Skill UI

Combines model routing, immutable budget tracking, retry policies, and prompt caching so LLM API calls stay on-budget without sacrificing quality, ideal for batch workloads with mixed complexity.

This skill provides comprehensive handling for Lokalise translation data, covering the entire localization lifecycle. It facilitates creating keys with rich metadata, managing versions using immutable snapshots, isolating work with feature branches, and generating various export formats (JSON, XLIFF). It is essential for handling sensitive translation data, ensuring PII compliance, and managing scalable internationalization within development pipelines.

This guide demonstrates how to implement clean and robust asynchronous pipelines in TypeScript using `TaskEither` from `fp-ts`. It solves the common problem of deeply nested, unmanageable `try/catch` blocks, allowing developers to compose sequential API calls and operations while correctly handling success and failure paths in a functional, immutable manner. Ideal for complex data fetching and workflow orchestration.

Implements an immutable backup workflow with Restic, S3-compatible Object Lock, and automated verification to harden ransomware protection and ensure reliable recovery testing.

A comprehensive guide to production-ready patterns for Dart and Flutter applications. Covers core concepts like null safety, immutable state, async composition, state management (BLoC, Riverpod), navigation (GoRouter), and best practices for building robust, scalable, and maintainable UIs.

A governance skill designed to secure AI agent tool calls using advanced authorization policies. It implements Cedar policies to control which tools agents can access and under what conditions. Key features include running in 'Shadow mode' for safe observation, 'Enforce mode' for strict blocking, and generating cryptographically signed Ed25519 receipts to provide an immutable, tamper-evident audit trail for every policy decision.

This skill provides advanced techniques for robust prompt engineering within LangChain applications. It solves common production issues, such as f-string template breakage when user input contains literal curly braces, and guides developers on consolidating scattered prompts. Crucially, it details how to use the LangSmith Prompt Hub to version, pin, and A/B test prompts using immutable commit hashes, ensuring stable, reproducible LLM deployments.