jmagly/voice-create

voice-create

Generate custom voice profiles from natural language descriptions.

Triggers

Alternate expressions and non-obvious activations (primary phrases are matched automatically from the skill description):

• "make me sound like [reference]" → reference-based voice creation
• "voice fingerprint" → voice profile extraction from text

Behavior

When triggered, this skill:

1. Parses the description to identify:

   • Target audience (developers, executives, general public)
   • Tone characteristics (formal/casual, confident/tentative, warm/clinical)
   • Domain context (technical, marketing, academic, conversational)
   • Any specific constraints or preferences mentioned

2. Maps description to voice dimensions:

   • Formality (0-1): casual ↔ formal
   • Confidence (0-1): hedging ↔ assertive
   • Warmth (0-1): clinical ↔ friendly
   • Energy (0-1): calm ↔ enthusiastic
   • Complexity (0-1): simple ↔ sophisticated

3. Generates vocabulary guidance:

   • Preferred terms based on domain
   • Terms to avoid based on tone
   • Signature phrases that match the voice

4. Creates structure patterns:

   • Sentence length preferences
   • Paragraph structure
   • Use of lists, examples, analogies

5. Outputs valid YAML conforming to voice-profile.schema.json

Usage Examples

Technical Documentation Voice

User: "Create a voice for API documentation - precise, no-nonsense, assumes developer knowledge"

Output: technical-api-docs.yaml
- formality: 0.6
- confidence: 0.9
- warmth: 0.2
- energy: 0.3
- complexity: 0.8
- vocabulary: technical terms, code references, precise metrics

Friendly Tutorial Voice

User: "Make me a voice for beginner tutorials - encouraging, patient, uses lots of analogies"

Output: beginner-tutorial.yaml
- formality: 0.2
- confidence: 0.7
- warmth: 0.9
- energy: 0.7
- complexity: 0.3
- vocabulary: everyday language, encouraging phrases, analogies

Executive Summary Voice

User: "Generate a voice profile for board presentations - authoritative but accessible"

Output: board-presentation.yaml
- formality: 0.8
- confidence: 0.9
- warmth: 0.4
- energy: 0.5
- complexity: 0.6
- vocabulary: business metrics, strategic language, clear conclusions

Output Location

Generated profiles are saved to:

1. .aiwg/voices/{name}.yaml (project-specific, default)
2. ~/.config/aiwg/voices/{name}.yaml (user-wide, with --global flag)

Voice Generation Process

Step 1: Dimension Calibration

Parse natural language for dimension indicators:

| Description Keywords | Dimension | Value Range | |---------------------|-----------|-------------| | casual, relaxed, conversational | formality | 0.1-0.3 | | professional, business | formality | 0.5-0.7 | | formal, academic, official | formality | 0.8-1.0 | | tentative, careful, hedging | confidence | 0.2-0.4 | | balanced, measured | confidence | 0.5-0.7 | | assertive, authoritative, direct | confidence | 0.8-1.0 | | clinical, detached, objective | warmth | 0.1-0.3 | | neutral, professional | warmth | 0.4-0.6 | | friendly, warm, personable | warmth | 0.7-0.9 | | calm, measured, understated | energy | 0.1-0.3 | | balanced, engaged | energy | 0.4-0.6 | | enthusiastic, dynamic, energetic | energy | 0.7-0.9 | | simple, accessible, plain | complexity | 0.1-0.3 | | clear, moderate | complexity | 0.4-0.6 | | sophisticated, detailed, nuanced | complexity | 0.7-0.9 |

Step 2: Domain Detection

Identify domain from context:

• Technical: API, code, system, architecture, implementation
• Marketing: brand, campaign, audience, engagement, conversion
• Academic: research, methodology, analysis, findings, literature
• Executive: strategy, ROI, stakeholder, decision, outcome
• Support: help, issue, solution, troubleshoot, resolve

Step 3: Vocabulary Generation

Based on domain and tone, generate:

• 5-10 preferred terms
• 3-5 terms to avoid
• 2-4 signature phrases

Step 4: Structure Selection

Map tone to structure patterns:

• High formality → longer sentences, structured paragraphs
• Low formality → shorter sentences, varied structure
• High confidence → direct statements, conclusions first
• High warmth → questions, inclusive language ("we", "let's")

Integration

Works with other voice-framework skills:

• Created voices can be applied via voice-apply
• Created voices can be inputs to voice-blend
• voice-analyze can create base profiles that voice-create refines

References

• Schema: ../../../schemas/voice-profile.schema.json
• Dimensions guide: ../voice-apply/references/voice-dimensions.md
• Built-in templates: ../../voices/templates/