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 /avoid guidance

• 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 relevant

Transform 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 formality

Voice 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 profile

Script Reference

voice_loader.py

Load and validate voice profiles:

python scripts/voice_loader.py --profile technical-authority

voice_analyzer.py

Analyze content against voice profile:

python scripts/voice_analyzer.py --content input.md --profile technical-authority

Integration

Works with:

• /voice-apply command for explicit invocation

• /voice-create command 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