Voice Apply Skill
Purpose
Transform content to match a specified voice profile. This skill loads voice profiles and applies their characteristics (tone, vocabulary, structure, perspective) to new or existing content.
When This Skill Applies
- User asks to "write in X voice" or "use Y tone"
- User wants to "make this sound more [casual/formal/technical/etc.]"
- User provides content and asks to transform its style
- User references a voice profile by name
- User wants content to match a specific audience or context
Trigger Phrases
| Natural Language | Action |
|---|---|
| "Write this in technical voice" | Apply technical-authority profile |
| "Make it more casual" | Apply casual-conversational or calibrate toward casual |
| "This needs to sound executive" | Apply executive-brief profile |
| "Explain like I'm a beginner" | Apply friendly-explainer profile |
| "Use the [profile-name] voice" | Load and apply named profile |
| "Transform this to match [example]" | Analyze example, apply derived voice |
Voice Profile Locations
Skill checks these locations (in order):
- Project:
.aiwg/voices/ - User:
~/.config/aiwg/voices/ - Built-in:
voice-framework/voices/templates/
Built-in Voice Profiles
| Profile | Description | Best For |
|---|---|---|
technical-authority | Direct, precise, confident | Docs, architecture, engineering |
friendly-explainer | Approachable, encouraging | Tutorials, onboarding, education |
executive-brief | Concise, outcome-focused | Business cases, stakeholder comms |
casual-conversational | Relaxed, personal | Blog posts, social, newsletters |
Application Process
1. Load Voice Profile
# Load from YAML
profile = load_voice_profile("technical-authority")2. Analyze Source Content (if transforming)
- Current tone characteristics
- Vocabulary patterns
- Structure patterns
- Gap analysis vs target voice
3. Apply Voice Characteristics
Tone Calibration:
- Adjust formality level (word choice, contractions)
- Calibrate confidence (hedging vs assertion)
- Set warmth (clinical vs personable)
- Tune energy (measured vs enthusiastic)
Vocabulary Transformation:
- Replace words per
prefer/avoidguidance - Introduce domain terminology naturally
- Weave in signature phrases where appropriate
Structure Adjustment:
- Modify sentence length distribution
- Adjust paragraph breaks
- Add/remove lists, examples, analogies as specified
Perspective Shift:
- Adjust narrative person (I, we, you, they)
- Calibrate opinion expression
- Set reader relationship tone
4. Verify Authenticity Markers
Ensure output includes profile's authenticity characteristics:
- Acknowledges uncertainty (if specified)
- Shows tradeoffs (if specified)
- Uses specific numbers (if specified)
- References constraints (if specified)
Usage Examples
Apply Named Voice
User: "Write release notes in technical-authority voice"
Process:
1. Load technical-authority.yaml
2. Generate release notes with:
- Precise technical terminology
- Specific version numbers
- Direct, confident statements
- Tradeoff acknowledgments where relevantTransform Existing Content
User: "Make this documentation more friendly for beginners"
Input: "The API endpoint accepts a JSON payload containing the requisite parameters..."
Process:
1. Load friendly-explainer.yaml
2. Analyze: formal, technical, passive
3. Transform to: casual, accessible, active
Output: "To use this endpoint, send it some JSON with the info it needs..."Calibrate Voice
User: "This is too formal, dial it back 30%"
Process:
1. Identify current formality (~0.8)
2. Calculate target (0.8 - 0.3 = 0.5)
3. Adjust vocabulary and structure for medium formalityVoice Blending
Combine multiple profiles:
User: "Write this with 70% technical-authority and 30% friendly-explainer"
Process:
1. Load both profiles
2. Weighted merge:
- tone.formality: 0.7 * 0.7 + 0.3 * 0.3 = 0.58
- tone.warmth: 0.7 * 0.3 + 0.3 * 0.8 = 0.45
- etc.
3. Apply merged profileScript Reference
voice_loader.py
Load and validate voice profiles:
python scripts/voice_loader.py --profile technical-authorityvoice_analyzer.py
Analyze content against voice profile:
python scripts/voice_analyzer.py --content input.md --profile technical-authorityIntegration
Works with:
/voice-applycommand for explicit invocation/voice-createcommand for generating new profiles- SDLC templates (apply appropriate voice per artifact type)
- Marketing templates (brand voice consistency)
Output Format
When reporting voice application:
Voice Applied: technical-authority
Transformations:
- Formality: 0.4 → 0.7 (increased)
- Confidence: 0.5 → 0.9 (increased)
- Vocabulary: 12 replacements
- Structure: Added 2 examples, removed 1 rhetorical question
Authenticity Check:
✓ Acknowledges tradeoffs
✓ Uses specific numbers
✓ References constraints