Skill UI

Guidance and local audit tools for Python workflows using GeoPandas GeoSeries, GeoDataFrame, spatial operations, CRS handling, and vector-data I/O. Includes safety gates for provenance, geometry validity, and privacy-aware geodata sharing.

This utility detects and redacts sensitive personally identifiable information (PII) from screenshots and images. It can mask various data types, including IDs, credit card numbers, API keys, phone numbers, and emails. Crucially, all processing is conducted locally and offline, ensuring that sensitive data never leaves the user's machine. It is essential for secure debugging, data sharing, and analysis workflows.

A comprehensive guide for preparing scholarly manuscripts involving spatial data. It outlines best practices for documenting data provenance (sources, projections, processing chains), ensuring reproducibility, and adhering to critical ethical standards such as geoprivacy and human-subjects protection. Essential for maintaining research integrity in academic publishing.

Provides comprehensive guidelines for researchers packaging and releasing artifacts (data, code, instruments) derived from real communities in CSCW research. It emphasizes ethical constraints, de-identification techniques (such as quote search and join attacks), and best practices for protecting studied communities' privacy, ensuring responsible sharing.

This skill performs an adversarial second pass to scan already de-identified clinical text for residual identifiers (PHI/PII). It combines deterministic regex checks (SSN, phone, dates) with a model-based PII detection rerun. It scores severity and implements a hard block rule: any detected leak prevents data release, ensuring the highest standard of data privacy and compliance.

This skill allows users to select and implement various compliance-aligned privacy policies (such as HIPAA Safe Harbor, GDPR pseudonymization, and PIPEDA) for robust de-identification. It manages complex policy profiles, ensuring data is masked, redacted, or pseudonymized according to specific legal requirements, while also supporting custom surrogate generators for advanced research needs.

This tool de-identifies sensitive free-text columns (e.g., names, dates, identifiers) in local structured datasets (CSV, JSONL, Parquet) using advanced OpenMed models. It ensures strict privacy by generating a completely redacted dataset and a separate PHI-free aggregate summary, allowing for secure data sharing and analysis without compromising source privacy or logging sensitive values.

This skill helps plan and review read-only data warehouse analysis with explicit scope, privacy, and validation checks. It turns business questions into reproducible analysis plans without assuming specific schemas. It identifies needed data, reviews proposed queries for risks like grain or privacy, and ensures results are communicated accurately. Suitable for authorized data warehouse usage, query review, and analysis documentation while avoiding administrative tasks.