Thumbnail and Graphics Generation with OpenClaw

📖 Multi-agent content pipeline: Use OpenClaw to run a researcher, writer, and editor agent pipeline for automated blog posts, outlines, and publishing. Trigger via Telegram, Discord, or schedule-all self-hosted.

Overview

A content factory is a multi-agent workflow where specialized OpenClaw agents handle research, drafting, and editing in sequence. You give a topic or brief (e.g. via Telegram or a cron-triggered prompt); the researcher gathers and summarizes sources; the writer produces a first draft; the editor polishes tone and structure. Output can be delivered back to you as messages, saved to files, or passed to publishing tools.

What you'll learn:

  • Why use a multi-agent content pipeline with OpenClaw
  • How to design researcher → writer → editor agents and handoffs
  • Step-by-step setup: agents, prompts, skills (browser, files), and channels
  • Best practices for quality, consistency, and safety
  • Common issues and where to extend (SEO, social, thumbnails)

Why OpenClaw for a Content Factory?

  • Multi-agent orchestration: Run separate agents for research, writing, and editing so each has a clear role and context. See Multi-Agent Orchestration for patterns.
  • Task execution: Agents can use ClawHub skills (browser, web search, file read/write) for real research and saving drafts-not just chat.
  • Persistent memory: Use OpenClaw’s memory system so the pipeline remembers style guides, brand voice, and past topics.
  • Channels as interface: Trigger runs or receive drafts via Telegram, Discord, Slack, or other channels you already use.
  • Self-hosted and private: Content and sources stay on your infrastructure; see security best practices.

Prerequisites

Implementation Guide

Step 1: Define the pipeline (researcher → writer → editor)

Design three logical roles and how they hand off:

  • Researcher: Input = topic or brief. Output = structured notes, key facts, links, and a short summary. Use skills for web search or browser if available.
  • Writer: Input = researcher output + style/audience instructions. Output = first draft (blog post, outline, or section).
  • Editor: Input = draft + editing rules (tone, length, SEO). Output = polished text and optional suggestions.

You can implement this as one agent with a long system prompt that defines phases, or as separate agents that you invoke in sequence (e.g. by forwarding outputs in prompts or via a coordinating agent). For more patterns, see Multi-Agent Orchestration.

Step 2: Create agent(s) and system prompts

In your OpenClaw config, define agent(s) with clear instructions. Example for a single “content factory” agent that does all three steps internally:

# Example: single agent with phased instructions
# In your agent system prompt, specify:
# 1. RESEARCH: Given a topic, search (or use provided sources), then output a "Research summary" with bullet points and links.
# 2. WRITE: Using the research summary, write a first draft. Audience: [your audience]. Style: [your style].
# 3. EDIT: Review the draft for clarity, tone, and length; output the final version and a short changelog.

Alternatively, define three agents (e.g. content-researcher, content-writer, content-editor) and call them in order from your channel or from a simple orchestrator. Use agent customization and memory so the writer and editor remember your style guide.

Step 3: Add skills for research and file output

Install and configure skills that give agents real capabilities:

  • Web search / browser: So the researcher can look up current information. Browse ClawHub for search or browser skills; always check skills security before installing.
  • File read/write: So drafts can be saved to a folder (e.g. ~/content-factory/drafts/) or read for editing. Restrict paths and permissions per security best practices.

After adding skills, run openclaw status and test with a simple prompt (e.g. “Research [topic] and give me a one-paragraph summary”) to confirm skills work.

Step 4: Trigger and run the pipeline

Trigger the content factory from a channel or on a schedule:

  • On demand: Send a message in Telegram or Discord, e.g. “Create a post about [topic]” or “Weekly brief: [theme]”. The agent(s) run research → write → edit and reply with the result or a file path.
  • Scheduled: Use cron or a scheduler (e.g. system cron or a skill) to send a prompt to your OpenClaw channel (e.g. “Generate this week’s blog outline for [keywords]”). OpenClaw’s gateway keeps the agent available 24/7.

Verify with openclaw logs --follow during a test run. Refine prompts and handoffs based on output quality.

Step 5: Optional-publish or hand off

Today, handoff is often manual: you receive the final text in chat or from a file and paste it into your CMS or social tool. For automation, you can add skills or scripts that post to your blog API or trigger social media automation. Keep API keys and credentials in environment variables; see credential management.

Best Practices

  • Start with one agent and phases: Get research → write → edit working in a single agent before splitting into multiple agents.
  • Lock down style in memory: Store “brand voice” and “audience” in OpenClaw memory so the writer and editor stay consistent.
  • Limit tool scope: Give file and browser skills only the paths and permissions they need; see security best practices and sandbox options.
  • Review before publishing: Use the factory for drafts and outlines; keep a human in the loop before going live.
  • Monitor and tune: Watch openclaw logs and iterate on prompts and handoffs. Join the Discord community for tips.

Common Issues & Solutions

Issue Cause Solution
Research is shallow or outdated No web/search skill or weak prompt Add a search/browser skill from ClawHub (after security audit); tighten researcher prompt with “cite sources” and “summarize key facts”.
Writer ignores style or audience Style not in prompt or memory Put style guide and audience in system prompt or memory; reference them explicitly in the writer step.
Editor changes too much or too little Vague editing instructions Specify concrete rules (e.g. “fix grammar only” or “shorten to 300 words, keep tone”).
Agent can’t save or read files Skill not installed or path not allowed Check skill config and sandbox/path settings; ensure directory exists and is writable.
Pipeline stops or times out Long output or model limits Break into shorter steps; check model/API limits and timeouts.

Need more help? See the full troubleshooting guide.

Advanced Tips

  • Combine with other content use cases: Feed your factory with topics from research automation or SEO monitoring; send finished posts to social media automation or track performance with YouTube analytics.
  • Multi-agent team: Use a dedicated orchestrator agent that calls researcher, writer, and editor in sequence and passes context; see Multi-Agent Orchestration.
  • Thumbnails and graphics: Add a step or skill for image generation (e.g. via an external API) for thumbnails and social cards.
  • Custom skills: Build a skill that posts to your CMS or notifies your team when a draft is ready; see creating custom skills.

For deeper configuration, see the advanced configuration guide.

Related Resources

📚 Setup & Config

🎥 Learn More

💬 Community

Next Steps

After setting up your content factory, consider: