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From voice
Combines multiple voice profiles with weighted mixing to create hybrid voices, interpolating tone dimensions like formality and confidence, and merging vocabularies.
npx claudepluginhub jmagly/aiwg --plugin voiceHow this skill is triggered — by the user, by Claude, or both
Slash command
/voice:voice-blendThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Combine multiple voice profiles with weighted mixing to create hybrid voices.
Generates custom YAML voice profiles from natural language descriptions of audience, tone, domain, and preferences. Maps to dimensions like formality, confidence, warmth; includes vocabulary and structure guidance.
Generates personalized AI writer skill by extracting linguistic fingerprint from interactive writing samples, style preferences, and pattern rejection questionnaire (~15 min).
Generate audio content — text-to-speech, podcasts, voice cloning, sound effects, speech-to-speech, dubbing, and audio isolation. Currently powered by ElevenLabs. Works with both the Python SDK and the ElevenLabs CLI. Includes ready-to-run generator scripts that Claude writes to a temp file and executes directly. Triggers: audio, elevenlabs, text-to-speech, TTS, podcast, voice, voiceover, narration, voice clone, sound effects, dubbing, speech-to-speech, audio isolation.
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Combine multiple voice profiles with weighted mixing to create hybrid voices.
When triggered, this skill:
Loads source voice profiles from:
voices/templates/).aiwg/voices/)~/.config/aiwg/voices/)Parses blend specification:
Interpolates dimensions:
Generates hybrid profile with clear lineage tracking
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
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
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
For each tone dimension:
blended_value = Σ(weight_i × value_i) / Σ(weight_i)
Structure settings use the dominant voice (highest weight):
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
Blended profiles are saved to:
.aiwg/voices/{name}.yaml (default)--output flag~/.config/aiwg/voices/ with --global flagvoice-create or built-in templatesvoice-applyvoice-analyze → voice-blend → voice-apply# 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
../voice-apply/scripts/voice_loader.py../../../schemas/voice-profile.schema.json../../voices/templates/