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Agent skill

Malloy Lookml Review

by malloydata in malloydata/publisher

Analyze LookML files as prior art for Malloy modeling. An agent skill from malloydata/publisher.

MITAuto-check passedLegal & Compliance

Install Malloy Lookml Review

skills CLI
$ npx skills add malloydata/publisher --skill malloy-lookml-review -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install malloydata/publisher malloy-lookml-review --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/malloydata/publisher.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/malloy-lookml-review .claude/skills/malloy-lookml-review && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
malloy-lookml-review
GitHub stars
116
Token cost
~1.4k tokens
SKILL.md length
692 words
Files
9
Skills in repo
29
Repo updated
First seen
Licence
MIT

At a glance

Analyze LookML files as prior art for Malloy modeling. An agent skill from malloydata/publisher.

  • Works in 2 steps: Identify the target explore's… → Verify the service account actually has…
  • Tasks that involve Intellectual property
  • SKILL.md covers When to Use, Two Modes, Numeric Parity Validation… and Reference Files, plus 3 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Malloy Lookml Review is an agent skill from malloydata/publisher. Analyze LookML files as prior art for Malloy modeling. Used during Step 1 (DISCOVER) when .lkml files are present. Coordinates reference files that extract business logic, relationships, and curation decisions. Works with or without a database connection.

Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 9 other files (for example `reference/_concepts.md`, `reference/build-derived-tables.md` and `reference/build-unnest.md`).

It sits in Legal & Compliance, covering Intellectual property. The repository describes itself as: Publisher is the open-source analytics engine for Malloy. It lets you define data models once — and use them everywhere. The licence is MIT.

When your agent uses it

  • Tasks that involve Intellectual property

Example prompts

  • “/malloy-lookml-review”

Workflow steps

2 steps, taken from the first numbered list in SKILL.md.

  1. Identify the target explore's required_access_grants and the user attributes they key on.
  2. Verify the service account actually has non-empty values for those attributes. If it doesn't, the Looker path is a dead end: don't spend…

What it can do on your machine

Read from SKILL.md and the folder at commit c43a052. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Malloy Lookml Review loads about 1.4k tokens when it runs. Until then it costs about 69 tokens; SKILL.md has 692 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~69
When it runs · the whole SKILL.md, loaded when a task matches
~1.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from malloydata/publisher at commit c43a052, republished under its MIT licence (© malloydata). 692 words, ~1,410 tokens.

Download SKILL.mdSave it as .claude/skills/malloy-lookml-review/SKILL.md (or your agent's skills folder). This skill also uses 8 other files; get the full folder from GitHub.
name
malloy-lookml-review
description
Analyze LookML files as prior art for Malloy modeling. Used during Step 1 (DISCOVER) when .lkml files are present. Coordinates reference files that extract business logic, relationships, and curation decisions. Works with or without a database connection.
<!--
Copyright (c) Credible Data Inc.
SPDX-License-Identifier: MIT
-->

LookML Review

Purpose: Evaluate a LookML project as prior art for building a Malloy semantic model. This skill coordinates the LookML adapter. The implementation lives in reference files under reference/.

Tool names are written bare here - get_context, execute_query, search_malloy_docs. The exact prefixed name depends on the host surface; match each against the tools you actually have.

This is NOT a blind conversion. Each LookML pattern is evaluated for quality and relevance to Malloy. Bad practices, Looker-specific UI patterns, and performance-only constructs are identified and skipped.

When to Use

  • Auto-detected: The agent finds .lkml files during Step 1 (DISCOVER) and the user confirms they should be used as prior art.
  • Explicitly requested: The user says "model from LookML", "convert LookML", or provides a path to LookML files.

Two Modes

ModeWhenBehavior
LookML + live dataA connection is configured and you can query the dataLookML provides prior art; the data validates it. Full data-driven proposals.
LookML onlyNo connection, or queries return nothingLookML provides all context. Proposals flagged as unvalidated.

If in LookML-only mode, warn the user: "No database connection found. I'll use LookML as the sole source of context, but proposals cannot be validated against live data."

Numeric Parity Validation (preflight before you trust the Looker path)

To prove the Malloy numbers match Looker, there are two channels, and the "obvious" one fails silently more often than you'd expect.

Preflight the Looker-API path before attempting it. Running the original explore through the Looker API only works if the API service account satisfies that explore's required_access_grants. A service account that doesn't (e.g. its org_id user attribute is empty/NULL, or an *_user_id attribute the grant keys on is unset) gets a 404 on every restricted explore, indistinguishable at a glance from "explore not found", and cannot self-provision without administer/sudo. So before you build a parity harness on the Looker API:

  1. Identify the target explore's required_access_grants and the user attributes they key on.
  2. Verify the service account actually has non-empty values for those attributes. If it doesn't, the Looker path is a dead end: don't spend time discovering that through 404s.

SQL-level parity against the same warehouse is a first-class fallback, not a consolation prize. When the Looker path is blocked (or just as the primary method), validate by running equivalent SQL directly against the same warehouse the LookML explore reads and comparing to the Malloy result (execute_query). This is what actually validates the numbers in practice: reach for it first if access grants are in doubt.

Show full SKILL.md (274 more words)Show less

Reference Files

Each reference file is loaded by the workflow phase that needs it (via dispatch tables in each phase skill). You do not need to read them all at once.

Reference FilePhaseWhat It Does
reference/discover.mdStep 1 (DISCOVER)Inventory .lkml files, extract source candidates, capture prior-art notes
reference/propose-fields.mdStep 4 (DEFINE)Extract field proposals from .lkml views
reference/build-derived-tables.mdStep 5 (BUILD)Classify and convert LookML derived tables
reference/build-unnest.mdStep 5 (BUILD)Convert UNNEST joins and struct field access
reference/curate-visibility.mdStep 8 (CURATE)Map LookML visibility mechanisms to Malloy access modifiers
reference/document.mdStep 9 (DOCUMENT)Extract LookML descriptions as #(doc) tag seeds
reference/review-coverage.mdStep 7 (REVIEW)Compare Malloy model against LookML: source, field, and join coverage with rationale for gaps
Shared Reference

reference/_concepts.md is the LookML to Malloy concept mapping table. Referenced by propose-fields.md and build-derived-tables.md for type mapping and syntax translation.

What LookML Provides

  • Field names, descriptions, and business logic (accelerates Step 4)
  • Join relationships and cardinality (accelerates Step 3)
  • Field visibility decisions: hidden: yes, fields exclusions, required_access_grants (accelerates Step 8)
  • Organizational structure via group_label and view_label (informs source design)
  • Derived table intent: NDTs to computed sources, PDTs to evaluate

What to Skip

  • Looker UI patterns (link:, drill_fields:, html:, action:)
  • Liquid templating ({% %}, {{ }}): strip and note intent
  • parameter: definitions: note the business intent, don't replicate
  • PDT optimization (partition_keys:, datagroup_trigger:, increment_key:)
  • Dashboard files (.dashboard.lookml)
  • sql_always_where:: document as context, don't bake into Malloy

What to Flag for User Decision

  • Complex SQL dimensions (50+ lines): default is simplify or push upstream
  • Derived tables: classify as performance-only, transformation, or aggregation
  • Refinement structure (+view): consolidate vs. preserve layering via extend
  • Synthetic primary keys: ask about actual grain

© malloydata, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 8 other files in skills/malloy-lookml-review of malloydata/publisher.

  • SKILL.md
  • reference/_concepts.md
  • reference/build-derived-tables.md
  • reference/build-unnest.md
  • reference/curate-visibility.md
  • reference/discover.md
  • reference/document.md
  • reference/propose-fields.md
  • reference/review-coverage.md

Open the folder on GitHubat commit c43a052

Compare with similar skills

Malloy Lookml Review next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Malloy Lookml Review compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Malloy Lookml Review this skillmalloydata/publisher116—~1.4kAutomated safety check: PassMIT
Paper to Chinese Patent DrafterYuan1z0825/nature-skills47k1 repos~1.1kAutomated safety check: PassApache-2.0
Paper To Cn Patentsnipp-zha/Paper-to-patent-Skill1071 repos~959Automated safety check: PassNone
Patent Examinegfodor/legal-skills393—~4.8kAutomated safety check: PassGPL-3.0
Patent Auditgfodor/legal-skills393—~2.9kAutomated safety check: PassGPL-3.0
Replica BrandJakeschincariol/replica-skill1.4k—~1.1kAutomated safety check: PassMIT

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Categories

Questions about Malloy Lookml Review

What does Malloy Lookml Review do?

Analyze LookML files as prior art for Malloy modeling. An agent skill from malloydata/publisher. Malloy Lookml Review is an agent skill from malloydata/publisher. Analyze LookML files as prior art for Malloy modeling.

When should I use Malloy Lookml Review?

Malloy Lookml Review fits situations like: tasks that involve Intellectual property.

How do I install Malloy Lookml Review in Claude Code?

Run `npx skills add malloydata/publisher --skill malloy-lookml-review -a claude-code`. Or copy the skill folder (skills/malloy-lookml-review in malloydata/publisher) into .claude/skills/malloy-lookml-review in your project. Claude Code loads it when a task matches its description.

How do I install Malloy Lookml Review in Codex?

Run `npx skills add malloydata/publisher --skill malloy-lookml-review -a codex`. Or copy the skill folder (skills/malloy-lookml-review in malloydata/publisher) into .agents/skills/malloy-lookml-review in your project. Codex loads it when a task matches its description.

Can I use Malloy Lookml Review in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add malloydata/publisher --skill malloy-lookml-review -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/malloy-lookml-review, .gemini/skills/malloy-lookml-review, .github/skills/malloy-lookml-review and .opencode/skills/malloy-lookml-review in your project.

What does Malloy Lookml Review need to run?

SKILL.md names no scripts, command-line tools or credentials: Malloy Lookml Review is instructions for the agent only.

Does Malloy Lookml Review access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Malloy Lookml Review safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Malloy Lookml Review use?

Malloy Lookml Review is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Malloy Lookml Review use?

About 1.4k tokens (SKILL.md is roughly 5.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Malloy Lookml Review?

Skills that share tags, products or a category with Malloy Lookml Review: Paper to Chinese Patent Drafter (Yuan1z0825/nature-skills, 47k stars), Paper To Cn Patent (snipp-zha/Paper-to-patent-Skill, 107 stars), Patent Examine (gfodor/legal-skills, 393 stars) and Patent Audit (gfodor/legal-skills, 393 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Malloy Lookml Review?

malloydata (a GitHub organization) maintains it in malloydata/publisher, which has 116 GitHub stars. The repository holds 29 skills in this directory. The repository was last updated on October 10, 2026.

Source: malloydata/publisher on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.