Natural conversation with AI through speech, balancing latency against control.
Works with
Choose between speech-to-speech models (lowest latency, less controllable) or pipeline architectures (STT→LLM→TTS for fine-grained control)
Core challenges: latency budgeting across all components, voice activity detection, barge-in handling, and turn-taking to avoid awkward pauses or overlaps
Requires semantic VAD, response length constraints in prompts, and noise handling to achieve natural conversation
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionvoice-agentsExecute the skills CLI command in your project's root directory to begin installation:
Fetches voice-agents from sickn33/antigravity-awesome-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate voice-agents. Access via /voice-agents in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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You are a voice AI architect who has shipped production voice agents handling millions of calls. You understand the physics of latency - every component adds milliseconds, and the sum determines whether conversations feel natural or awkward.
Your core insight: Two architectures exist. Speech-to-speech (S2S) models like OpenAI Realtime API preserve emotion and achieve lowest latency but are less controllable. Pipeline architectures (STT→LLM→TTS) give you control at each step but add latency. Mos
Direct audio-to-audio processing for lowest latency
Separate STT → LLM → TTS for maximum control
Detect when user starts/stops speaking
| Issue | Severity | Solution |
|---|---|---|
| Issue | critical | # Measure and budget latency for each component: |
| Issue | high | # Target jitter metrics: |
| Issue | high | # Use semantic VAD: |
| Issue | high | # Implement barge-in detection: |
| Issue | medium | # Constrain response length in prompts: |
| Issue | medium | # Prompt for spoken format: |
| Issue | medium | # Implement noise handling: |
| Issue | medium | # Mitigate STT errors: |
Works well with: agent-tool-builder, multi-agent-orchestration, llm-architect, backend
This skill is applicable to execute the workflow or actions described in the overview.
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
sickn33/antigravity-awesome-skills
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
Registry listing for voice-agents matched our evaluation — installs cleanly and behaves as described in the markdown.
Solid pick for teams standardizing on skills: voice-agents is focused, and the summary matches what you get after install.
Keeps context tight: voice-agents is the kind of skill you can hand to a new teammate without a long onboarding doc.
We added voice-agents from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Useful defaults in voice-agents — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
voice-agents is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
voice-agents fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
I recommend voice-agents for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Solid pick for teams standardizing on skills: voice-agents is focused, and the summary matches what you get after install.
voice-agents has been reliable in day-to-day use. Documentation quality is above average for community skills.
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