From posthog
Explores PostHog MCP intent clusters that group agent goals by semantic similarity, showing tool distributions, error rates, and routing entropy. Use when analyzing what goals agents try to achieve with MCP tools and which ones fail.
How this skill is triggered — by the user, by Claude, or both
Slash command
/posthog:exploring-mcp-intent-clustersThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Intent clustering takes the free-text `$mcp_intent` values agents attach to
Intent clustering takes the free-text $mcp_intent values agents attach to
their tool calls, embeds them, and groups semantically similar goals into
clusters. Each cluster carries its tool distribution, call counts, and error
rates — answering "what are people trying to do, and does it work?" rather
than "which tool was called".
Unlike tool quality and sessions (which are plain HogQL over $mcp_tool_call),
clustering needs embeddings and is not expressible in SQL. It is served by
two typed tools backed by a stored snapshot.
| Tool | Purpose |
|---|---|
posthog:mcp-analytics-intent-clusters-retrieve | Fetch the latest cluster snapshot for the project |
posthog:mcp-analytics-intent-clusters-recompute | Trigger an async recompute of the snapshot |
posthog:mcp-analytics-intent-clusters-retrieve
{}
Returns a snapshot with status, last_computed_at, computed_with (the
embedding model and clustering parameters), and a clusters array. Each cluster
has a label, intent_count, call_count, error_count, error_rate_pct,
routing_entropy, a tool_distribution (which tools that goal routes to, with
per-tool error rates), and sample_intents.
Read clusters by call_count for "what are agents mostly doing", or by
error_rate_pct for "which goals are failing" — a high error rate on a cluster
points at a class of agent goals the tools serve badly.
routing_entropy is how spread-out a cluster's tool usage is: low entropy means
one goal reliably maps to one tool; high entropy means agents are casting around
for the right tool for that goal (often a missing-capability signal).
status: idle, clusters: []): no run has
happened yet. Trigger one (below) and tell the user it computes in the
background.last_computed_at: offer to recompute.posthog:mcp-analytics-intent-clusters-recompute
{}
Returns immediately with status: computing (HTTP 202); the work runs in the
background. Poll ...-retrieve until status returns to idle (done) or
error. Don't block waiting — tell the user to re-ask in a minute.
https://app.posthog.com/project/<project_id>/mcp-analytics/intent-clustering$mcp_intent coverage — if few calls carry
an intent, clusters will be sparse; cross-check intent coverage with a quick
countIf(toString(properties.$mcp_intent) != '') over $mcp_tool_callerror_rate_pct plus high routing_entropy is the
strongest "the tools don't serve this goal well" signal — worth a closer look
at its sample_intents and tool_distributionexploring-mcp-tool-quality —
per-tool error rates and latencyexploring-mcp-sessions — the individual
runs behind the intentsnpx claudepluginhub anthropics/claude-plugins-official --plugin posthogInvestigates PostHog MCP sessions by listing recent sessions or reading tool-call sequences for debugging agent runs. Generates LLM summaries of session intents.
Analyzes AI agent conversation logs to surface underserved topics, coverage gaps, and product opportunities. Use when your project has Amplitude Agent Analytics.
Automates Mixpanel analytics tasks like event aggregation, segmentation, funnels, cohorts, user profiles, and JQL queries via Composio's Rube MCP toolkit. Requires active connection.