Skill UI

Phoenix Evals enables developers to build and run evaluators for AI and LLM applications. It supports both code-based and LLM-based evaluation methods, including RAG systems. Users can create datasets, run experiments, validate evaluator accuracy, and implement production guardrails. Available in Python and TypeScript, it helps ensure model performance and reliability through systematic testing and human validation.

Guidance and local audit tooling for Python workflows using GeoPandas GeoSeries, GeoDataFrame, spatial operations, and vector data I/O, covering reproducible environments, CRS and antimeridian rules, correctness gates, and privacy safeguards.

A comprehensive guide detailing how to securely configure and manage Canva Connect API integrations across development, staging, and production environments. This resource covers environment strategy, best practices for OAuth credential management, secret handling (AWS, GCP, Vault), and implementing environment isolation guards to ensure secure and scalable deployments.

This detailed guide outlines how to configure the Flexport API for isolated development, staging, and production environments. It enforces best practices for data separation using distinct API keys, webhook secrets, and implements critical safety guards to prevent accidental data access or modification across environments.

A powerful pre-action hook designed to enforce rigorous checks before AI agents modify code, create files, or execute destructive commands. Instead of relying on superficial self-evaluation, GateGuard forces the LLM to gather concrete facts (like dependencies, data schemas, and impacted modules), significantly improving code reliability and architectural depth. It provides multiple gates for edits, writes, and destructive bash commands.

This guide details how to implement robust content safety guardrails for applications powered by Claude. It covers essential techniques such as system prompt hardening, input validation to detect injection patterns and length limits, and output validation to prevent system prompt leakage. It ensures compliance with Anthropic's policies, making the AI integration secure and reliable.

Implements automated, multi-layered policy enforcement and guardrails for Adobe integrations. This skill provides crucial security checks, including detecting sensitive credentials (e.g., p8_ secrets), pre-screening content for services like Firefly (blocking trademarks or explicit material), and enforcing usage quotas for PDF Services. It is essential for maintaining secure and compliant CI/CD pipelines.

This tool provides comprehensive AI security scanning using the Tencent AI-Infra-Guard framework. It systematically audits AI infrastructure, scans tools and skills, checks agent security, and performs advanced LLM jailbreak evaluations to identify vulnerabilities and ensure the robustness and reliability of AI systems.

This comprehensive guide details multiple strategies for configuring Algolia across distinct environments (development, staging, production). It covers best practices such as index prefixing, scoped API keys, environment variable management, and implementing isolation guards, ensuring data integrity and reliable search functionality in CI/CD pipelines.

This comprehensive skill provides multi-layered guardrails for integrating with LLMs like Claude. It enforces security and compliance by implementing input validation (PII detection, length checks), system prompt restriction (scope control, safety rules), output filtering (preventing sensitive data leakage), cost governance, and model access policies. Ideal for building secure, robust, and governed AI applications.

Implements automated policy enforcement and guardrails for Canva Connect API integrations. This system enforces best practices such as preventing hardcoded credentials, ensuring proper rate limit handling (429 responses), and validating robust OAuth configurations. It is essential for maintaining high code quality and security in CI/CD pipelines and pre-commit hooks.

This guide provides a comprehensive strategy for configuring ClickUp API access across development, staging, and production environments. It emphasizes the critical importance of environment isolation using separate API tokens and dedicated workspaces to prevent data leaks and accidental destructive operations in live environments. Includes best practices for secret management (Vault, AWS, GCP) and implementing environment guards.