Productivity

Reflect

by Reflect

Overview

Reflect turns your HR and workforce data into answers, right inside ChatGPT. Ask in plain language and Reflect queries your people analytics directly: headcount, turnover, recruitment, compensation and more, with no export or spreadsheet. It also surfaces the figures behind your legal and compliance reports (BDESE, Egapro index, EU pay transparency). It explores the data catalog you're allowed to see, runs structured analytics queries, and handles advanced SQL when needed. Read-only and secure by design: every request respects your existing Reflect permissions.

Tools

get_query

ChatGPT

Run a structured analytics query against the user’s Reflect workspace. Args: - query.measures: required array of fully-qualified measure titles, e.g. ["EMPLOYEES.COUNT"]. - query.dimensions: optional dimension titles, e.g. ["EMPLOYEES.DEPARTMENT"]. - query.dateRange / compareDateRange: { startDate, endDate } ISO dates. - query.filters: [{ member, operator, values }]. Cube operators: equals, notEquals, contains, gt, lt, gte, lte, set, notSet, inDateRange. - query.limit / offset / total. - query.order: [{ member, direction: "asc"|"desc" }, ...]. - scopeType: "COMPANY" (company-wide) or "USER_SCOPE" (filtered by caller’s permissions). Discover valid titles via list_schema first. Returns { data: row[], total? }.

list_schema

ChatGPT

Returns the catalog of Reflect cubes with their measures, dimensions, and joins, restricted to the members the current workspace and caller may actually query. Always call this first when writing a new analytics query so you use real measure/dimension titles instead of guessing. Filter by cube name to keep output small.

sql_query

ChatGPT

Run a read-only SQL query against the user’s Reflect workspace via the Cube SQL API. Use this when the user asks for a complex query (custom aggregations, expressions, or joinsrankings, filtering on measures, etc.). Cube SQL rules: cubes are tables in UPPER_SNAKE_CASE, measures must be wrapped in MEASURE(), dimensions are selected directly, and every selected dimension must appear in GROUP BY. Discover valid cube and column names via list_schema first, and add a LIMIT to keep output small. scopeType: "COMPANY" (company-wide) or "USER_SCOPE" (filtered by caller’s permissions). Returns { rows: row[], rowCount }.

Example Prompts

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Links

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App Stats

Tools

Prompts

ChatGPT

Platforms