Skill UI

Instructor lets you shape LLM responses into validated Pydantic models, automatically retrying failed extractions, parsing complex JSON with type safety, and streaming partial outputs across Anthropic, OpenAI, and other providers.

Diagnose Kling AI video generation failures by analyzing HTTP/API errors, mapping codes to causes, and applying fixes such as auth correction, parameter validation, retry/backoff, or prompt simplification before retesting.

Provides a quick reference for diagnosing and fixing common Mistral AI errors, including authentication issues, rate limits, invalid parameters, payload limits, service outages, and timeouts, with concrete logging checks, retry logic, and remediation code snippets.

Provides Retell AI SDK coding patterns for TypeScript and Python, including singleton clients, error wrapping, retry/backoff, validation, and logging so teams can implement reliable, idiomatic integrations quickly.

Teaches how to steer OpenAI, Anthropic, Google, and local LLMs into emitting validated JSON/enums by defining strict schemas, prompting for data extraction, handling validation failures, and building retry/logging pipelines.

Intelligent reliability wrapper that monitors agent tool calls, retries failures, heals malformed responses, and checkpoint-resumes interrupted chains so your workflows recover automatically without hand-coded retry logic.