# exploring-llm-clusters

A skill for investigating AI observability clusters in PostHog: find clustering runs, compare groups, compute cost and latency metrics.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 407 installs · ★ 75
- Author: PostHog
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/PostHog/skills/tree/HEAD/skills/omnibus/exploring-llm-clusters/SKILL.md
- Updated: 2026-10-24
- HTML: https://affpapa.org/en/nevibecode/skills/exploring-llm-clusters

## What it does

The skill works with clusters PostHog builds by embedding similarity over LLM traces, individual generations, or evaluation events. A Temporal workflow runs on a schedule or on demand and writes results as `$ai_trace_clusters`, `$ai_generation_clusters`, or `$ai_evaluation_clusters`. Five tools sit inside: list and get clustering jobs, `posthog:execute-sql` for metrics, find traces in a cluster, and inspect one trace.

The flow is stepwise. First a query for recent runs, then a pick by `$ai_clustering_run_id`, then parsing the `$ai_clusters` JSON array with titles, sizes and coordinates. After that SQL totals cost, latency, tokens and errors per trace in the cluster. A cluster with `cluster_id: -1` is noise — items that fit no group.

The analyzed window closes when a run starts, but the cluster event lands later, anywhere from seconds to hours. So bounding that event by `$ai_window_start` and `$ai_window_end` returns zero rows; those bounds only scope the traces and generations that were analyzed. Filter the event by `$ai_clustering_run_id` with a plain recent-time bound instead. Large JSON with thousands of IDs may not fit inline and auto-persists to a file — `print_clusters.py` turns it into a readable summary.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add PostHog/skills --skill exploring-llm-clusters -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/exploring-llm-clusters/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/exploring-llm-clusters/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add PostHog/skills --skill exploring-llm-clusters -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/exploring-llm-clusters/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add PostHog/skills --skill exploring-llm-clusters -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/exploring-llm-clusters/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install PostHog/skills exploring-llm-clusters
```

2. **Manually: put the files in place**
- File: `.github/skills/exploring-llm-clusters/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add PostHog/skills --skill exploring-llm-clusters -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/PostHog/skills --path skills/omnibus/exploring-llm-clusters
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/exploring-llm-clusters/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add PostHog/skills --skill exploring-llm-clusters -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/PostHog/skills/tree/HEAD/skills/omnibus/exploring-llm-clusters/SKILL.md

## When to use it
- list recent clustering runs for LLM traffic
- compare cluster behavior across groups
- compute cost and latency for traces in one cluster
- separate noise items from stable clusters
- drill into a single trace inside a cluster

## Example prompts
- show the clustering runs from the last two weeks
- pull clusters from run `<run_id>` and sort them by size
- compute cost and latency for the traces in this cluster

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: f18d8bdeff76b481.

BEGIN THIRD-PARTY FILE f18d8bdeff76b481
````text
---
name: exploring-llm-clusters
description: 'Investigate AI observability clusters — understand usage patterns in AI/LLM traffic, compare cluster behavior, compute cost/latency metrics, and drill into individual traces within clusters.'
---

# Exploring LLM clusters

Use this skill when investigating AI observability clusters —
understanding what patterns exist in your AI/LLM traffic,
comparing cluster behavior, and drilling into individual clusters.

## Tools

| Tool                               | Purpose                                         |
| ---------------------------------- | ----------------------------------------------- |
| `posthog:llma-clustering-job-list` | List clustering job configurations for the team |
| `posthog:llma-clustering-job-get`  | Get a specific clustering job by ID             |
| `posthog:execute-sql`              | Query cluster run events and compute metrics    |
| `posthog:query-llm-traces-list`    | Find traces belonging to a cluster              |
| `posthog:query-llm-trace`          | Inspect a specific trace in detail              |

## How clustering works

PostHog clusters LLM traces, individual generations, or evaluation events by embedding similarity.
A Temporal workflow runs periodically or on-demand, producing cluster events stored as
`$ai_trace_clusters` (trace-level), `$ai_generation_clusters` (generation-level), or
`$ai_evaluation_clusters` (evaluation-level).

Each cluster event contains:

- `$ai_clustering_run_id` — unique run identifier (format: `<team_id>_<level>_<YYYYMMDD>_<HHMMSS>[_<job_id>]`)
- `$ai_clustering_level` — `"trace"`, `"generation"`, or `"evaluation"`
- `$ai_window_start` / `$ai_window_end` — time window of the data that was analyzed
- `$ai_total_items_analyzed` — number of traces, generations, or evaluations processed
- `$ai_clusters` — JSON array of cluster objects
- `$ai_clustering_params` — algorithm parameters used

The analyzed window closes when a run starts, and the cluster event lands once the run finishes.
So the cluster event's own `timestamp` is always **after** `$ai_window_end`, by anything from seconds to hours.
Use the window only to bound the traces, generations, and evaluations that were analyzed.
To find the cluster event itself, filter on `$ai_clustering_run_id` with a plain recent-time bound.

### Cluster object shape (inside `$ai_clusters`)

```json
{
  "cluster_id": 0,
  "size": 42,
  "title": "User authentication flows",
````
END THIRD-PARTY FILE f18d8bdeff76b481

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