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Drive Miller (mlr) to process CSV/TSV/JSON/etc. data. Use when constructing mlr command lines: discover capabilities from the catalog rather than guessing, learn the data's shape before writing expressions, validate DSL before running, and recover from failures via structured errors.

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Source SKILL.md: https://github.com/johnkerl/miller/blob/HEAD/pkg/terminals/mcp/SKILL.md

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Miller agent playbook

Miller (mlr) is a command-line data processor for CSV, TSV, JSON, JSON Lines, and other tabular/record formats, with SQL-like verbs (cut, sort, join, stats1, ...) and an awk-like DSL (put, filter).

Work this loop. Each step exists to prevent a specific, common failure.

1. Discover — never invent names

Everything valid is in the catalog; anything not in the catalog does not exist. Hallucinated flag/function names are the top failure mode.

  • Route an intent: which with e.g. "join two files on a key" → ranked candidates. confident: true means a name matched; trust the top hit.
  • Browse cheaply: list_capabilities with index: true → every verb/function/flag/keyword with one-line summaries.
  • Drill in: list_capabilities with kind: "verb", names: ["join"] → the full entry. Prefer the structured options list (flag, arg, type, enum values) when present; usage_text is the prose fallback.
  • The whole catalog is cacheable against (mlr_version, catalog_schema_version) — re-fetch only when either changes.

2. Constrain — learn the data before touching it

Call describe_data on the input first. It returns, per field: name, types seen with counts, occurrence count, null count, cardinality, min/max, and — for low-cardinality fields — every distinct value.

  • Copy field names exactly from describe_data; never guess casing or spelling.
  • For flags like -g (group-by) and DSL comparisons, use values from the values array, not values you expect to exist.
  • Fields whose count is less than other fields' are absent in some records: guard DSL with is_present($field).

3. Validate — check DSL before spending a run

Before any run that includes put or filter, call validate_dsl with the expression. Cost: parse-only, no data read. On valid: false, the error document has kind, hint, and did_you_mean — apply the hint, don't re-guess syntax.

4. Run — and read errors structurally

Call run with argv as a list, one element per shell word (no shell quoting):

{"args": ["--icsv", "--ojson", "cat", "data.csv"]}

Command-line shape rules that prevent most argv errors:

  • Main flags (I/O formats etc.) come before the verb: mlr --icsv sort -f name f.csv.
  • Format shorthands: --icsv --ojson (separate in/out), --csv/--c2j etc. (combined).
  • Chain verbs with then: ["--icsv", "sort", "-f", "k", "then", "head", "-n", "3", "f.csv"].
  • If a field value being compared in filter might collide with a verb flag, end verb flags with -- before filenames.
  • Inline data goes in stdin_text; files go at the end of args.

On failure, exit_code is nonzero and error (when present) carries kind, hint, and did_you_mean — hint is often a corrected command line; prefer executing it over reasoning from the message. stdout_truncated: true means the output exceeded the server's cap: narrow the query (e.g. head, cut) rather than re-running the same command.

Notes

  • run cannot execute external commands (DSL system/exec, piped redirects, --prepipe) unless the server was started with --allow-shell; such calls fail cleanly. It can write files via tee, split, and DSL output redirects — treat it as a write-capable tool.
  • Long inputs: prefer describe_data + targeted verbs over dumping whole files through run.
  • One record format in, another out: Miller is format-to-format; there is no separate conversion step.