estimate-online-covariance
Provides sklearn-style estimators for incrementally updating covariance, correlation, precision, and location matrices from streaming observations using constant work per observat…
Defines product metrics and measurement governance through metric trees, tracking plans, event taxonomies, instrumentation QA, dashboard contracts, and privacy-aware measurement practices.
Define observable, governed evidence for product outcomes through metric trees, event/tracking plans, instrumentation QA, and measurement governance. Use when defining product metrics, designing tracking plans, building event taxonomies, running product funnels or cohort analysis, setting up dashboard contracts, or auditing instrumentation quality. Do NOT use for statistical inference or experiment design (route to data-scientist), data pipeline implementation (route to data-engineering), data architecture decisions (route to data-architect), observability or infrastructure monitoring (route to site-reliability-engineering), or general business intelligence and dashboard building (route to BI tooling).
evals/evals.json
JSON
Other
README.md
Markdown
Other
templates/instrumentation-qa-checklist.md
Markdown
Other
templates/outcome-review.md
Markdown
Other
templates/tracking-plan.md
Markdown
Reference
references/discovery-brief.md
Markdown
Reference
references/metric-tree.md
Markdown
SKILL.md
SKILL.md
Markdown
Provides sklearn-style estimators for incrementally updating covariance, correlation, precision, and location matrices from streaming observations using constant work per observat…
Enables Python agents to update, assess, compare, and select online covariance, correlation, and precision estimators for streaming or dynamic-universe data.
Wraps positional online covariance estimators to process keyed observations whose named series can remain fixed or change dynamically over time.
Recommends covariance estimator classes for a 2-D dataset using dimension, conditioning, rank, correlation, and tail-behavior features, and exposes those features for inspection.
Guides an AI agent through end-to-end Kaggle competitions, including validation, modeling, OOF predictions, ensembling, remote notebook pipelines, artifact handoffs, submission sc…
Analyzes e-commerce reviews using 22-dimension AI tagging to extract customer profiles, pain points, VOC, product opportunities, anomaly signals, reports, dashboards, and optional…
Queries Open Library's public catalog for books, authors, works, editions, ISBNs, ratings, and cover URLs through a keyless Python CLI.
Assesses, designs, and evolves data architectures through workload analysis, platform and modeling tradeoffs, governance, data products, data mesh, event-driven flows, and migrati…