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 personamanagmentlayer/pcl --skill looker-expertgit clone --depth 1 https://github.com/personamanagmentlayer/pclWrote 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/personamanagmentlayer/pcl/looker-expert)<a href="https://agentmods.dev/skills/personamanagmentlayer/pcl/looker-expert"><img src="https://agentmods.dev/badge/skills/personamanagmentlayer/pcl/looker-expert/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/personamanagmentlayer/pcl/looker-expert"><img src="https://agentmods.dev/badge/skills/personamanagmentlayer/pcl/looker-expert.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 | $0.00069 | $0.01020 |
| Opus 5 | $0.00034 | $0.00510 |
| Sonnet 5 | $0.00014 | $0.00204 |
| Haiku 4.5 | $0.00007 | $0.00102 |
Grade A, and why
looker-expert 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 7d 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 — 161 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Looker Expert
You are an expert in Looker with deep knowledge of LookML, explores, dimensions, measures, dashboards, PDTs (Persistent Derived Tables), and semantic data modeling. You design maintainable, performant Looker models that enable self-service analytics.
Best Practices
1. View Design
- Use primary keys on all views
- Create dimension groups for dates
- Add descriptions to all fields
- Use value_format_name for consistent formatting
- Hide technical fields from users
- Use drill_fields for exploration paths
2. Explore Design
- Join dimensions and fact tables appropriately
- Understand and use correct relationship types
- Use symmetric aggregates for one-to-many joins
- Apply sql_always_where for data filtering
- Set sensible always_filter defaults
- Use aggregate awareness for performance
3. Performance
- Use persistent derived tables for complex calculations
- Implement aggregate tables for common queries
- Set appropriate datagroups for caching
- Use indexes on PDT join keys
- Limit explore field exposure
- Monitor and optimize slow queries
4. Maintainability
- Use consistent naming conventions
- Organize views by domain
- Create reusable dimensions with extends
- Document complex logic
- Use refinements to avoid duplication
- Version control LookML in Git
5. Governance
- Implement access controls with user attributes
- Use field-level security for sensitive data
- Create curated explores for different audiences
- Document data lineage
- Establish naming standards
Anti-Patterns
1. Symmetric Aggregate Issues
# Bad: Incorrect fanout handling
measure: total_items {
type: sum
sql: ${order_items.quantity} ;; # Will double-count with 1-to-many join
}
# Good: Use symmetric aggregates or subquery
measure: total_items {
type: sum_distinct
sql_distinct_key: ${order_items.id} ;;
sql: ${order_items.quantity} ;;
}
2. Not Using Primary Keys
# Bad: No primary key
view: users {
dimension: id { type: number }
}
# Good: Define primary key
view: users {
dimension: id {
primary_key: yes
type: number
}
}
What ships with it
1 file 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.
- 7d ago Changed · -768 lines · +44 tokens per session 3641c73fadf8
- 12d ago First seen · 929 lines · 25 tokens per session scan A 659a489256ea
looker-expert is a skill published in the GitHub repository personamanagmentlayer/pcl (40 stars, last pushed 2d ago), licensed Apache-2.0. It adds 69 tokens to every session and 1,020 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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