Lean and directive throughout: a tools table, two short script invocations, and a tight five-step flow with no re-explanation of PostHog, MCP, or analytics concepts Claude already knows.

Fully executable guidance — named MCP tools, concrete commands ('python3 scripts/compare_to_prior_periods.py < query_result.json', 'WINDOW=7 ...'), exact field references (query.kind, trendsFilter.display === "BoxPlot", compareFilter: {"compare": true}), and a copy-paste findings template.

Steps are explicitly sequenced (1 classify → 2 opening moves 2.1/2.2/2.3 → 3 playbook → 4 cross-check → 5 findings) with validation checkpoints: 2.2 'If the movement is normal variance, report that and stop' and Step 4's control-segment test with an expand branch.

SKILL.md is a concise overview with clearly signaled one-level-deep references (seven playbooks, shared-patterns.md, common-causes.md) and two helper scripts; all referenced paths resolve to real bundle files, so navigation is clean.

Lists multiple concrete actions — 'orchestrating breakdowns, actors, paths, lifecycle, retention, and annotations queries' and diagnoses specific change types (dropped, spiked, plateaued) — matching the top anchor of named concrete actions.

Explicitly answers both what (diagnose why a metric changed via the listed query types) and when ('Use when the user reports an anomaly, asks "why did X change?", or needs root-cause analysis...').

Natural user phrasing is well covered: 'why did X change?', 'anomaly', 'root-cause analysis', plus metric-type terms (trend, funnel, retention, stickiness, lifecycle) a user would actually say.

A clear niche (product metric-change diagnosis) with distinct, specific triggers unlikely to fire for unrelated skills; not score 2 because the triggers are specific rather than broadly overlapping.