Super data analytics

Skill arpitexplores/super-data-analytics

Portable Markdown skill for AI vibe coding with data and analytics: pipelines, BI dashboards, SQL optimisation, metrics, data quality, and analytics workflows.

Install

npx -y skills add arpitexplores/super-data-analytics

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Data & analytics: pipelines, BI, SQL optimisation, analytics tooling, and dashboards.

SKILL.md

1.7 KB, 330 tokens by cl100k_base, as published. Nobody here has run it

Super Data & Analytics

Overview

Build reliable data pipelines and analytics outputs with measurable insights.

User Intent Examples

"Need help with Product Analytics for my product/site."
"Create a plan for Data Engineering."
"Audit or improve Data Science."

Workflow

Define business questions, metrics, and data sources.
Design ingestion and transformation pipelines.
Select storage, modelling, and access patterns.
Implement analytics, dashboards, and reporting.
Validate data quality and performance.
Document lineage, ownership, and SLAs.

Minimal Intake Questions

Primary goal or outcome
Scope (pages, systems, teams, or timeframe)
Constraints (tools, budget, timeline)

Output Format

Data pipeline plan
Data model and storage choices
Analytics and dashboard spec
Data quality checklist
Operations and SLA notes

Routing Map (Modules)

Product Analytics -> references/modules/analytics-product.md
Data Engineering -> references/modules/data-engineer.md
Data Science -> references/modules/data-scientist.md

Bundled References

references/modules/
scripts/
assets/
agents/

Compatibility Notes

If any module references slash commands or tool-specific paths, translate them into plain-language steps.
Keep outputs platform-agnostic unless the user specifies a specific tool, stack, or agent.

Guardrails

Do not report metrics without validation.
Separate raw data from transformed outputs.
Track lineage and ownership explicitly.

Gives 0 of the 12 instructions most analytics metrics skills give in 330 tokens

Counted across 368 of the 369 authors here whose files we hold, read 2026-08-06

read product marketing context before asking questionsin 18 of 368, across 12 files
use lowercase with underscores for event namesin 16 of 368, across 6 files
track events for decisions not vanity metricsin 15 of 368, across 5 files
use object-action format for event namesin 15 of 368, across 8 files
produce a tracking plan documentin 14 of 368, across 4 files
Call RUBE_SEARCH_TOOLS first to get current schemasin 13 of 368, across 2 files
establish consistent event naming conventions before implementingin 10 of 368, across 4 files
Verify dimension and metric compatibility before reportingin 9 of 368, across 2 files
Encrypt data at rest and in transitin 9 of 368, across 3 files
use snake_case for event namesin 9 of 368, across 5 files
monitor technical health during the testin 9 of 368, across 5 files
use consistent property namesin 8 of 368, across 4 files

Said here and by no other author read

Define business questions, metrics, and data sources
Design ingestion and transformation pipelines
Select storage, modelling, and access patterns
Implement analytics, dashboards, and reporting
Document lineage, ownership, and SLAs
Translate tool-specific paths into plain-language steps

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.