Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add malloydata/publisher --skill malloy-lookml-reviewgit clone --depth 1 https://github.com/malloydata/publisherWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/malloydata/publisher/malloy-lookml-review)<a href="https://agentmods.dev/skills/malloydata/publisher/malloy-lookml-review"><img src="https://agentmods.dev/badge/skills/malloydata/publisher/malloy-lookml-review/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/malloydata/publisher/malloy-lookml-review"><img src="https://agentmods.dev/badge/skills/malloydata/publisher/malloy-lookml-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00058 | $0.01300 |
| Opus 5 | $0.00029 | $0.00650 |
| Sonnet 5 | $0.00012 | $0.00260 |
| Haiku 4.5 | $0.00006 | $0.00130 |
Grade A, and why
malloy-lookml-review scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
.lkmlfiles 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
| Mode | When | Behavior |
|---|---|---|
| LookML + live data | A connection is configured and you can query the data | LookML provides prior art; the data validates it. Full data-driven proposals. |
| LookML only | No connection, or queries return nothing | LookML 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:
What ships with it
8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 84 lines · 58 tokens per session scan A f2b9d618844b
malloy-lookml-review is a skill published in the GitHub repository malloydata/publisher (101 stars, last pushed today), licensed MIT. It adds 58 tokens to every session and 1,300 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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