Aiwg voice-apply

Applies a voice profile to transform content. Use when user asks to write in a specific voice, match a tone, apply a style, or transform content to sound like a particular voice profile.

install

source · Clone the upstream repo

git clone https://github.com/jmagly/aiwg

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/jmagly/aiwg "$T" && mkdir -p ~/.claude/skills && cp -r "$T/.agents/skills/voice-apply" ~/.claude/skills/jmagly-aiwg-voice-apply && rm -rf "$T"

manifest: .agents/skills/voice-apply/SKILL.md

source content

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

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