jmagly/voice-apply

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):

1. Project: .aiwg/voices/
2. User: ~/.config/aiwg/voices/
3. 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

References

• @$AIWG_ROOT/agentic/code/addons/voice-framework/README.md — Voice framework addon overview and profile documentation
• @$AIWG_ROOT/agentic/code/addons/voice-framework/voices/templates/ — Built-in voice profile templates
• @$AIWG_ROOT/agentic/code/addons/writing-quality/README.md — Writing quality addon for authenticity enforcement
• @$AIWG_ROOT/docs/cli-reference.md — CLI reference for voice commands
• @$AIWG_ROOT/agentic/code/addons/aiwg-utils/rules/instruction-comprehension.md — Parsing voice and style directives accurately