Use this skill to check whether registered data sites and their source datasets are stale, repull data from the configured sources, and rebuild a target. When publishing is requested, it deploys through the approved command and verifies the refreshed value on the live site.
Keeps your data-backed public sites and their source datasets current: freshness check, source repull, rebuild, gated deploy, live verify. USE WHEN update our data sites, refresh the data sites, are our datasets stale, data freshness, repull the data, refresh the dataset, update the data and the site, stale data check, which sites need updating, refresh any registered data property by name. NOT FOR system upgrades (Upgrade), uptime/security monitoring (Bunker/Workflows), US economic indicators (USStats), non-data deploys (use your deploy tooling).
Run score Not tested
See how to installDataRefresh checks registered public sites that use data and compares each dataset’s update status with that site’s configured refresh cadence. When asked to update a target, it retrieves the data again from the registered sources and rebuilds the site. If publishing is part of the request, it deploys using the registered procedure and checks the refreshed value on the live page. It supports both fleet-wide freshness checks and updates to a specified target.
Use it to find which of several data sites need an update, retrieve source data again for a particular site, or rebuild and publish updated data. After publication, it can check whether the new value appears on the live site.
The target, source, build steps, and deploy method must be registered. Publishing happens when the request includes it. An upstream source may continue serving old data, so check whether its recorded update date advances.
This overview was written by AI (OpenAI Codex) from the public SKILL.md. Check the original below for the exact behavior.
AI checked the SKILL.md for operations that need care and for setup requirements. The score comes only from actually running the skill (hands-on test).
Not counted in the score. Rated by AI (OpenAI Codex) as of Oct 28, 2026. Treat it as a guide and read the original before installing.
Review the instructions the agent will receive before you install.
Registry-driven freshness engine for data-backed public sites. Each registered target carries its own fetch scripts, source-of-record dataset updater, build step, sanctioned deploy command, and live verify probe. One command reports staleness across the fleet; one command brings a target current.
| Intent | Trigger | File |
|---|---|---|
| Status | "are our data sites stale", "data freshness", "status" | Intents/Status.md |
| Refresh | "update the data sites", "refresh <target>", "repull and redeploy" | Intents/Refresh.md |
| File | Read when |
|---|---|
Setup.md | first install, registry fields, accounts and secrets, the throwaway-target proof |
~/.claude/LIFEOS/USER/CUSTOMIZATIONS/SKILLS/DataRefresh/Registry.json. $DATAREFRESH_REGISTRY overrides the path. With no registry, or one with no targets, the tool exits 1 and points at Setup.md.~/.claude/LIFEOS/MEMORY/OBSERVABILITY/datarefresh.jsonlUser: "are our data sites stale?"
→ Status intent
→ One table of every target with last pull, age vs cadence and STALE/FRESH, plus a one-line read and an offer to refresh what is stale
User: "refresh example-site"
→ Refresh intent, no --deploy (no publish intent)
→ Data repulled and rebuilt but not deployed; the report shows before → after record dates
User: "update the data sites"
→ Refresh intent with --all --deploy
→ Every target current, deployed through its sanctioned command, live freshness field shown advancing, repos committed
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