Results for “data-integrity”

Upgrade Qdrant version without interrupting application availability and ensuring data integrity.

`task-agent`: use when physical relational schema or database-enforced integrity changes; skip conceptual-model, repository-only, or unchanged relational-storage work.

Use when designing, creating, or modifying database schemas, data structures, or data models. This skill provides standardized data modeling practices ensuring consistency, integrity, and maintainability across all data stores.

Implements immutable backup strategy using restic with S3-compatible storage and object lock for ransomware-resistant data protection, automating backup creation, integrity verification, snapshot retention, and restore testing.

Installs the official MongoDB Node.js driver and verifies package integrity for building MongoDB-backed applications.

Validate multi-skill workflows by checking skill existence, inter-skill data contracts, JSON schema compatibility, file naming conventions, and handoff integrity.

Audit database schema for consistency, validation, and industry standards. Use when reviewing schema design, naming conventions, constraints, indexes, or migrations. Destructive-op gates → plan-data-integrity. Who-can-read-what RLS → plan-rls-audit. Restore/RPO → plan-backup-dr.

Audits local code for production readiness, identifying risks in security, data integrity, payments, operations, and UX before shipping.

Audit a project for destructive-operation and migration safety gaps, then produce a phased safeguard plan. Use when "is my migration safe", "could I lose data", "my agent might delete prod", or "safe schema changes". Restore drills and RPO/RTO belong to plan-backup-dr. Source transforms → audit-codemod-safety.

Limpia datasets crudos eliminando nulos, duplicados, outliers e inconsistencias de tipos, con estrategias de imputación y pipelines reproducibles.

Validate backup integrity through cryptographic hash verification, automated restore testing, corruption detection, and recoverability checks to ensure backups are reliable for disaster recovery and ransomware response scenarios.

Identify sensitive data exposure vulnerabilities including API key leakage, PII in responses, insecure storage, and unprotected data transmission during security assessments.

Enforces GDPR-compliant deployment practices for Google Cloud Run, including Frankfurt region verification, PII-safe logging, data anonymization, retention policies, and CSP configuration.

Validate ransomware recovery plans by testing backup restore operations, measuring RTO/RPO targets, verifying data integrity, and documenting recovery gaps in an isolated lab environment.

Data quality framework — completeness, accuracy, consistency, validation, and contracts. Use when implementing data validation, setting up quality checks for a pipeline, defining data contracts between teams, or investigating data anomalies.

Apply GDPR-compliant engineering practices across your codebase, covering API design, data models, authentication, logging, retention, and cloud infrastructure.

Detects missing zeroization of sensitive data in source code and identifies zeroization removed by compiler optimizations, with assembly-level analysis and control-flow verification. Use for auditing C/C++/Rust code handling secrets, keys, passwords, or other sensitive data.

Enforces output quality, intellectual honesty, and continuous improvement across security analysis agents with anti-rationalization guards, self-critique loops, retry protocols, and quality gates.

Implement the NIST Privacy Framework CONTROL function covering CT.DM data management, CT.DP data processing policies and procedures, and CT.PO disassociated processing. Provides technical control architectures, data management workflows, and de-identification implementation guidance.

GDPR and CCPA/CPRA privacy compliance audit for codebases. Inventories PII fields (email, phone, SSN, IP, device ID, geolocation, biometrics, behavioral data), maps data collection points (forms, APIs, cookies, analytics, error tracking), audits consent mechanisms (cookie banners, opt-in, pre-checked boxes, consent withdrawal), verifies data subject rights implementation (right to access, erasure, rectification, portability, opt-out, Do Not Sell), traces third-party data sharing (Google Analytics, Facebook Pixel, Stripe, SendGrid, Sentry), and checks data retention policies and automated purging. Use when auditing privacy compliance, building data export or deletion endpoints, reviewing cookie consent, or assessing DSAR readiness.

GDPR compliance implementation. Data subject rights (access, deletion, portability), consent management, data processing records, PII handling, and privacy by design patterns. USE WHEN: user mentions "GDPR", "data privacy", "right to be forgotten", "data deletion", "consent management", "PII", "data subject request", "privacy policy", "cookie consent" DO NOT USE FOR: authentication - use auth skills; encryption - use `cryptography`; audit logging - use `audit-logging`

Implement the NIST Privacy Framework PROTECT function covering PR.AC access control, PR.DS data security, and PR.PO protective policies. Provides technical control implementation guidance, encryption standards, access management architectures, and security-privacy integration patterns.

Investigates insider threat incidents involving employees, contractors, or trusted partners who misuse authorized access to steal data, sabotage systems, or violate security policies. Combines digital forensics, user behavior analytics, and HR/legal coordination to build an evidence-based case.

Provides French data protection compliance guidance covering RGPD, Loi Informatique et Libertés, and LCEN, including retention periods, cookie rules, and standard wording templates.

Guides privacy impact assessment for emerging technologies including IoT, blockchain, AR/VR, quantum computing, and digital twins. Covers risk identification methodology, proportionality assessment, and technology-specific privacy challenges. Activate when evaluating new technology adoption, innovation projects, or emerging tech procurement. Keywords: PIA, emerging technology, IoT, blockchain, AR/VR, quantum computing, digital twins, innovation privacy.

Design reliable, scalable, and maintainable data systems by applying principles from storage engines, replication, partitioning, transactions, and consistency models.

Enforces a strict input boundary protocol (detect, classify, filter, verify) to ensure untrusted data never reaches business logic raw.

Persists app data on Android using Room, SQLite, SharedPreferences, DataStore, and encryption.

Validates data quality with completeness, consistency, accuracy checks and generates quality reports

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