voice-blend

jmagly's avatarfrom jmagly

Combine multiple voice profiles with weighted mixing to create hybrid voices. Use when relevant to the task.

51stars🔀4forks📁View on GitHub🕐Updated Jan 4, 2026

When & Why to Use This Skill

The voice-blend skill enables the creation of sophisticated hybrid AI personas by merging multiple voice profiles through weighted interpolation. It allows users to precisely calibrate communication styles by blending dimensions such as formality, confidence, and warmth, while merging vocabulary preferences and structural patterns to ensure consistent, brand-aligned AI interactions.

Use Cases

  • Brand Voice Customization: Combine 'Professional Authority' and 'Friendly Explainer' profiles to create a unique, approachable corporate identity for customer support.
  • Technical Content Tailoring: Blend 'Deep Technical' with 'Casual Conversational' styles to produce developer documentation that is both expert-level and easy to digest.
  • Executive Communication: Mix 'Executive Brief' with 'Empathetic' profiles (e.g., 70/30 ratio) to generate internal leadership updates that maintain authority while remaining personable.
  • Contextual Tone Shifting: Rapidly adjust the tone of an agent for different platforms, such as blending a 'Formal' base voice with a 'Social Media' profile for multi-channel content distribution.
namevoice-blend
descriptionCombine multiple voice profiles with weighted mixing to create hybrid voices. Use when relevant to the task.

voice-blend

Combine multiple voice profiles with weighted mixing to create hybrid voices.

Triggers

  • "blend X and Y voices"
  • "mix technical with friendly"
  • "combine executive and casual"
  • "70% technical, 30% friendly"
  • "merge these voices..."

Behavior

When triggered, this skill:

  1. Loads source voice profiles from:

    • Built-in templates (voices/templates/)
    • Project voices (.aiwg/voices/)
    • User voices (~/.config/aiwg/voices/)
  2. Parses blend specification:

    • Equal blend: "blend X and Y" → 50%/50%
    • Weighted blend: "70% X, 30% Y" → explicit weights
    • Multi-voice: "blend X, Y, and Z" → equal thirds
  3. Interpolates dimensions:

    • Weighted average of tone values (formality, confidence, etc.)
    • Merged vocabulary lists (union of prefer, intersection of avoid)
    • Dominant structure patterns from highest-weighted voice
  4. Generates hybrid profile with clear lineage tracking

Usage Examples

Equal Blend

User: "Blend technical-authority and friendly-explainer"

Result: 50/50 blend
- formality: 0.45 (avg of 0.7 and 0.2)
- confidence: 0.8 (avg of 0.9 and 0.7)
- warmth: 0.55 (avg of 0.3 and 0.8)
- vocabulary: merged from both

Weighted Blend

User: "80% executive-brief, 20% casual-conversational"

Result: Weighted blend
- formality: 0.7 (0.8*0.85 + 0.2*0.15)
- confidence: 0.86 (0.8*0.9 + 0.2*0.6)
- Dominant structure from executive-brief

Multi-Voice Blend

User: "Combine technical-authority, friendly-explainer, and executive-brief"

Result: Equal thirds (33.3% each)
- All dimensions averaged across three profiles
- Vocabulary merged from all three

Blend Algorithm

Dimension Interpolation

For each tone dimension:

blended_value = Σ(weight_i × value_i) / Σ(weight_i)

Vocabulary Merging

  • prefer: Union of all prefer lists, deduplicated
  • avoid: Intersection of all avoid lists (only avoid if ALL sources avoid)
  • signature_phrases: Top N from each source (weighted by blend ratio)

Structure Resolution

Structure settings use the dominant voice (highest weight):

  • sentence_length: from dominant
  • paragraph_length: from dominant
  • use_lists: from dominant
  • use_examples: averaged (rarely=0, when-appropriate=1, frequently=2)

Perspective Handling

  • person: Majority vote (tie goes to second person)
  • voice: Active unless all sources use passive
  • tense: Present unless all sources use past

Output Format

name: technical-friendly-blend
version: 1.0.0
description: Blended voice profile
blend_sources:
  - name: technical-authority
    weight: 0.7
  - name: friendly-explainer
    weight: 0.3
tone:
  formality: 0.55  # interpolated
  confidence: 0.84 # interpolated
  warmth: 0.45     # interpolated
  energy: 0.49     # interpolated
  complexity: 0.65 # interpolated
vocabulary:
  prefer:
    - precise technical terminology  # from technical
    - concrete examples              # from friendly
  avoid:
    - marketing superlatives         # common to both
  signature_phrases:
    - "The system handles..."        # from technical (70%)
    - "Think of it like..."          # from friendly (30%)
structure:
  sentence_length: medium            # from dominant (technical)
  use_examples: frequently           # averaged

Output Location

Blended profiles are saved to:

  1. .aiwg/voices/{name}.yaml (default)
  2. Custom path with --output flag
  3. ~/.config/aiwg/voices/ with --global flag

Integration

  • Input: Takes profiles created by voice-create or built-in templates
  • Output: Creates profiles usable by voice-apply
  • Chain: voice-analyzevoice-blendvoice-apply

CLI Usage

# Equal blend of two voices
python voice_blender.py --voices "technical-authority,friendly-explainer"

# Weighted blend
python voice_blender.py --voices "technical-authority:0.7,friendly-explainer:0.3"

# Custom output name
python voice_blender.py --voices "..." --name my-hybrid-voice

# Output to specific directory
python voice_blender.py --voices "..." --output .aiwg/voices/

# JSON output for inspection
python voice_blender.py --voices "..." --json

Error Handling

  • Profile not found: Lists available profiles and suggests closest match
  • Invalid weights: Normalizes weights to sum to 1.0
  • Incompatible profiles: Warns but proceeds with best-effort blend

References

  • Voice loader: ../voice-apply/scripts/voice_loader.py
  • Schema: ../../../schemas/voice-profile.schema.json
  • Built-in templates: ../../voices/templates/