
Auditing Warehouse Source Health
FreeQuickly assess the health of your PostHog data sources.
Free · Opens the source repo
What Auditing Warehouse Source Health does
The Auditing Warehouse Source Health skill is designed for users of PostHog who need to quickly assess the health of their data warehouse sources and syncs. This skill provides a comprehensive audit of the source connections, sync schemas, and webhook push channels in your data pipeline. It is particularly useful when users encounter issues such as import failures or stale data, allowing them to identify and prioritize problems without diving into individual sync failures. By using this skill, users can obtain a prioritized report that groups issues by severity, helping them determine the most critical areas to address first.
The skill operates by utilizing the data-warehouse-data-health-issues-retrieve endpoint, which aggregates data across the entire pipeline. Users can quickly pull a summary of all failed or degraded items, focusing specifically on source connections and sync schemas. If no issues are found, the skill will inform the user that their sources are healthy. If problems are detected, the skill categorizes them into high and medium-high urgency, allowing users to focus on the most pressing issues first.
This skill is ideal for data engineers, analysts, and project managers who need to maintain the integrity of their data warehouse. It is particularly beneficial for those who are new to a project and need to understand the health of inherited data sources. Regular audits using this skill can also help teams stay proactive in managing their data pipelines, ensuring that dashboards and reports remain accurate and up-to-date.
In scenarios where users require a deeper dive into specific sync failures or materialized view health, this skill can direct them to the appropriate resources, such as diagnosing-failed-warehouse-syncs or auditing-warehouse-view-health. Overall, the Auditing Warehouse Source Health skill streamlines the process of identifying data source issues, making it an essential tool for maintaining a healthy data pipeline.
When to use it
Use this skill when you need a quick overview of the health of your data sources, especially during import failures or when data appears stale.
When not to use it
This skill is not suitable for deep dives into individual sync failures; use specific diagnostic tools for those cases.
What you can build with it
Identifying Import Failures
A data analyst uses this skill to quickly determine why their data imports are failing, allowing them to address the most critical issues first.
Regular Health Checks
A project manager schedules regular audits of data sources using this skill to ensure data integrity and prevent stale dashboards.
Onboarding New Team Members
A new team member uses this skill to understand the health of inherited data sources, helping them get up to speed with the project.
How to install Auditing Warehouse Source Health
View source1. Install with the skills CLI
npx skills add posthog/posthog/auditing-warehouse-source-health --agent claude-code2. Or install it manually
Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.
Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs
Inside SKILL.md
Written by posthogAuditing data warehouse source health
This skill produces a project-wide audit of the source and sync side of the data warehouse pipeline — source
connections, sync schemas, and webhook push channels. Use it when the user wants a summary of what's broken with
their imports, not a deep-dive on one sync. The deep-dive on individual failures is
diagnosing-failed-warehouse-syncs; this skill is the scan that tells them where to look first.
The same underlying endpoint (data-warehouse-data-health-issues-retrieve) also reports materialized-view,
batch-export-destination, and transformation issues. Materialized views are covered by
auditing-warehouse-view-health. Destinations (batch exports) and transformations are owned by other products — surface
them if they appear, but route them to the relevant team rather than diagnosing here.
When to use this skill
- "Why are my imports failing?" / "What's broken with my sources?"
- "Why is my warehouse data stale?"
- The user is new to a project and wants to know which sources they've inherited and whether they're healthy
- Weekly or monthly review of source/sync health
- Dashboards are stale and the user isn't sure which source is at fault
Available tools
| Tool | Purpose |
|---|---|
data-warehouse-data-health-issues-retrieve | One-shot: all failed/degraded items across the whole pipeline |
external-data-sources-list | All sources with status and latest error |
external-data-schemas-list | All schemas with status, last_synced_at, latest_error |
external-data-sources-webhook-info-retrieve | Check per-source webhook state (not covered by data-health-issues) |
The data-health-issues endpoint aggregates across the whole pipeline — it's the fastest path to a summary. Filter
its results to the source and external_data_sync types for this audit. Use the list endpoints when you need more
context than the summary provides (row counts, non-failing items, schema-level detail).
What counts as a source/sync "issue"
From the data-health endpoint, this audit cares about two of the five categories:
type | Trigger | Typical urgency |
|---|---|---|
source | ExternalDataSource.status = Error — whole source connection broken | High |
external_data_sync | schema in Failed or BillingLimitReached state (the data-health endpoint returns status: "failed" or status: "billing_limit" respectively) | Medium–High |
Each entry includes id, name, type, status, error, failed_at, url, and source_type.
The other categories the endpoint returns are out of scope for this skill:
materialized_view→auditing-warehouse-view-healthdestination(batch export) → owned by the batch exports / data pipelines producttransformation(HogFunction) → owned by the CDP / ingestion side
Note the data-health endpoint only reports active failures. For source/sync health it doesn't flag:
- Schemas paused by the user (
should_sync = false) - Schemas that are slow or stale but technically
Completed - Webhook problems on
sync_type: "webhook"schemas. The bulk-sync safety net can succeed while the webhook push channel is silently broken (deregistered, disabled on the remote side, failing signature verification). These don't surface indata-health-issues— check per-source withwebhook-info-retrieve.
If the user asks about staleness or unused items, reach beyond this endpoint — see Step 4.
Workflow
Step 1 — One-shot pull
Call data-warehouse-data-health-issues-retrieve and keep the source and external_data_sync entries.
If there are no source/sync issues, tell the user their sources are healthy and stop. Don't invent problems.
Step 2 — Group and prioritize
- Sources in Error first. A source failure cascades — every schema under it is effectively dead until the source reconnects. Fix these first.
- Sync schemas next, in this order:
status: "billing_limit"entries (billing issue, non-technical — flag and route to billing)Failedon heavily-used tables (user asks / check row counts via schemas-list if needed)Failedon less-used tables
Step 3 — Present the audit
Render a prioritized report. Don't dump the raw JSON — human-readable table per category:
## Data warehouse source health — 4 issues
### 🔴 Sources (1)
- Stripe — authentication failed (failed 2h ago). All 8 tables under it are currently dead.
→ `diagnosing-failed-warehouse-syncs` on this source
### 🟠 Sync schemas (3)
- postgres_prod.orders (Failed 6h ago) — column "updated_at" does not exist
- postgres_prod.invoices (Failed 6h ago) — column "updated_at" does not exist
- hubspot.contacts (BillingLimitReached) — team quota exceeded
Recommended order:
1. Stripe auth (everything under it is dead)
2. Schema-drift on postgres_prod.orders / invoices — looks like upstream renamed a column
3. Billing limit on hubspot
The exact format is less important than: prioritized, grouped, actionable, and hinting at the right next skill.
Step 4 — Go beyond active failures (when asked)
If the user wants more than just "what's on fire" — e.g. "what else should I look at?" — cross-check:
Stale but "Completed" schemas:
Call external-data-schemas-list and look for schemas with old last_synced_at relative to their sync_frequency.
A schema on 1hour frequency that last synced 3 days ago is effectively broken even if status says Completed.
Sources with zero sync activity:
Sources where every schema has should_sync: false or status = Paused. These were set up and then abandoned —
candidates for cleanup via external-data-sources-destroy.
Broken webhooks on webhook-type schemas:
Iterate the sources that have any schema with sync_type: "webhook" (visible via external-data-schemas-list). For
each, call external-data-sources-webhook-info-retrieve({source_id}):
exists: falsewhile a schema issync_type: "webhook"→ webhook was never registered, or was deleted. Push channel is dead; only the bulk fallback is ingesting.external_status.errorpresent → remote service is reporting a problem (permission revoked, endpoint deleted on their dashboard).external_status.statusnot"enabled"→ remote has disabled the endpoint (often after repeated delivery failures).
Report these separately from the primary audit — they're a different shape of problem than failed syncs, and the fix
is a different skill (diagnosing-failed-warehouse-syncs scenario I, or setting-up-a-data-warehouse-source step
5.5).
Only run these extra checks if the user explicitly asks for a broader audit — they involve more tool calls and heuristics.
Step 5 — Offer the next step
End the audit with a clear hand-off:
- "Want me to dig into the Stripe failure?" → hands off to
diagnosing-failed-warehouse-syncs - "Want me to fix the schema drift on orders?" → hands off to
tuning-incremental-sync-config - "Want to disable the billing-capped schemas?" → one-click via
external-data-schemas-partial-update
Never start applying fixes autonomously from an audit — the audit's job is to report and recommend, not remediate. Any fix should be confirmed explicitly before executing.
Important notes
- The audit is read-only. Never call destructive tools from the audit flow. Hand off to the diagnosis/tuning skills — which in turn confirm before acting.
- Empty = healthy. Don't pad an empty audit with hypothetical issues. "No source issues found" is a good answer.
- Source failures cascade. When reporting a source in Error, also mention which schemas under it are affected (or will be, once they try to sync again). The user needs to understand the blast radius.
- Billing limits aren't technical problems. Flag them but route to billing / quota discussion, not to a recovery action.
data-health-issuesonly surfaces active failures. For staleness or abandoned sources you need to cross-check the list endpoints. Only do this when the user explicitly asks for a deeper audit.- Webhook health is separate from schema health. The data-health endpoint doesn't know about webhook state.
When a user's request mentions "real-time", "Stripe webhook", or "why is data hours behind on a webhook
source", go straight to
webhook-info-retrieverather than inferring from schema status. - Materialized views, destinations, and transformations are out of scope here. They share the data-health endpoint but belong to other audits/products — route, don't diagnose.
Frequently asked questions about Auditing Warehouse Source Health
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