smithery/jmagly

voice-apply

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

Installation

$ npx skills add smithery/jmagly --skill voice-apply

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Skill metadata

Parsed from SKILL.md frontmatter.

Version1.0.0

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 6,057 B
  • docs SUMMARY.md 205 B

History

  1. First recorded snapshot · 0 installs

SKILL.md

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