auditing-warehouse-view-health

Saved queries that are kept pre-computed can quietly stop running, or go on costing storage and compute while nobody reads them. This finds both across a whole project and says which to look at first.

WHEN TO USE

SCOPE

USE FOR

Find out why a specific materialized view keeps failing to run.
Check whether a materialized view is wasting storage and compute because nobody queries it.
Review view health right after making a HogQL or schema change.
Get a one-shot triage of every broken materialized view in a project.

NOT FOR

Source or sync health issues — that's auditing-warehouse-source-health instead.
Batch-export destinations or transformations — those are owned by a different product.

This is a saved copy of the skill's SKILL.md instructions. It may differ from the upstream file or the version you install.

In this document (6)

Auditing data warehouse view health

This skill produces a project-wide audit of materialized views (materialized saved queries) in the data warehouse — which ones are failing, and which are materialized but unused. Use it when the user wants a summary of view health, not a deep-dive on one failure.

The same underlying endpoint (data-warehouse-data-health-issues-retrieve) also reports source, sync, batch-export, and transformation issues. Source and sync health is covered by auditing-warehouse-source-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

"Which of my views are broken?" / "Why is this materialized view failing?"
"Are any of my materialized views wasting compute?"
Reviewing view health after a HogQL or schema change
Dashboards backed by materialized views are stale or erroring

Available tools

Tool	Purpose
`data-warehouse-data-health-issues-retrieve`	One-shot: all failed/degraded items across the whole pipeline
`view-list`	All saved queries / materialized views with status and latest_error
`view-run-history`	Run history for a specific materialized view

Filter the data-health-issues results to the materialized_view type for this audit. Use view-list when you need more than the active-failure summary (non-failing views, materialization flags, last-queried info) and view-run-history to see the run trail for a specific view.

What counts as a view "issue"

From the data-health endpoint, this audit cares about one of the five categories:

`type`	Trigger	Typical urgency
`materialized_view`	`DataWarehouseSavedQuery.is_materialized=true, status=Failed`	Medium

Each entry includes id, name, type, status, error, failed_at, and url.

The other categories the endpoint returns are out of scope for this skill:

source / external_data_sync → auditing-warehouse-source-health
destination (batch export) → owned by the batch exports / data pipelines product
transformation (HogFunction) → owned by the CDP / ingestion side

Note the data-health endpoint only reports active failures. For views it doesn't flag:

Non-materialized views with errors (only materialized views are reported)
Materialized views that are healthy but unused (costing compute every run) — see Step 4

Workflow

Step 1 — One-shot pull

Call data-warehouse-data-health-issues-retrieve and keep the materialized_view entries.

If there are no view issues, tell the user their materialized views are healthy and stop. Don't invent problems.

Step 2 — Triage failures

Materialized view failures are usually independent of sources — a view failure is a HogQL or data issue in the view itself (syntax error, missing table reference, type mismatch). For each failing view, surface the error and point at the offending query. Use view-run-history if the user wants the failure trail.

Step 3 — Present the audit

Render a prioritized report. Don't dump the raw JSON — human-readable:

## Materialized view health — 2 issues

### 🟠 Materialized views (2)
- monthly_revenue — view failed (syntax error in HogQL: 'FORM' instead of 'FROM')
- active_users_30d — view failed (missing table reference)

Both are HogQL issues in the view definitions — independent of your sources. Want me to open one?

Step 4 — Go beyond active failures (when asked)

Unused materialized views: Call view-list. Materialized views cost storage and compute every run. If any are marked materialized but haven't been queried lately, surface them as cleanup candidates (the data is available via view-list; unmaterialize via view-unmaterialize).

Only run this extra check if the user explicitly asks for a broader audit.

Step 5 — Offer the next step

End the audit with a clear hand-off — e.g. "Want me to open monthly_revenue and fix the HogQL?" Never apply fixes autonomously from an audit; confirm explicitly before editing or unmaterializing a view.

Important notes

The audit is read-only. Never call destructive tools (e.g. view-unmaterialize, view-delete) from the audit flow without explicit confirmation.
Empty = healthy. Don't pad an empty audit with hypothetical issues. "No view issues found" is a good answer.
View failures are usually self-contained. Unlike source failures, a failed materialized view rarely cascades — it's a query problem in that view. Don't imply a broader outage.
Sources, syncs, destinations, and transformations are out of scope here. They share the data-health endpoint but belong to other audits/products — route, don't diagnose.

MCP connection required

1 FOR YOU

01
Connect the PostHog MCP
Connect the PostHog MCP server to a project with materialized views set up so the skill can call data-warehouse-data-health-issues-retrieve, view-list, and view-run-history.

This skill ships inside the PostHog AI Plugin. Listed package contents include 3 slash commands, 1 agent, hooks and an MCP server. Installed components and skill versions may vary by AI tool.

Install just this skill

Run this outside your AI tool, using Node.js 22.20.0 or newer. Skills CLI 1.7.2 skips skill selection; prompts cancel installation without an interactive terminal. If choosing Project when asked about installation scope, open your project's folder before running this.

Install checklist

Check selected tools if a picker appears; Claude Code may be unchecked under Additional agents. ↑/↓ moves, Space toggles selections, Enter confirms.
Without a tool picker, check Installing to: for your intended tool. Stop if it names a different tool.
If asked, choose Symlink (Recommended) for shared files or Copy to all agents for separate copies; review the summary before confirming installation.
Install the find-skills skill? defaults to Yes, adding another skill for all projects. Choose No if you don't want that extra installation.

Skills CLI 1.7.2 sends install data to skills.sh by default, including the source, skill names, target tools and file details; GitHub sources are reported only when GitHub confirms the repository is public, while other remote sources can also be reported. Confirmed-public GitHub sources also trigger a security-audit request. Before installing, set the environment variable DO_NOT_TRACK=1 or DISABLE_TELEMETRY=1 to disable both.

npx skills add posthog/ai-plugin --skill "auditing-warehouse-view-health" --full-depth

After installing for Claude Code, open it in the installation project if you chose Project; Global makes the skill available across projects on this computer. Once Claude Code loads the skill, its description helps Claude decide when to use it.

If installation created a top-level skills folder that did not exist when your Claude Code session started, run /reload-skills in that session. Run it again after later changes in that folder.

MCP server required

The skill's steps call for tools from the PostHog MCP server. Installing just this skill doesn't add that connection; set it up in your AI tool if it isn't there yet: PostHog MCP setup. Installing the whole plugin instead includes the connection settings.

Requirements for the PostHog MCP server

Connecting is free; sign in with your PostHog account.
Some tools use AI internally and may incur PostHog AI charges. Those tools require AI data processing to be enabled in your organization settings.

Install the whole plugin

This route installs the posthog plugin package selected for your AI tool.

Claude Code

Add PostHog's marketplace, then install the plugin

Run both lines in a terminal. They install PostHog's posthog plugin: this skill, PostHog's other skills, and a connection to PostHog's hosted MCP server.

claude plugin marketplace add PostHog/ai-plugin

02
```
claude plugin install posthog@posthog
```

Installation and loading

The plugin installs for you in every project and loads the next time you start Claude Code, or after /reload-plugins in an open session.
To connect your PostHog account, start claude, type /mcp, select plugin:posthog:posthog and follow the browser prompts.

Good to know

The plugin includes PostHog's session-capture hook; PostHog's README says it sends sessions to LLM Analytics only when POSTHOG_LLMA_CC_ENABLED=true and POSTHOG_API_KEY are both set.
If you use the separate posthog-telemetry plugin, PostHog says to keep only one of the two, because both register the same hook.
On a self-hosted PostHog, this route's MCP connection won't reach your instance: the plugin's config is fixed to https://mcp.posthog.com/mcp, which routes to PostHog's US or EU region. POSTHOG_MCP_URL works only for the Gemini CLI extension.

View on GitHub

Same repository

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auditing-endpoints
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Auditing data warehouse view health

When to use this skill

Available tools

What counts as a view "issue"

Workflow

Step 1 — One-shot pull

Step 2 — Triage failures

Step 3 — Present the audit

Step 4 — Go beyond active failures (when asked)

Step 5 — Offer the next step

Important notes

MCP connection required

Install just this skill

Install the whole plugin

Add PostHog's marketplace, then install the plugin

More skills

adding-warehouse-person-properties

analyzing-expensive-users

analyzing-experiment-session-replays

assessing-heatmaps

auditing-endpoints

auditing-experiments-flags