 | $0.00004 | $0.00241 |
Grade A, and why
data-machine scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 400 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
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) |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2mo ago First seen · 400 lines · 41 tokens per session scan A f3de67e68795
data-machine is a skill published in the GitHub repository Sarai-Chinwag/wp-openclaw (63 stars, last pushed 8mo ago), licensed MIT. It adds 41 tokens to every session and 2,412 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
data-machine compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| data-machineSarai-Chinwag/wp-openclaw | Skill | 63 | 8mo ago | MIT | A | 41 | Claude Code, Codex |
| imagegenopenai/codex | Skill | 134k | today | Apache-2.0 | A | 113 | Codex |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 2d ago | MIT | A | 200 | Claude Code, Codex |
| playwright-climicrosoft/playwright | Skill | 100k | today | Apache-2.0 | A | 19 | Claude Code |
| browser-automationopenclaw/openclaw | Skill | 394k | today | MIT | A | 36 | Claude Code |
| systematic-debuggingobra/superpowers | Skill | 315k | 5d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 95 | Claude Code |