Results for “data”

Analyze SQLite databases using the better-sqlite3 Node.js library, including querying, inserting, and iterating over rows.

Engineering privacy and data governance: PII detection, data flow mapping, consent patterns, GDPR/CCPA-compliant implementation, DPIA. Use when privacy-by-design is needed.

Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets.

Use to map where support conversation data actually goes — systems, vendors, countries, AI providers — and compare it against what the privacy notice and records say. Trigger for "where does our support data go", "map our data flows", "which vendors process our conversations", cross-border transfer review, ROPA accuracy for support, or a new tool that was connected without a review.

Design GraphQL schemas, resolvers, and DataLoader patterns, integrate with Apollo/urql, and implement federation for microservices.

Design GraphQL schemas, resolvers, and DataLoader patterns, and integrate clients with Apollo or urql.

Build declarative UI and manage data flow with SwiftUI in iOS. Use when building declarative SwiftUI views or managing data flow with property wrappers.

Implement local persistence with SwiftData, Core Data, and secure storage. Use when setting up SwiftData models, Core Data stacks, or local persistence in iOS.

Prediction markets data - Polymarket, Kalshi markets, prices, positions, and trades. Use when: the user needs market data, stock analysis, watchlists, or portfolio workflows.

Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets.

Evaluates US market bubble risk through quantitative data analysis using the Minsky/Kindleberger framework, scoring indicators like Put/Call ratio, VIX, margin debt, breadth, and IPO data.

> Use this skill when working with Brain Imaging Data Structure (BIDS) datasets: organizing neuroscience and biomedical data (MRI, EEG, MEG, iEEG, PET, microscopy, NIRS, motion capture, EMG, MR spectroscopy, behavioral), querying BIDS layouts, validating compliance, converting DICOM to BIDS, writing metadata sidecars, or creating BIDS derivatives.

Use this skill when working with Brain Imaging Data Structure (BIDS) datasets: organizing neuroscience and biomedical data (MRI, EEG, MEG, iEEG, PET, microscopy, NIRS, motion capture, EMG, MR spectroscopy, behavioral), querying BIDS layouts, validating compliance, converting DICOM to BIDS, writing metadata sidecars, or creating BIDS derivatives.

Call APIs, load data into components, and handle the async lifecycle in Blazor.

Guides Firebase development with best practices for security rules, data modeling, and avoiding common pitfalls.

Guides Firebase development with best practices for security rules, data modeling, and avoiding common pitfalls.

Compute over read. Run sandboxed scripts to process data instead of dumping it into context. Trigger: 'compute', 'process this data', 'analyze logs', or PreToolUse nudge.

Time-Series Data

Databricks → Fabric migration assessment + execute

Guides AI agents to apply advanced mathematical and probabilistic techniques (Bloom filters, HyperLogLog, FFT, etc.) for large-scale data problems where classical algorithms are optimal but math offers better asymptotic bounds.

Guides compliance with Singapore's Personal Data Protection Act 2012 (PDPA). Covers PDPC advisory guidelines, Do Not Call Registry, data intermediary obligations, deemed consent, notification requirements, and the 2020-2021 amendments. Keywords: Singapore PDPA, PDPC, Do Not Call Registry, deemed consent, data intermediary, advisory guidelines.

Write effective unit tests with TUnit, covering standard tests, data-driven tests, and migration from xUnit.

Use when implementing GDPR compliance, handling data subject requests, conducting DPIAs, managing consent, or responding to data breaches - covers all key GDPR requirementsUse when ", " mentioned.

Guides compliance with Thailand's Personal Data Protection Act B.E. 2562 (2019). Covers consent framework, DPO requirements, PDPC enforcement, lawful bases for processing, cross-border transfer mechanisms, and data subject rights under the PDPA. Keywords: Thailand PDPA, PDPC, consent, DPO, cross-border transfers, data subject rights.

Build automated evaluation pipelines for Python LLM applications using real LLM calls and structured test datasets.

Organize, query, validate, and convert neuroscience and biomedical data using the Brain Imaging Data Structure (BIDS) standard.

Write JavaScript code in n8n Code nodes for data transformation, API calls, and workflow logic.

Read, write, and modify DICOM medical imaging files, including pixel data extraction, anonymization, format conversion, and compression handling.

Write, review, debug, test, and optimize Python Polars code with version-grounded object types, schemas, and execution boundaries.

Use this skill to evaluate which DataQ challenges have winning evidence and which don't. Covers documented vs anecdotal evidence and the 8 high-success patterns.

Release Notes from Commits

Pull data from a web page. (gstack)

Designs and implements data retention schedules compliant with GDPR Article 5(1)(e) storage limitation principle. Maps data categories to retention periods with legal basis justification, regulatory minimum holding periods, and automated review triggers for schedule maintenance. Activate for retention policy, storage limitation, data lifecycle, retention period queries.

Write Python code in n8n Code nodes, covering data access patterns, return formats, and standard library limitations.

Executes Python code iteratively via an MCP bridge to produce verified results for calculations, data analysis, and task decomposition.

Guides writing MariaDB-specific database create scripts with best practices for schema objects, session settings, and UUID usage.