voice-create
Generate custom voice profiles from natural language descriptions by mapping tone, formality, and domain to voice dimensions
Best use case
voice-create is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
It is a strong fit for teams already working in Codex.
Generate custom voice profiles from natural language descriptions by mapping tone, formality, and domain to voice dimensions
Teams using voice-create should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/voice-create/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How voice-create Compares
| Feature / Agent | voice-create | Standard Approach |
|---|---|---|
| Platform Support | Codex | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Generate custom voice profiles from natural language descriptions by mapping tone, formality, and domain to voice dimensions
Which AI agents support this skill?
This skill is designed for Codex.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# voice-create
Generate custom voice profiles from natural language descriptions.
## Triggers
Alternate expressions and non-obvious activations (primary phrases are matched automatically from the skill description):
- "make me sound like [reference]" → reference-based voice creation
- "voice fingerprint" → voice profile extraction from text
## Behavior
When triggered, this skill:
1. **Parses the description** to identify:
- Target audience (developers, executives, general public)
- Tone characteristics (formal/casual, confident/tentative, warm/clinical)
- Domain context (technical, marketing, academic, conversational)
- Any specific constraints or preferences mentioned
2. **Maps description to voice dimensions**:
- Formality (0-1): casual ↔ formal
- Confidence (0-1): hedging ↔ assertive
- Warmth (0-1): clinical ↔ friendly
- Energy (0-1): calm ↔ enthusiastic
- Complexity (0-1): simple ↔ sophisticated
3. **Generates vocabulary guidance**:
- Preferred terms based on domain
- Terms to avoid based on tone
- Signature phrases that match the voice
4. **Creates structure patterns**:
- Sentence length preferences
- Paragraph structure
- Use of lists, examples, analogies
5. **Outputs valid YAML** conforming to voice-profile.schema.json
## Usage Examples
### Technical Documentation Voice
```
User: "Create a voice for API documentation - precise, no-nonsense, assumes developer knowledge"
Output: technical-api-docs.yaml
- formality: 0.6
- confidence: 0.9
- warmth: 0.2
- energy: 0.3
- complexity: 0.8
- vocabulary: technical terms, code references, precise metrics
```
### Friendly Tutorial Voice
```
User: "Make me a voice for beginner tutorials - encouraging, patient, uses lots of analogies"
Output: beginner-tutorial.yaml
- formality: 0.2
- confidence: 0.7
- warmth: 0.9
- energy: 0.7
- complexity: 0.3
- vocabulary: everyday language, encouraging phrases, analogies
```
### Executive Summary Voice
```
User: "Generate a voice profile for board presentations - authoritative but accessible"
Output: board-presentation.yaml
- formality: 0.8
- confidence: 0.9
- warmth: 0.4
- energy: 0.5
- complexity: 0.6
- vocabulary: business metrics, strategic language, clear conclusions
```
## Output Location
Generated profiles are saved to:
1. `.aiwg/voices/{name}.yaml` (project-specific, default)
2. `~/.config/aiwg/voices/{name}.yaml` (user-wide, with --global flag)
## Voice Generation Process
### Step 1: Dimension Calibration
Parse natural language for dimension indicators:
| Description Keywords | Dimension | Value Range |
|---------------------|-----------|-------------|
| casual, relaxed, conversational | formality | 0.1-0.3 |
| professional, business | formality | 0.5-0.7 |
| formal, academic, official | formality | 0.8-1.0 |
| tentative, careful, hedging | confidence | 0.2-0.4 |
| balanced, measured | confidence | 0.5-0.7 |
| assertive, authoritative, direct | confidence | 0.8-1.0 |
| clinical, detached, objective | warmth | 0.1-0.3 |
| neutral, professional | warmth | 0.4-0.6 |
| friendly, warm, personable | warmth | 0.7-0.9 |
| calm, measured, understated | energy | 0.1-0.3 |
| balanced, engaged | energy | 0.4-0.6 |
| enthusiastic, dynamic, energetic | energy | 0.7-0.9 |
| simple, accessible, plain | complexity | 0.1-0.3 |
| clear, moderate | complexity | 0.4-0.6 |
| sophisticated, detailed, nuanced | complexity | 0.7-0.9 |
### Step 2: Domain Detection
Identify domain from context:
- **Technical**: API, code, system, architecture, implementation
- **Marketing**: brand, campaign, audience, engagement, conversion
- **Academic**: research, methodology, analysis, findings, literature
- **Executive**: strategy, ROI, stakeholder, decision, outcome
- **Support**: help, issue, solution, troubleshoot, resolve
### Step 3: Vocabulary Generation
Based on domain and tone, generate:
- 5-10 preferred terms
- 3-5 terms to avoid
- 2-4 signature phrases
### Step 4: Structure Selection
Map tone to structure patterns:
- High formality → longer sentences, structured paragraphs
- Low formality → shorter sentences, varied structure
- High confidence → direct statements, conclusions first
- High warmth → questions, inclusive language ("we", "let's")
## Integration
Works with other voice-framework skills:
- Created voices can be applied via `voice-apply`
- Created voices can be inputs to `voice-blend`
- `voice-analyze` can create base profiles that `voice-create` refines
## References
- Schema: `../../../schemas/voice-profile.schema.json`
- Dimensions guide: `../voice-apply/references/voice-dimensions.md`
- Built-in templates: `../../voices/templates/`Related Skills
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voice-analyze
Reverse-engineer voice profiles from sample content by analyzing writing patterns
soul-to-voice
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soul-create
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Create a W3C PROV-compliant provenance record for an artifact
issue-create
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devkit-create-extension
Create a new AIWG extension (framework expansion pack) with AI-guided setup