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 looker-open-source/looker-skills --skill creating-lookml-modelgit clone --depth 1 https://github.com/looker-open-source/looker-skillsWrote 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/looker-open-source/looker-skills/creating-lookml-model)<a href="https://agentmods.dev/skills/looker-open-source/looker-skills/creating-lookml-model"><img src="https://agentmods.dev/badge/skills/looker-open-source/looker-skills/creating-lookml-model/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/looker-open-source/looker-skills/creating-lookml-model"><img src="https://agentmods.dev/badge/skills/looker-open-source/looker-skills/creating-lookml-model.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 | $0.01598 |
| Opus 5 | $0.00029 | $0.00799 |
| Sonnet 5 | $0.00012 | $0.00320 |
| Haiku 4.5 | $0.00006 | $0.00160 |
Grade A, and why
creating-lookml-model 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 12d 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 — 176 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Creating LookML Model, Views & Writing LookML
This skill guides you through the process of writing all the LookML (view files and model files) needed to prepare for the dashboard requested by the user during their onboarding interview, configuring the connection mapping in Looker, validating the LookML code, and running inline queries to verify it functions correctly.
Prerequisites & References
- Looker CLI must be installed and authenticated (Step 2 & 3).
- Looker project and BigQuery connection must be created (Step 4 & 5).
- Critical Reference: You MUST read and strictly follow the best
practices, naming requirements, and constraints in the
lookml-modeling-guidelinesskill for writing and verifying all LookML elements.
Instructions
1. Retrieve Onboarding Goal and Schema Specifications
Refer back to the onboarding goal and BigQuery exploration results established with the user during the Step 1 (Discovery) interview in the conversation history. You must recall: 1. The target Dashboard Goal (e.g., Sales Overview, Support Metrics, etc.). 2. The specific GCP Project, Dataset, and Tables (fact and dimension) agreed upon. 3. The target Metrics (measures) and Dimensions to be used.
Note: Do not invent or guess these details. The names of your views, fields, explores, and models must directly map to the schema and tables you discovered in Step 1.
2. Create and Edit View Files (CLI)
For each BigQuery table required to build the target dashboard, you must write the LookML view definition and upload it to the project:
-
Create the
viewsdirectory: If theviews/directory does not yet exist in the project, create it using the Looker CLI:looker-cli project directory create {project_id} views -
Generate View Content: Retrieve the table columns (from Discovery) and write the LookML view file content locally (e.g. to
/tmp/{table_name}.view.lkml). Follow the primary key and field definitions guidelines.
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.
- 12d ago First seen · 176 lines · 58 tokens per session scan A 319af3a22eef
creating-lookml-model is a skill published in the GitHub repository looker-open-source/looker-skills (28 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 58 tokens to every session and 1,598 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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