Results for “structured-iteration”

Executes a predefined implementation plan step by step, updating progress inline and running build or test commands.

Extract structured data from LLM responses with Pydantic validation, automatic retries, and streaming support across multiple providers.

Extract structured data from LLM responses with Pydantic validation, retry failed extractions automatically, parse complex JSON with type safety, and stream partial results with Instructor - battle-tested structured output library

Push the LLM to reconsider, refine, and improve its recent output. Use when user asks for deeper critique or mentions a known deeper critique method, e.g. socratic, first principles, pre-mortem, red team.

Redesign a blocked topic sequence into an interleaved plan with mixed practice across related topics. Use when planning units, homework schedules, or revision programmes.

Design a spaced retrieval schedule for any topic list and timeline. Use when planning units, term sequences, or revision programmes.

Structured producer who turns research insights into production-ready slide deck outlines and scripts. Triggers: 'create slides', 'write webinar script', 'build slide deck', 'webinar outline', 'produce webinar', or when user asks for the Producer.

Select and configure a structured discussion protocol matched to the purpose, topic, and group readiness. Use when planning classroom discussions, Socratic seminars, or structured debate.

Extract structured data from LLM responses with Pydantic validation, retry failed extractions automatically, parse complex JSON with type safety, and stream partial results with Instructor - battle-tested structured output library

A library of reusable, production-tested prompt engineering patterns for building AI-powered features. Use when designing system prompts for apps, building AI pipelines, selecting the right prompting technique for a use case, or reviewing prompts for common failure modes. Complements the prompt-engineering skill (which covers the optimization framework); this skill covers the pattern library itself.

Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production. Use when optimizing prompts, improving LLM outputs, or designing production prompt templates.

Push the LLM to reconsider, refine, and improve its recent output. Use when user asks for deeper critique or mentions a known deeper critique method, e.g. socratic, first principles, pre-mortem, red team.