AI & RAG
Nightly Coda wiki freshness grader
Every night, embeds each Coda doc page and scores it for staleness against the current source-of-truth, then writes a freshness grade and reason back onto each page.
How it runs
The automated pipeline, trigger to output.
- TriggerNightly schedule fires
- ActionList all pages in target Coda docCoda
- ActionEmbed page bodies with OpenAIOpenAI
- ActionFetch source-of-truth records from PostgresPostgres
- LogicCompute staleness score from drift + age
- OutputWrite freshness grade and reason back to each Coda pageCoda
What it does
Runs a nightly sweep of your Coda workspace, embeds every page, and grades each one on how stale its content has become relative to the authoritative source data. Each page gets a freshness score (0-100), a plain-English reason, and a last-graded timestamp written back into Coda so editors can see at a glance which docs are decaying.
When to use it
Use it when your team's wiki has grown past the point where humans can manually audit freshness, and outdated runbooks or specs are causing real mistakes. Best for orgs that keep a structured source of truth (a Postgres table, product spec, or canonical doc) that wiki pages are supposed to reflect.
How it works
- 1A nightly schedule fires the run.
- 2The flow pulls every page from the target Coda doc.
- 3Each page body is embedded with OpenAI and compared against embeddings of the source-of-truth records.
- 4A grading step computes a staleness score from semantic drift plus age since last edit.
- 5The score, reason, and timestamp are written back to a freshness column on each Coda page.
Set it up
What you configure once, before turning it on.
- 1Connect CodaDocs, packs, automations.
- 2Connect OpenAIModels, embeddings, files.
- 3Connect PostgresAny Postgres URL — query, write, migrate.
- 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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