> AI agents: this is one page from PostHog's docs. Full index of Markdown docs for LLMs: https://posthog.com/llms.txt

# Agent skills for AI Observability - Docs

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# Agent skills for AI Observability - Docs

PostHog ships agent skills for AI Observability that teach your coding agent how to investigate LLM traces, costs, clusters, evaluations, and failures through the [PostHog MCP server](/docs/model-context-protocol.md). They're the same skills PostHog's own background agents run.

## Set up

1.  Make sure there's LLM data to investigate – if you haven't instrumented yet, [install a PostHog SDK or provider integration](/docs/ai-observability/installation.md).

2.  [Connect the PostHog MCP server](/docs/ai-observability/surfaces/mcp.md). (The AI plugin in step 3 does this for you.)

3.  Install the [PostHog AI plugin](https://github.com/PostHog/ai-plugin) – it bundles every PostHog skill and keeps them up to date. Its README covers installation in Claude Code, Codex, Cursor, and Gemini CLI.

**Want the skills directly?**

Every PostHog skill also ships as `skills.zip` on the [latest skills release](https://github.com/PostHog/posthog/releases/tag/agent-skills-latest) – ready-to-use `SKILL.md` directories you can drop into your agent's skills folder (like `.claude/skills/` for Claude Code).

Each skill's description tells your agent when to load it, so "why did our LLM costs spike yesterday?" picks up `exploring-llm-costs` without you naming it.

## The skills

### analyzing-expensive-users

[View source](https://github.com/PostHog/posthog/tree/master/products/ai_observability/skills/analyzing-expensive-users)

Analyze the most expensive users in AI observability and explain why they cost so much.

-   execute-sql
-   query-llm-traces-list
-   query-llm-trace
-   read-data-schema
-   generate-app-url

### creating-online-evaluations

[View source](https://github.com/PostHog/posthog/tree/master/products/ai_observability/skills/creating-online-evaluations)

Author continuously-running online evaluations in PostHog AI observability, grounded in real failure modes you've identified.

-   llma-evaluation-config-get
-   llma-provider-key-list
-   llma-evaluation-judge-models
-   llma-evaluation-directory-list
-   llma-evaluation-directory-create
-   llma-evaluation-test-hog
-   llma-evaluation-create
-   llma-evaluation-run
-   llma-evaluation-update
-   execute-sql
-   generate-app-url

### exploring-ai-failures

[View source](https://github.com/PostHog/posthog/tree/master/products/ai_observability/skills/exploring-ai-failures)

Find where an AI/LLM application is failing in production and surface the failure patterns, working from real traces.

-   query-llm-traces-list
-   query-llm-trace
-   execute-sql
-   llma-evaluation-list
-   llma-evaluation-summary-create
-   generate-app-url

### exploring-llm-clusters

[View source](https://github.com/PostHog/posthog/tree/master/products/ai_observability/skills/exploring-llm-clusters)

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.

-   llma-clustering-job-list
-   llma-clustering-job-get
-   execute-sql
-   query-llm-traces-list
-   query-llm-trace

### exploring-llm-costs

[View source](https://github.com/PostHog/posthog/tree/master/products/ai_observability/skills/exploring-llm-costs)

Investigate LLM spend in PostHog — total cost over time, cost by model, provider, user, trace, or custom dimension, token and cache-hit economics, and cost regressions.

-   execute-sql
-   query-llm-traces-list
-   query-llm-trace
-   read-data-schema
-   insight-create
-   dashboard-create
-   alert-create
-   docs-search

### exploring-llm-evaluations

[View source](https://github.com/PostHog/posthog/tree/master/products/ai_observability/skills/exploring-llm-evaluations)

Investigate AI observability evaluations — `hog` (deterministic code-based), `llm_judge` (LLM-prompt-based), and `sentiment` (user-message sentiment).

-   llma-evaluation-list
-   llma-evaluation-get
-   llma-evaluation-create
-   llma-evaluation-update
-   llma-evaluation-delete
-   llma-evaluation-run
-   llma-evaluation-test-hog
-   llma-evaluation-summary-create
-   execute-sql
-   query-llm-trace

### exploring-llm-traces

[View source](https://github.com/PostHog/posthog/tree/master/products/ai_observability/skills/exploring-llm-traces)

ABSOLUTE MUST to debug and inspect LLM/AI agent traces using PostHog's MCP tools.

-   query-llm-traces-list
-   query-llm-trace
-   read-data-schema
-   execute-sql

Prefer PostHog to run these investigations for you and offer fixes when possible? Checkout [self-driving](/docs/ai-observability/self-driving.md).

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