moonlight-lupin

data-analyse

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.

data-convert

Re-express structured data to hit a DIFFERENT target — map a source (system A's export) onto another system's import CONTRACT, and/or RESHAPE its structure (wide↔long, nested JSON↔flat table, split one file into many, union many into one). Use when the user wants to "convert this to X's format", "map these columns to the import template", "get this into the format System B needs", "reshape / pivot / unpivot this", "flatten this JSON", "split this file by column", "combine these files", or "prepare this for upload/import". Works INTENT-FIRST: proposes the target shape from the contract + purpose, you confirm, it applies deterministically and writes a reusable **conversion card** (Markdown + an embedded JSON spec) a future agent re-runs after sense-checking the source. Delegates cleaning to data-tidy; never invents values; live inputs (FX) are pinned/user-approved, not fetched. NOT data cleaning (data-tidy), NOT matching two record sets (data-reconcile), NOT raw file-format-only conversion.

data-extract

Get STRUCTURED data OUT of documents — PDFs (incl. multi-table/scanned via local OCR), Word, PowerPoint (.pptx), and Outlook .msg — into a clean .xlsx plus an audit report. Use when the user wants to "extract data from this PDF/document", "pull the table out of this report", "get the figures from these statements/certificates", "turn these confirmations into a table", "read the fields off this form", or "extract these line items". Two modes: key-value/FORM extraction (label → value, one record per document — certificates, confirmations, cover sheets) and TABLE extraction (list a document's tables, pick one, pull it). Intent-first; normalises via the shared engine (dates → DD MMM YYYY, currency → amount + code) and flags anything unfound or uncertain — never invents values. Extraction is computed locally (local OCR only). NOT for already-tabular data (use data-tidy) or deal-document intelligence like lease abstraction/model review (out of scope).

data-reconcile

Reconcile any two record sets (A vs B) and triage the discrepancies — match line-by-line on a shared key, or heuristically on amount + date, then classify every unreconciled item (category, materiality, probable cause, suggested action) into a reconciliation working paper (.xlsx). Use whenever the user wants to "reconcile A to B", "reconcile the bank to the ledger", "reconcile the invoice tracker to the accounts", "tie out", "match these two lists/exports", "find the differences between", "what doesn't match", "reconciliation working paper", or "discrepancy triage". Ships presets for common recurring reconciliations (invoice tracker vs accounting records, bank vs cashbook, fund administrator vs internal records, payments/PRF vs bank); generic for anything else. Deterministic and computed locally — it never force-fits a match and never posts an adjustment; it produces a working paper for finance to review and sign off. NOT budget/variance analysis, and NOT deal/asset analysis (that is out of scope).

data-tidy

Clean up messy data into a structured, validated table — from any source (a junk-filled .xlsx/CSV, a pasted email/markdown table, a Word table, or an Outlook .msg) to a clean .xlsx plus an audit/change report. Use when the user wants to "clean up this data", "tidy this spreadsheet", "normalise this list", "structure this messy export", "dedupe this", "standardise these dates / currencies", or "turn this into a clean table". (For getting data OUT of PDFs — tables, forms, scanned documents — use data-extract, not this skill.) Works INTENT-FIRST: it asks what the data is for and the expected output up front, then profiles → proposes a transform recipe → you confirm → applies it deterministically → reports every change and flags cells for review. Never mangles silently; transforms are computed locally by a deterministic engine. Standalone — no other toolkit required. NOT deal-document intelligence (lease abstraction, model review, comps) — that's out of scope.

data-visualise

Orchestrate visual output from tabular data or a data-analyse analysis.json into either (1) a brandable self-contained HTML dashboard (print/PDF / artifact) or (2) an Excel workbook of native charts for analysts. Use when the user says "build a dashboard", "visualise this", "make a chart / KPI cards / scorecard", "Excel charts", "chart this in a spreadsheet", "a one-pager of these numbers", "RAG status board", or wants a shareable visual summary. HTML path: inline SVG, no CDN. Excel path: openpyxl charts with OfficeCLI-aligned chartType names (column/bar/line/pie/doughnut/waterfall). Draft for review, not advice. NOT PowerPoint or letters; clean/extract first via data-tidy / data-extract; compute metrics via data-analyse when numbers must be exact.