MARKETING

Redirect Chain Audit that Files ClickUp Cleanup Tasks

Crawls published content for links buried in multi-hop redirect chains or pointing at soft-404 pages.

CategoryMarketing
Enginesim
Difficultyintermediate
Triggerschedule
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerMonthly schedule starts the audit
  • ActionFirecrawl crawls and captures redirect chainsFirecrawl
  • LogicFlag 2+ hop chains and soft-404 destinations
  • ActionOpenAI names the final canonical URL per linkOpenAI
  • ActionCreate ClickUp cleanup task per pageClickUpClickUp
  • OutputReport redirect-bloat count to SlackSlack

What it does

This workflow targets a subtler problem than plain 404s: links that technically work but route through two or more redirects, or land on thin soft-404 pages. It crawls your content, follows each link's redirect chain, and flags links with excessive hops or suspicious destinations. For each, it opens a ClickUp task recommending the final canonical URL so editors can point straight there and recover the lost link equity.

When to use it

Use it when your links resolve but performance and SEO suffer from redirect bloat after a site migration or domain change. Ideal for content teams cleaning up legacy links that pile on hops.

How it works

  1. 1A monthly schedule starts the audit.
  2. 2Firecrawl crawls the content and captures each link's full redirect chain and final status.
  3. 3A logic step flags links with 2+ redirect hops or soft-404 destinations.
  4. 4OpenAI summarizes each finding and names the final canonical URL to use.
  5. 5A ClickUp task is created per page with the redirect chains and recommended direct URLs.
  6. 6A Slack note reports the count of redirect-bloated links found.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect FirecrawlCrawl, scrape, structured extract.
  2. 2
    Connect OpenAIModels, embeddings, files.
  3. 3
    Connect ClickUpDocs + tasks + chats in one workspace.
  4. 4
    Connect SlackChannels, DMs, threads, mentions.
  5. 5
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  6. 6
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  7. 7
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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