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smithery/sickn33

voice-ai-engine-development

Build real-time conversational AI voice engines using async worker pipelines, streaming transcription, LLM agents, and TTS synthesis with interrupt handling and multi-provider support

Installation

$ npx skills add smithery/sickn33 --skill voice-ai-engine-development
View on skills.sh ↗ Website ↗

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Also in this package

Other skills from smithery/sickn33 · top by installs.

npx skills add smithery/sickn33
zoom-automation 0 youtube-summarizer 0 x-article-publisher-skill 0 workflow-patterns 0 workflow-orchestration-patterns 0 webflow-automation 0 wcag-audit-patterns 0 vexor 0 vercel-deployment 0 vector-database-engineer 0

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Not declared
Cursor Not declared
Codex Not declared
GitHub Copilot Not declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 1,410 B
  • docs SUMMARY.md 218 B

History

  1. First recorded snapshot · 0 installs

SKILL.md

Voice AI Engine Development

Detailed Guide

Read [the detailed guide](references/detailed-guide.md) before executing this skill. It retains the complete procedure and reference material. Treat its safety, prerequisites, and validation requirements as mandatory. For focused work, load the relevant sections; for end-to-end work, read the guide completely.

When to Use This Skill

Use this skill when:

  • Building real-time voice conversation systems
  • Implementing voice assistants or chatbots
  • Creating voice-enabled customer service agents
  • Developing voice AI applications with interrupt capabilities
  • Integrating multiple transcription, LLM, or TTS providers
  • Working with streaming audio processing pipelines
  • The user mentions Vocode, voice engines, or conversational AI

Limitations

  • Use this skill only when the task clearly matches the scope described above.
  • Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
  • Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.