CONTENT CREATION
Evergreen Post Refresh: Rewrite Stale Stats in Notion Blog Posts
On a schedule, scans your Notion blog database for posts whose statistics are out of date, pulls fresh numbers from live sources, rewrites the affected paragraphs.
How it runs
The automated pipeline, trigger to output.
- TriggerWeekly schedule fires
- ActionQuery Notion for posts not refreshed in 90+ daysNotion
- ActionExtract statistics and cited sources per post (LLM)OpenAI
- ActionScrape source pages for current figuresFirecrawl
- LogicSkip posts with no stat drift
- ActionRewrite only the stale sentences (LLM)OpenAI
- OutputSave updated draft with change-log to NotionNotion
What it does
Keeps an evergreen content library accurate without a manual audit. The workflow walks every published post in a Notion database, finds claims tied to dated statistics ("as of 2023", "X% of users"), verifies each against a live source, and rewrites only the stale sentences — leaving the rest of the post untouched.
When to use it
You maintain a content hub of pillar posts and guides that rank on numbers that drift over time (market sizes, adoption rates, pricing benchmarks). You want a weekly sweep that flags and fixes decay before readers or search engines notice.
How it works
- 1A weekly schedule fires the run.
- 2Query the Notion blog database for posts marked published and last-refreshed over 90 days ago.
- 3For each post, an LLM extracts every factual statistic and its implied source.
- 4Firecrawl scrapes the cited source page (or the canonical authority) for the current figure.
- 5A logic step compares old vs. fresh values; posts with no drift are skipped.
- 6The LLM rewrites only the changed sentences, preserving tone and surrounding copy.
- 7The revised body is written back to Notion as a new draft with a change-log callout.
Set it up
What you configure once, before turning it on.
- 1Connect NotionPages, databases, comments.
- 2Connect FirecrawlCrawl, scrape, structured extract.
- 3Connect OpenAIModels, embeddings, files.
- 4Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 5Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 6Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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