Skill UI

This guide provides detailed instructions for optimizing cost and implementing voice-to-text API integrations using Wispr Flow. It covers best practices for WebSocket streaming, REST API usage, context awareness (like programming code), and robust error handling, helping developers build advanced voice-powered applications.

This technical guide provides comprehensive integration details for the Wispr Flow voice-to-text API. It equips developers with the knowledge to build sophisticated, voice-powered applications. Learn how to utilize WebSocket streaming for low latency or REST endpoints for simple transcription, ensuring accurate context awareness for code and development jargon across multiple platforms.

Provides comprehensive guidance for optimizing the performance of the Wispr Flow voice-to-text API. This skill covers best practices for integrating dictation, streaming, and building robust voice-powered applications. It details optimal WebSocket usage, recommended audio formats (16kHz PCM), context setting for specialized fields like programming, and robust error handling strategies (e.g., 401 Unauthorized, network disconnects).

This comprehensive guide provides developers with best practices and detailed instructions for integrating the Wispr Flow voice-to-text API. It covers critical topics such as managing rate limits, choosing between WebSocket streaming and REST endpoints, handling common error codes (e.g., 401 Unauthorized), and optimizing context awareness, particularly for programming code and developer jargon. Essential for building reliable, low-latency voice-powered applications.

This guide provides the reference architecture and best practices for integrating Wispr Flow's voice-to-text API. It details how to utilize both WebSocket (for low-latency streaming) and REST APIs, covering essential concepts like audio format requirements, context awareness (especially for programming code), and comprehensive error handling strategies.

A comprehensive guide for migrating and integrating with the Wispr Flow voice-to-text API. This skill details best practices for both WebSocket (low-latency streaming) and REST API methods. It covers authentication, optimal audio formatting (16kHz mono PCM), context awareness for specialized jargon (e.g., code), and robust error handling across multiple platforms (Mac, iOS, browser).

Use this skill to prompt the agent to zoom out from a specific code section, providing a broader, higher-level perspective. It helps map out the overall system architecture, identifying relevant modules, calling relationships, and how the current code snippet fits into the larger picture. Ideal when you are unfamiliar with a specific area or need system-level understanding.

This guide outlines a sophisticated development workflow using dedicated subagents for each independent task within a plan. It mandates a two-stage review process—first for specification compliance, then for code quality—ensuring highly reliable, high-quality, and rapidly iterative development cycles while maintaining strict context isolation.

A comprehensive guide for simulating a full Software Development Life Cycle (SDLC) using specialized AI roles. It defines protocols for managing cross-functional teams (Dev, QA, DevOps, Product), covering sprint planning, codebase management, bug tracking, and deployment, ensuring context survival across multiple chat sessions.

Cavecrew provides a set of specialized subagents (investigator, builder, reviewer) designed for efficient code development delegation. Unlike vanilla agents, Cavecrew compresses output results, drastically reducing the token usage injected back into the main context. This allows users to maintain context over much longer, complex coding sessions, effectively preventing context exhaustion during large-scale refactoring, debugging, or multi-step feature development.

A deep-dive session designed to stress-test a technical plan against an existing domain model. This process forces the user to resolve ambiguities, sharpen terminology, and validate decisions by cross-referencing code and existing documentation (CONTEXT.md, ADRs). It ensures architectural rigor and completeness before implementation.

This skill implements advanced 'Gatekeeper' logic to optimize AI agent performance in long-running workflows. It prevents context window bloat by sharding metadata, allocating token budgets, and enforcing Atomic Precision to deliver purely functional, filler-free responses. Ideal for complex coding, multi-step reasoning, and large document analysis where conciseness is critical.