letin: Power Query (M)

Add a standard Power Query step without hand-writing M. `action` and `params` come from list_transforms, e.g. action='unpivot_other_columns', params={'keep_columns': ['Region']}; or action='group_by', params={'group_columns': ['Region'], 'aggregations': [{'name': 'Total', 'operation': 'sum', 'column': 'Amount'}]}. The step is appended to the end unless after_step names an earlier step (the next step is rewired, as the editor does).

Copy queries between workbooks / projects / folders, bringing the queries they depend on along.

Delete a query. Refuses when other queries reference it unless force=true.

Query-to-query lineage. With `query`, only its upstream and downstream. format: 'json' or 'mermaid'. Also reports roots (read external data), leaves (final outputs), orphans and cyclic references.

Compare the queries of two sources (workbook vs workbook, workbook vs exported folder, PBIP vs PBIP ...). Line endings are ignored. To compare against a snapshot pass its `path` from list_snapshots.

Run any M expression and get the result as data. Tables come back with column names, types and the first `rows` rows (cell errors shown as #ERROR); lists, records, scalars and functions (with signature) are described too; an M error comes back as reason + message. With `source`, the expression can reference that source's queries by name (e.g. Table.RowCount(Sales)), including unsaved edits of an open workbook. count_rows and profile (Table.Profile: min/max/nulls/distinct per column) scan the whole result, so they can be slow. ignore_privacy_levels=true disables the privacy firewall for this throwaway evaluation only.

Freshly evaluate a query, or the query only up to `step`, and return columns, types and sample rows. Use it to get real column names before apply_transform, to see what a step does to the data, or to find the step where an error first appears (evaluate step by step).

Evaluate EVERY step of a query in one engine call and describe each one (columns, types, first `rows` rows, or the error). The fastest way to see how a query transforms its data and to find the step where an error first appears: one call instead of evaluate_query once per step.

Write every query to `folder` as one .pq file each plus pq-manifest.json: ready to commit to git and diff in a PR. Existing .pq files in the folder are replaced.

Turn a hard-coded text value (path, server, URL) into a Power Query parameter and reference it everywhere. With match_prefix, "C:\Data" also rewrites "C:\Data\sales.csv" to SourceFolder & "\sales.csv".

Search the M of every query. scope: all | code | strings | comments. Returns query, step and line for each hit.

Pretty-print M (an expression or a section document): one step per line, long calls broken at their arguments, comments kept. The output is verified token-for-token against the input, so meaning can never change.

Format one query, or every query, in place (same guarantees as format_m).

Full M code of one query plus its parsed steps, data sources, dependencies and dependents.

Load .pq files from `folder` back into `source`. mode='update' changes/adds queries; mode='sync' also deletes queries that are not in the folder.

Audit queries for folding breakers, hard-coded paths/servers, dead steps, brittle column lists, untyped columns, duplicated logic, orphans and cycles. Each finding carries a concrete suggestion. min_severity: info | warning | error.

Inventory of every external source (files, folders, servers, URLs) each query reads, and whether it is hard-coded or parameterised.

Every query with its kind (query/function/parameter), step count, load destination, and what it depends on / is used by.

Snapshots taken automatically before each write to this source, newest first.

List workbooks open in Excel and models open in Power BI Desktop (both usable as `source`), and explain the other source forms.

Catalog of guided actions for apply_transform (unpivot, pivot, group_by, merge, filter, types, ...) with their params.

Look up the M standard library (generated from the real engine: 850+ members). Give an exact name ('Table.Group') for its signature and description, a namespace ('Table.' or 'List') to list its members, or words to search names and descriptions ('unpivot', 'split text delimiter'). Use it instead of guessing signatures.

Column names and first rows of a query that is loaded to a worksheet table (open Excel workbook only). Use it to get real column names before apply_transform.

Refresh one query in an open Excel workbook and wait for it; returns the mashup engine's error text if it fails.

Delete a step and rewire the steps that used it to the step it read from.

Rename a query and rewrite every reference to it in all other queries (token-aware: strings, comments and same-named columns are left alone).

Rename a step inside a query and every reference to it.

Find and replace across all (or the listed) queries in one go, e.g. repoint a server or folder. scope: 'strings' (only inside text literals; safest, the default), 'code' (outside strings and comments), 'all'.

Roll back to a snapshot. With `queries`, only those are restored; otherwise the whole query set is put back (queries created since are deleted). 'latest' undoes the most recent write.

Replace a query's M (or create it with create_if_missing). Rejects unbalanced brackets/strings before writing. For standard actions prefer apply_transform; for renames prefer rename_query / rename_step.

Check M before using it: syntax errors with line/column and a plain-English cause (missing comma between steps, unquoted step name, if without else ...), unknown steps/queries/functions with did-you-mean, and wrong argument counts for library functions. Pass `formula` (an expression or a whole section document); add `source` so its query names are known. Or pass `source` (+ optional `query`) alone to validate what is already there.