Skip to content
Agentmart
Submit a tool
This entry duplicates auditing-endpoints, which is the one we list in search.

auditing-endpoints

Audit every endpoint in a PostHog project for staleness, failed materialisations, and unused materialised versions.

Skill by PostHogOfficial

IndexedMIT★ 75
Write a review

Chalk Labs

Need help deploying auditing-endpoints?

Chalk Labs sets up agents like this inside real teams: connected to your tools, tested on your data, with a human in the loop where it matters.

Book a call

Install

Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.

npx agentmarket install skill/posthog-auditing-endpoints-fcc8d2

Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.

Get the weekly diff

The weekly diff: one email a week with new agent tools, notable updates and grade changes, plus security alerts for tools you follow. Double opt-in, one-click unsubscribe.

We use your email only for this newsletter. Privacy

About

Audit every endpoint in a PostHog project for staleness, failed materialisations, and unused materialised versions. Use when the user asks "what endpoints can I clean up?", "are any of my endpoints broken?", "which materialised versions are still being called?", or wants a one-shot cleanup pass over the Endpoints product. Produces a prioritised report grouped by issue type, with recommended actions but does not modify anything without explicit confirmation.

Use cases: documents, security

No reviews yet. Installed it? Reviews come from verified installers only. How reviews work

Permissions: not checked yet.

What's inside

Skill name
auditing-endpoints
Description
Audit every endpoint in a PostHog project for staleness, failed materialisations, and unused materialised versions. Use when the user asks "what endpoints can I clean up?", "are any of my endpoints broken?", "which materialised versions are still being called?", or wants a one-shot cleanup pass over the Endpoints product. Produces a prioritised report grouped by issue type, with recommended actions but does not modify anything without explicit confirmation.
License
MIT
Instructions
202 lines in SKILL.md · view on GitHub

Files (1)

  • SKILL.md

SKILL.md (first lines)

# Auditing endpoints

This skill produces a project-wide audit of the Endpoints product. Use it when the user wants to
**find what to clean up** — unused endpoints, failing materialisations, materialised versions that
nobody calls any more. It does not modify anything; it reports.

The deeper investigation per endpoint is `diagnosing-endpoint-performance`. The audit's job is to
find candidates and hand off.

## When to use this skill

- "Audit my endpoints" / "What endpoints can I clean up?"
- The user is taking over a project and wants to know what they've inherited
- A periodic review (monthly / quarterly) of endpoint sprawl
- The user is over a materialisation cost budget and wants to know what to disable

The dedicated tools give a fast endpoint-level view. For call frequency, recency, and cost over
time, query the `query_log` table with `execute-sql` (endpoint-level). Per-version recency comes
from `endpoint-versions` — each version carries its own `last_executed_at`.

## Available tools

| Tool                              | What it's for                                                                                                                                                                                                                          |
| --------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `execute-sql` (HogQL)             | **Primary read path.** Query `system.data_modeling_endpoints` for metadata (name, is_active, current_version, derived_from_insight, last_executed_at) and `query_log` for endpoint-level usage (call counts, recency, duration, bytes) |
| `endpoint-materialization-status` | Per endpoint: is materialisation eligible, current status, last run, last error (not in the system tables — use this tool)                                                                                                             |
| `endpoint-versions`               | All versions for one endpoint, latest first, with each version's query, materialisation state, and `last_executed_at`                                                                                                                  |
| `endpoint-update`                 | Write path — disable (`is_active: false`) or unmaterialise (`is_materialized: false`) after the user confirms                                                                                                                          |
| `agent-feedback`                  | Tell the PostHog team what's missing or confusing in this flow so the product and skill improve                                                                                                                                        |

Prefer reading from the system tables over the `endpoints-get-all` / `endpoint-get` tools — one
SQL query returns the whole inventory and lets you join metadata to usage in `query_log`.

## What counts as an issue

| Category                        | Trigger                                                                                          | Typical action                                     |
| ------------------------------- | ------------------------------------------------------------------------------------------------ | -------------------------------------------------- |
| **Never called**                | No rows in `query_log` for the endpoint (personal-API-key calls only)                            | Confirm with the user, then disable                |
| **Stale**                       | `query_log` shows the last call more than 30 days ago                                            | Confirm with the user; often safe to disable       |
| **Inactive**                    | `is_active = 0` in `system.data_modeling_endpoints`                                              | Verify intent; if abandoned, delete                |
| **Failing materialisation**     | `endpoint-materialization-status` returns `Failed` with an error                                 | Hand off to `diagnosing-endpoint-performance`      |
| **Unused materialised version** | A materialised version whose `last_executed_at` (from `endpoint-versions`) is null or long stale | Unmaterialise that version, or roll to a newer one |
| **Drifted versions**            | Many versions exist (query changed repeatedly)                                                   | History noise — not an issue, but worth noting     |

Usage counts only **personal-API-key calls** — an endpoint exercised solely from the Playground
tab or the app will look unused. Per-version `last_executed_at` is recorded only for runs since
that tracking was added, so a version can read null while still being used; always confirm before
removing.

## Workflow

### 1. List endpoints and their metadata

One `execute-sql` query gets the whole inventory from `system.data_modeling_endpoints`:

```sql
SELECT name, is_active, current_version, derived_from_insight, last_executed_at
FROM system.data_modeling_endpoints
ORDER BY name
```

No rows → the project has no endpoints; say so and stop. Don't invent issues. (The
`last_executed_at` column here is a convenience endpoint-level timestamp; for call frequency and
cost, use `query_log` in the next step.)

### 2. Pull usage from `query_log`

`query_log` records every personal-API-key call, tagged with the endpoint name. One query gives
recency and call counts across all endpoints:

```sql
SELECT name, count() AS calls, max(query_start_time) AS last_called
FROM query_log
WHERE endpoint LIKE '%/endpoints/%' AND is_personal_api_key_request
GROUP BY name
ORDER BY name
```

Cross-reference with step 1:

- **In metadata, absent from `query_log`** → never called via API key
- **Last call more than 30 days ago** → stale

`query_log` also exposes `query_duration_ms`, `read_rows`, and `read_bytes` per call — useful to
flag expensive endpoints in the same pass. This is endpoint-level; per-version recency comes from
`endpoint-versions` (step 3).

### 3. Check materialisation health and unused versions

For each materialised endpoint, call `endpoint-materialization-status` (this isn't in the system
tables). Surface any with `status: "Failed"` separately — these are active failures, not staleness.

Then call `endpoint-versions` and read each version's `last_executed_at`: a **materialised**
version that's null or long stale is an unused-materialised-version candidate. Treat this as a
lead, not proof — per-version recency only counts API-key runs since tracking was added, so confirm
with the user before unmaterialising.

### 4. Present the audit

Render a prioritised report grouped by category. Don't dump raw JSON; use a readable table per
section:

```text
## Endpoints audit — 9 issues

### 🔴 Failing materialisations (1)
- weekly_revenue (v3) — Failed 2h ago, "Column 'event_date' does not exist"
  → hand off to diagnosing-endpoint-performance

### 🟠 Never called via API key (3)
- internal_admin_query — created 5 months ago
- legacy_signup_funnel — created 1 year ago, materialised
- experiment_arm_lookup — created 9 months ago

### 🟠 Unused materialised versions (2)  [from endpoint-versions]
- monthly_active_users — v3 materialised, last_executed_at null (currently on v4 — unmaterialise v3)
- order_summary — v1 materialised, last_executed_at null

### 🟡 Stale (3)

From PostHog/skills, MIT.

Signals

Stars
75
Forks
8
Open issues
2
Last commit (this folder)
Jun 14, 2026 (4 months ago)

From GitHub (fetched Oct 24, 2026).

Details

License
MIT
First seen
Oct 24, 2026
Last checked
Oct 24, 2026

Same skill elsewhere

Copies of this SKILL.md published by others; search shows only one of them.

More from PostHog

Done for you

Rather not set it up yourself?

Chalk Labs scopes, builds and runs agents for your team: the task audit, the eval harness, the integrations and the weekly ops report. Fixed scope, priced before we start.

See how we build AI agents →Read the case studies

Chalk Labs is a two-founder AI and growth studio. We build AI agents, CRMs and MVPs for Web3 and AI companies, and have shipped 50+ product and token launches.

launches shipped
50+
launches shipped
media placements
500+
media placements
founders on every account
2
founders on every account

Press we've placed: Forbes · CoinDesk · Decrypt · Cointelegraph · The Block

Rahil Jain, Co-founder, Chalk Labs. Ex-Goldman Sachs engineer. You talk to the person who builds it.

Get auditing-endpoints running in your business

Tell us what you want to automate. A founder replies within one working day with a plan, not a pitch deck.

Goes to the Chalk Labs founders, not the newsletter. Privacy

Need help deploying auditing-endpoints?

CallGet help
Cite this page

auditing-endpoints skill. Agent Mart. https://agentmart.dev/skills/posthog-auditing-endpoints-fcc8d2 (last updated 2026-10-24)