CONTENT CREATION

CEO-orchestrated localized launch package from a Coda spec

An agent reads a launch spec in Coda, coordinates copy and image generation across locales, stages everything in a GitHub PR.

CategoryContent Creation
Enginepaperclip
Difficultyadvanced
Triggermanual
Steps5
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerManual kickoff hands the agent the launch spec
  • ActionAgent reads spec and plans locales from CodaCodaCoda
  • ActionGenerate localized copy and per-locale hero imagesImage generation
  • ActionStage copy and image references in a launch PRGitHubGitHub
  • OutputFile a Linear launch-readiness task with per-locale checklistLinearLinear

What it does

Runs the full localized launch as an agent-driven workflow. Instead of a fixed pipeline, the CEO agent reads the launch spec, decides which locales are in scope, and orchestrates copy, imagery, staging, and tracking end to end.

When to use it

For a real product launch where scope shifts and the work spans content generation, repo staging, and project tracking. Use this when you want one agent owning the launch rather than wiring deterministic blocks.

How it works

  1. 1A manual kickoff hands the agent the launch spec location in Coda.
  2. 2The agent reads the spec, confirms locales and deadlines, and plans the work.
  3. 3It generates localized copy and adapts an image prompt per locale with OpenAI, rendering each hero image.
  4. 4It stages all copy and image references in a single GitHub launch PR.
  5. 5It files a Linear launch-readiness task with a per-locale checklist and links the PR, so the launch owner tracks sign-off in one place.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect CodaDocs, packs, automations.
  2. 2
    Connect OpenAIModels, embeddings, files.
  3. 3
    Connect Image generationManaged Nano Banana image renders, metered per image.
  4. 4
    Connect GitHubRepos, issues, pull requests, actions.
  5. 5
    Connect LinearIssues, projects, cycles, triage.
  6. 6
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  7. 7
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  8. 8
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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