data-analyselisted

Analyse a dataset and deliver the insights and key metrics that matter for it — an insight brief with headline findings, trends, breakdowns, concentration, outliers and ageing, tailored to the type of data (transactions, receivables, pipeline, survey, task list, any table). Use when the user says "analyse this data", "what are the key metrics", "any insights from this spreadsheet", "summarise this export", "what's driving the numbers", "who are the top customers", or hands over a table and asks what it says. Works INTENT-FIRST: asks what question the data should answer, profiles it, proposes a plan → you confirm → computes every metric deterministically (local engine, exact Decimal, currency-aware) → writes a calibrated brief separating observation from interpretation. Metrics computed locally by the engine. NOT data cleaning (data-tidy), NOT matching two datasets (data-reconcile), NOT a dashboard (data-visualise — natural next step); descriptive analysis only, never financial or investment advice.
moonlight-lupin/data-toolkit · ★ 0 · Data & Documents · score 72

Install: claude install-skill moonlight-lupin/data-toolkit

# Data Analyse Take a dataset — a clean(ish) `.xlsx`/CSV, a pasted table, or the output of another toolkit skill — and deliver an **insight brief**: the headline findings, the key metrics for that *type* of data, and honest caveats. The design is **intent-first** (ask what question the data should answer before computing anything) and **compute-then-interpret** (every quoted number comes from the deterministic local engine; the narrative interprets, it never generates figures). > **Self-sufficient & local engine.** All computation runs on your machine via > `scripts/analyse.py` (+ the shared toolkit engine) — no network calls. Note the AI agent > driving the skill does send whatever it reads into its context to your AI provider; > "never leaves" is not claimed. See `../../PRINCIPLES.md#data-handling--pii-policy`. ## Workflow ### 0 — Intent (ask up front) Before computing anything, ask (a short `AskUserQuestion`): 1. **What question should this data answer?** ("are sales growing?", "where's the risk in receivables?", "which segment matters?") — or is it an open "tell me what you see"? 2. **Who's it for and what decision does it feed?** — a partner one-pager reads differently from a working analysis. 3. **Anything known already?** — targets, prior-period figures, or a hypothesis to test. An open-ended "just analyse it" is fine — then the playbook (step 3) drives the metric selection, and the brief says so. ### 1 — Ingest Same shared engine as the other skills (`../.