9 data-science skills — dataset profiling & quality audit, data-dictionary generation, data-pipeline architecture, cohort analysis, anomaly-detection rule building, A/B test design, experiment readouts, forecasting model specs, and causal-impact analysis
0 on GitHubby John O'Connorv2.1.0MITupdated 8 days agodata-science
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Plugin data-science from marketplace official-business-plugins in web-lifter/official-business-plugins. Commands read from the repo's manifest — 8 days ago.
What it installs
Skills (9)
ab-test-designer
Design rigorous A/B/n experiments — hypothesis, power analysis, MDE, randomisation unit, guardrails, decision criteria — and route to stats-reviewer for peer-review.
anomaly-detection-rule-builder
Build rule-based and statistical anomaly detection systems for business metrics — revenue drops, traffic spikes, churn increases, cost overruns
causal-impact-analyser
Quasi-experimental design and analysis (diff-in-diff, synthetic control, ITS, regression discontinuity) for when randomised testing is infeasible. Routes to stats-reviewer.
cohort-analysis-builder
Design cohort analysis frameworks with SQL queries and visualisation specs for retention, revenue, and churn
data-dictionary-generator
Auto-generate comprehensive data dictionaries from database schemas, CSV files, or API responses with column definitions, relationships, and Mermaid ERD
data-pipeline-architecture
Design ETL/ELT pipeline architectures with data flow diagrams and transformation specs for Supabase and BigQuery
dataset-profiling-quality-audit
Profile datasets and audit data quality across six dimensions, producing prioritised cleaning recommendations
experiment-readout-builder
Analyse A/B test results — significance, CIs, segment cuts, novelty/primacy check, SRM, decision matrix application, and follow-up experiments.
forecasting-model-spec
Time-series forecast spec (ARIMA / Prophet / ML) with validation strategy, monitoring plan, and retraining triggers.
Statically analyze a local JS/Electron app you're authorized to study, with file+line evidence, and build an original reimplementation of what you find. Experimental. Phase 1: JS/Electron static analysis. Phase 2: native ELF/PE/Mach-O container inspection. Phase 3: .NET assembly metadata. Phase 4: EVM bytecode inventory. Phase 5: CUDA fatbin, cubin and PTX inventory. Parses, never executes. Dynamic analysis is a future phase, not yet implemented.
Full Domino Data Lab platform support — workspaces, jobs, model deployment, experiment tracking, GenAI tracing, Spark/Ray/Dask, and app deployment for data science teams
Define a Proof of Life probe to test a risky hypothesis cheaply.
1 skill
deanpeters/Product-Manager-Skills 8,001
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