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 j4flmao/agent-skills --skill analytics-engineeringgit clone --depth 1 https://github.com/j4flmao/agent-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/j4flmao/agent-skills/analytics-engineering)<a href="https://agentmods.dev/skills/j4flmao/agent-skills/analytics-engineering"><img src="https://agentmods.dev/badge/skills/j4flmao/agent-skills/analytics-engineering/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/j4flmao/agent-skills/analytics-engineering"><img src="https://agentmods.dev/badge/skills/j4flmao/agent-skills/analytics-engineering.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 | $0.00218 | $0.04823 |
| Opus 5 | $0.00109 | $0.02412 |
| Sonnet 5 | $0.00044 | $0.00965 |
| Haiku 4.5 | $0.00022 | $0.00482 |
Grade A, and why
data-science-analytics-engineering 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 6d 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 — 566 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analytics Engineering
Purpose
Design and build production analytics pipelines with dbt Core (models, materializations, Jinja macros, ref/source, tests, documentation), metrics and semantic layers (dbt Metrics, MetricFlow, Cube.js, metric definitions, dimensions, filters, time granularity), data modeling for analytics (marts approach, One Big Table, dimensional modeling, medallion architecture), and analytical SQL (window functions, CTEs, pivoting, statistical functions, time series, performance optimization, UDFs).
Agent Protocol
Trigger
Exact user phrases: "analytics engineering", "dbt", "dbt model", "dbt materialization", "Jinja macro", "dbt ref", "dbt source", "dbt test", "dbt doc", "metrics layer", "semantic layer", "MetricFlow", "Cube.js", "dbt metrics", "data modeling", "marts approach", "OBT", "One Big Table", "dimensional modeling", "medallion architecture", "bronze silver gold", "SQL analytics", "window function", "CTE", "pivot", "unpivot", "analytical SQL", "time series SQL", "SQL UDF".
Input Context
Before activating, verify:
- Transformation tool (dbt Core, dbt Cloud, SQLMesh)
- Data warehouse (Snowflake, BigQuery, Redshift, Databricks, Postgres)
- BI tools (Tableau, Looker, Power BI, Metabase)
- Existing data model layer (raw, staging, intermediate, marts)
- dbt version and packages installed (dbt_utils, dbt_expectations)
- CI/CD setup (GitHub Actions, dbt Cloud CI)
- Testing and documentation practices
Output Artifact
dbt project configuration, model SQL, macro definitions, metric definitions, data model documentation, and analytical query patterns.
Response Format
-- dbt model code
-- Analytical SQL queries
-- dbt project config, schema.yml, metrics definitions
-- Python dbt models
-- MetricFlow config
No preamble. No postamble. No explanations. No filler/hedging/transitions. Compress output — why use many token when few do trick.
Completion Criteria
- dbt project initialized with folder structure (staging, intermediate, marts)
- Source definitions and staging models for all raw data
- Intermediate models for business logic and transformations
- Mart models for consumption (dimension, fact, aggregate tables)
- Metrics defined and exposed via semantic layer
- Tests defined for critical columns (unique, not_null, relationships)
- Documentation generated with dbt docs
- Analytical SQL queries for common analytics patterns
What ships with it
6 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.
- 6d ago First seen · 566 lines · 218 tokens per session scan A ac881239589e
data-science-analytics-engineering is a skill published in the GitHub repository j4flmao/agent-skills (22 stars, last pushed 3d ago), licensed MIT. It adds 218 tokens to every session and 4,823 once invoked, about $0.0011 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 2026-09-03.
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