Skill UI

This guide provides production-grade patterns for using the Oracle Cloud Infrastructure (OCI) Python SDK. It addresses critical issues such as preventing Instance Principal memory leaks by implementing a thread-safe singleton client pattern, configuring explicit connection and read timeouts, and implementing robust exponential backoff retry strategies for transient errors (429, 5xx). Essential for building reliable, long-running OCI services.

This comprehensive skill guide provides deep insight into Oracle Cloud Infrastructure (OCI) IAM policy syntax and best practices. It covers the privilege verb hierarchy (inspect, read, use, manage), the correct structure for policy statements, and common patterns for granting least-privilege access across various compartments. Use this when developing automation scripts, setting up complex access control policies, or performing security audits in OCI.

This skill provides a comprehensive guide for safely upgrading the Oracle Cloud Infrastructure (OCI) Python SDK and Terraform provider. It addresses known breaking changes, schema modifications, and version incompatibility issues common in OCI tools. By implementing version pinning, pre-upgrade testing, and structured rollback procedures, users can ensure a stable and reliable migration process for both code and infrastructure definitions.

This skill guides users on building robust, event-driven pipelines within Oracle Cloud Infrastructure (OCI). It covers setting up OCI Events rules to monitor resource state changes (Compute, Object Storage, Identity). Events can then be routed to Oracle Notification Service (ONS) topics for real-time alerting or directly to OCI Functions (serverless processing) for complex, automated actions.

This skill establishes a secure, two-way synchronization bridge between a local ARIS development directory and an Overleaf project. It allows users to maintain collaborative editing in the Overleaf web UI while enabling local ARIS pipelines to run audits, process fixes, and ensure data integrity. Supports pull (remote to local) and push (local to remote) workflows, managing complex diffs and version control.

This framework implements the OWASP Top 10 standards, specifically tailored for Agentic Applications. It provides a standardized methodology to classify and map diverse security findings—such as prompt injection, tool misuse, and privilege abuse—to ten core risk categories (ASI01-ASI10). It is essential for security teams to systematically assess, prioritize, and remediate vulnerabilities inherent in complex AI agent systems.

This internal agent skill automates the complex process of determining the most likely owner or team for a bug cluster. It follows a strict 6-level precedence model, starting with cached overrides, and sequentially querying sources like Service Owners, Oncall status, CODEOWNERS, recent assignees, and committers. The process includes staleness detection and confidence scoring, providing highly accurate and auditable routing recommendations for internal development triage.

Comprehensive guide for setting up Continuous Integration/Continuous Deployment (CI/CD) pipelines for Palantir Foundry integrations using GitHub Actions. This workflow covers running PySpark unit tests, performing SDK integration tests with mocked APIs, and implementing custom linting rules to enforce Foundry-specific best practices. Use this when ensuring code quality and validating complex business logic changes in an automated build process.

This comprehensive guide provides quick, actionable solutions for the top common Palantir Foundry API and data transformation errors. It covers critical issues such as 401 Unauthorized, 403 Forbidden, ObjectTypeNotFound, and OutOfMemoryError. By offering specific code examples and best practices, this resource helps users efficiently debug and resolve complex issues in their Foundry pipelines.

This guide provides a comprehensive workflow for building robust ETL data pipelines within Palantir Foundry. It teaches how to utilize Python transforms, including the @transform_df decorator, for dataset-to-dataset processing. Key topics covered include multi-input joins, implementing incremental updates for efficiency, low-level file I/O, and configuring Spark resources for heavy computations. Essential for data engineers working with complex data modeling in Foundry.

This workflow provides comprehensive SDK access to the Palantir Foundry Ontology. It enables users to query, filter, link, and mutate Ontology objects, and execute defined business actions. Use this skill for building sophisticated, data-driven applications that require deep interaction with structured enterprise data, including cross-object navigation and advanced server-side aggregation.

This comprehensive bundle collects all necessary diagnostic artifacts for troubleshooting Palantir Foundry issues. It gathers SDK versions, environment variables (with secrets automatically redacted), API connectivity status, and recent application error logs. This structured output is essential for creating detailed, actionable support tickets when reporting persistent platform malfunctions.