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 vasilyu1983/AI-Agents-public --skill data-analytics-engineeringgit clone --depth 1 https://github.com/vasilyu1983/AI-Agents-publicWrote 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/vasilyu1983/ai-agents-public/data-analytics-engineering)<a href="https://agentmods.dev/skills/vasilyu1983/ai-agents-public/data-analytics-engineering"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/data-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/vasilyu1983/ai-agents-public/data-analytics-engineering"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/data-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 | $0.00040 | $0.04365 |
| Opus 5.5 | $0.00016 | $0.01746 |
| Sonnet 5.5 | $0.00008 | $0.00873 |
| Haiku 4.5 | $0.00004 | $0.00436 |
Grade A, and why
data-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 1mo 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 — 268 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Analytics Engineering
Code-defined marts, metrics as APIs, contracts on critical interfaces, semantic layers only where they improve reuse or AI/BI consumption, and metadata systems that expose owners, lineage, quality, and governance to both humans and agents.
Primary sources: data/sources.json. Refresh time-sensitive claims against official docs before giving definitive recommendations.
When to Use
- Choose or improve an analytics engineering stack (
dbt,SQLMesh,Coalesce) - Define marts, grains, dimensions, facts, wide tables, or activity schemas
- Design or migrate a semantic layer (
dbt Semantic Layer,Lightdash,Cube, warehouse-native) - Add data contracts, metric governance, ownership, catalogs, and lineage
- Build data quality checks, freshness monitoring, anomaly detection, and release gates
- Prepare BI-ready models for dashboards, notebooks, APIs, or AI/NLQ analytics
When NOT to Use
- Lakehouse or ingestion architecture -> data-lake-platform
- Product/event instrumentation, attribution, or identity resolution ->
marketing-product-analytics - OLTP tuning, indexes, locks, or transactional database operations -> data-sql-optimization
- Metabase API automation -> data-metabase
- ML feature engineering, experiments, or model evaluation -> ai-ml-data-science
Triage Checklist
Run through these before any recommendation:
- What are the canonical business metrics and who owns each one?
- Serving requirements: dashboards, notebooks, APIs, embedded analytics, or AI/NLQ?
- Transformation baseline:
dbt,SQLMesh, visual tooling, or warehouse SQL only? - Which datasets are contract-worthy (downstream consumers depend on schema, freshness, semantics)?
- Semantic layer needed, or are well-governed marts sufficient today?
- Which metadata systems already cover catalog, lineage, ownership, access, and quality?
What ships with it
34 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.
- agents/openai.yaml 373 B
- assets/data-quality-incident-runbook.md 721 B
- assets/data-quality-test-plan.md 881 B
- assets/metric-change-notice.md 480 B
- assets/metric-dictionary.md 961 B
- assets/ownership-catalog-worksheet.md 554 B
- assets/semantic-layer-migration-checklist.md 918 B
- assets/semantic-layer-spec.md 1.2 KB
- assets/transformation/dbt/template-dbt-project.md 7.6 KB
- assets/transformation/sqlmesh/template-sqlmesh-dag.md 993 B
- assets/transformation/sqlmesh/template-sqlmesh-incremental.md 1.5 KB
- assets/transformation/sqlmesh/template-sqlmesh-layering-and-access.md 6.1 KB
- assets/transformation/sqlmesh/template-sqlmesh-model.md 17 KB
- assets/transformation/sqlmesh/template-sqlmesh-production.md 23 KB
- assets/transformation/sqlmesh/template-sqlmesh-project.md 7.3 KB
- assets/transformation/sqlmesh/template-sqlmesh-security.md 19 KB
- assets/transformation/sqlmesh/template-sqlmesh-testing.md 12 KB
- data/sample-metric-dictionary.json 7.8 KB
- data/sources.json 15 KB
- data/valid-metric-dictionary.json 8.8 KB
- learnings.consolidated.md 602 B
- learnings.md 359 B
- references/contracts-catalogs-lineage.md 5.3 KB
- references/data-quality-testing.md 6.7 KB
- references/lake-data-quality-patterns.md 15 KB
- references/metric-governance.md 6.5 KB
- references/modeling-patterns.md 8.4 KB
- references/pii-vault-and-pseudonymisation.md 16 KB
- references/release-and-ci-patterns.md 4.7 KB
- references/semantic-layer-patterns.md 9.0 KB
- references/tool-comparison.md 12 KB
- references/transformation-patterns.md 11 KB
- scripts/analytics_linter.py 23 KB runs code
- scripts/README.md 2.1 KB
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.
- 1mo ago Changed · +3 lines ac76cac15952
- 1mo ago Changed · +9 lines cc68aed38e78
- 2mo ago First seen · 256 lines · 40 tokens per session scan A 9480df31148d
data-analytics-engineering is a skill published in the GitHub repository vasilyu1983/AI-Agents-public (101 stars, last pushed 21d ago), licensed MIT. It adds 40 tokens to every session and 4,365 once invoked, about $0.0002 per session on Opus 5.5. 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.
data-analytics-engineering compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| data-analytics-engineeringvasilyu1983/AI-Agents-public | Skill | 101 | 21d ago | MIT | A | 40 | Claude Code, Codex |
| postgresql-table-designwshobson/agents | Skill | 42k | yesterday | MIT | A | 37 | Claude Code |
| sql-optimization-patternswshobson/agents | Skill | 42k | yesterday | MIT | A | 44 | Claude Code |
| event-store-designwshobson/agents | Skill | 42k | yesterday | MIT | A | 33 | Claude Code |
| cqrs-implementationwshobson/agents | Skill | 42k | yesterday | MIT | A | 35 | Claude Code |
| projection-patternswshobson/agents | Skill | 42k | yesterday | MIT | A | 36 | Claude Code |
| erd-diagramclosedloop-ai/claude-plugins | Skill | 123 | yesterday | Apache-2.0 | A | 131 | Claude Code |