Skill UI

This guide provides essential security best practices for implementing Langfuse, an LLM observability platform. It covers critical areas such as secure credential management (public vs. secret keys), implementing PII scrubbing techniques before tracing, hardening self-hosted environments, and enforcing the principle of least privilege. Use this guide to ensure your LLM integrations meet strict data privacy standards (e.g., GDPR, HIPAA).

Configure GDPR-compliant data handling, PII scrubbing, and data retention policies within Sentry. This skill provides detailed instructions on implementing client-side `beforeSend` hooks and server-side scrubbing rules to securely redact sensitive data (passwords, emails, IP addresses, CC numbers). Essential for achieving compliance with regulations like GDPR, HIPAA, and meeting SOC 2 audit controls.

Provides a comprehensive guide and code examples for configuring Sentry across development, staging, and production environments. It ensures data isolation by using separate DSNs, allows tuning of sample rates (e.g., 10% for production, 100% for dev), and implements environment-specific logic for PII scrubbing and event filtering, optimizing visibility while maintaining data security.

This comprehensive guide details how to harden Sentry's security posture and ensure compliance with regulations like GDPR. It covers best practices for managing sensitive data, implementing PII scrubbing, and configuring `beforeSend` hooks to automatically redact sensitive information such as passwords, API keys, tokens, IP addresses, and emails from client-side and server-side events.

This skill provides robust mechanisms to ensure data privacy when interacting with Cohere APIs (Chat, Embed, etc.). It implements advanced PII detection (emails, SSNs, IP addresses, etc.), performs pre-call redaction to mask sensitive information, and includes post-call scrubbing and detailed audit logging. Essential for achieving GDPR/CCPA compliance and safely handling sensitive user data.

A comprehensive tool for importing and validating leads into a CRM. It performs advanced data scrubbing, deduplication, and mandatory compliance checks (GDPR, CCPA). Leads are automatically scored and qualified (MQL/SAL), ensuring that only high-quality, clean data reaches the sales team, while low-scoring leads are automatically routed into nurture sequences. Supports CSV, JSON, and manual sources.

This guide details the best practices for preparing and submitting supplementary materials for CSCW submissions. It clarifies the difference between main papers, appendices, and separate files, emphasizing that anything needed to *trust* the method belongs here. Crucially, it provides deep guidelines on data anonymization, metadata scrubbing, and version control for review rounds.

A comprehensive audit checklist for preparing research submissions for FSE. This guide covers critical steps including meeting two-stage deadlines (registration and submission), adhering to strict ACM formatting and page budgets, ensuring heavy double-anonymous anonymity by scrubbing all metadata leaks, and fulfilling open science requirements with Data Availability statements. Essential for authors ensuring their manuscript meets high academic standards.

Provides comprehensive guidelines for packaging research artifacts (code, data, logs) for submission to data mining conferences like ICDM. The focus is on ensuring the artifact is completely anonymous for triple-blind review (scrubbing Git history, paths, and internal names) and maximizing reproducibility, allowing reviewers to verify results with minimal effort.

This skill helps secure Large Language Model (LLM) and AI deployments against various threats including prompt injection, data exfiltration, model theft, and supply chain attacks. It covers implementing guardrails, output filtering, PII scrubbing, and API security measures to ensure compliance with standards like SOC2 or ISO 27001.