Miller
DocumentsmillerMiller is an Agent Skill for documents published in the johnkerl/miller repository on GitHub: a SKILL.md instruction file an AI agent can load to do this task.
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.
How to use this skill
Bring this guide into your coding agent with a prompt tailored to the tool you use.
- Open your project in Codex.
- Copy and paste the prompt below.
- Review files and risks before approving any action.
Inspect the Agent Skill at https://github.com/johnkerl/miller/blob/HEAD/pkg/terminals/mcp/SKILL.md and all supporting files. Report provenance, permissions, scripts, dependencies, and security risks. Propose an exact installation into .agents/skills/miller, but wait for my approval before writing files or running scripts.
Copying this prompt does not install or run the skill. Review third-party files before use.
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:
whichwith e.g."join two files on a key"→ ranked candidates.confident: truemeans a name matched; trust the top hit. - Browse cheaply:
list_capabilitieswithindex: true→ every verb/function/flag/keyword with one-line summaries. - Drill in:
list_capabilitieswithkind: "verb", names: ["join"]→ the full entry. Prefer the structuredoptionslist (flag, arg, type, enumvalues) when present;usage_textis 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 thevaluesarray, not values you expect to exist. - Fields whose
countis less than other fields' are absent in some records: guard DSL withis_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/--c2jetc. (combined). - Chain verbs with
then:["--icsv", "sort", "-f", "k", "then", "head", "-n", "3", "f.csv"]. - If a field value being compared in
filtermight collide with a verb flag, end verb flags with--before filenames. - Inline data goes in
stdin_text; files go at the end ofargs.
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
runcannot execute external commands (DSLsystem/exec, piped redirects,--prepipe) unless the server was started with--allow-shell; such calls fail cleanly. It can write files viatee,split, and DSL output redirects — treat it as a write-capable tool.- Long inputs: prefer
describe_data+ targeted verbs over dumping whole files throughrun. - One record format in, another out: Miller is format-to-format; there is no separate conversion step.