Provides an evidence-first workflow for profiling, validating, transforming, reconciling, and documenting messy tabular, text, JSON, and relational data.
Clean, profile, validate, reshape, and document messy tabular, text, JSON, and relational data through an evidence-first, reproducible workflow. Use when preparing data for analysis, reporting, modeling, ingestion, migration, or matching, including AI-suggested repair, entity-match review, score calibration limits, and reversible repair ledgers. Do not use for statistical modeling, dashboard design, or operating a named data platform; route those tasks to data-scientist, data-engineering, or the relevant tool skill.
evals/evals.json
JSON
Other
evals/trigger-cases.json
JSON
Other
README.md
Markdown
Other
templates/ai-repair-ledger.csv
Other
templates/cleaning-plan.md
Markdown
Other
templates/decision-log.csv
Other
templates/exception-register.csv
Other
templates/quality-report.md
Markdown
Other
templates/schema-contract.yml
YAML
Other
templates/transformation-log.jsonl
Reference
references/ai-repair-review.md
Markdown
Reference
references/cli-and-interactive-tools.md
Markdown
Reference
references/methodology.md
Markdown
Reference
references/operations.md
Markdown
Reference
references/sources.md
Markdown
Reference
references/text-and-entity.md
Markdown
Reference
references/tool-selection.md
Markdown
Reference
references/validation-and-scale.md
Markdown
Script
scripts/profile_dataset.py
Python
Script
scripts/reconcile_dataset.py
Python
Script
scripts/test_profile_dataset.py
Python
Script
scripts/test_reconcile_dataset.py
Python
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…
Assesses, designs, and evolves data architectures through workload analysis, platform and modeling tradeoffs, governance, data products, data mesh, event-driven flows, and migrati…
Queries Open Library's public catalog for books, authors, works, editions, ISBNs, ratings, and cover URLs through a keyless Python CLI.