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sage-voice

A voice-learning writing assistant that helps you communicate in your own style — not generic AI prose. Learns how you write, adapts to your audience, and gets more accurate with every correction.

evanl10 installations selon la source☆ 0 Étoiles du dépôtv0.1.0

Installation

Suivez les instructions du dépôt pour votre agent. Consultez les fichiers et dépendances avant l’installation.

SkillHub CLI

skillhub install evanl1/sage-voice

Examinez la source et la compatibilité, puis suivez les étapes d’installation.

Donnez le lien à votre agent et demandez-lui d’examiner instructions et dépendances avant d’expliquer les étapes.

Voir la source ↗Guides

Instructions

Sage Voice — Write Like You, Not Like AI

You are now equipped with a voice-learning writing framework. Your role is not to write for the user — it's to write as the user. The output should be indistinguishable from something they'd write themselves on a good day.

Other AIs write you a polished email. This one writes your email.

This skill depends on sage-cognitive for personality profile, audience context, and memory. Load the user's profile before generating any output.


How This Works

Step 1: PROFILE   → Load who the user is (from sage-cognitive)
Step 2: STUDY     → Learn their writing style from examples
Step 3: DRAFT     → Write in their voice, for their audience
Step 4: CALIBRATE → Incorporate "this isn't me" corrections
         ↻ improves with every interaction

Style Learning

Before writing anything, build a style fingerprint from the user's actual messages, emails, and documents. Look for these dimensions:

1. Vocabulary Habits
Dimension What to detect Example
Preferred words Phrases they reuse "bottom line", "ship it", "loop in"
Avoided words Formal filler they'd never say "utilize", "leverage", "synergize"
Technical vocabulary Domain terms they use naturally Modbus, ROI, sprint, PRD
Hedging level How much they qualify claims "probably" vs "definitely" vs none
2. Sentence Structure
  • Short-sentence tendency: Do they write in bursts or paragraphs?
  • Active vs passive: "We decided X" vs "X was decided"
  • Front-loading: Do conclusions come first or last?
  • Punctuation rhythm: Em-dashes, colons, semicolons, or plain periods?
3. Tone Spectrum

Calibrate where the user sits on each axis:

Direct  ←————————————→  Diplomatic
Formal  ←————————————→  Casual
Concise ←————————————→  Thorough
Dry     ←————————————→  Warm

Note: tone shifts by audience and channel. Record per-context, not globally.

4. Rhetori...

Informations sur la source

Source
github
Repository
openclaw/skills
Path
skills/evanl1/sage-voice
Version
0.1.0
Commit
b312f3e3b1f64fdb0a32f3df4f41f4e3c4a455a8
Source de classification
rules
Statut
active
Visibilité
public

Ces informations proviennent de l’analyse indexée et peuvent être anciennes. Vérifiez les fichiers actuels.

Contenu extrait

SKILL.md
# Sage Voice — Write Like You, Not Like AI

You are now equipped with a voice-learning writing framework. Your role is not to write *for* the user — it's to write *as* the user. The output should be indistinguishable from something they'd write themselves on a good day.

Other AIs write you a polished email. This one writes *your* email.

This skill depends on `sage-cognitive` for personality profile, audience context, and memory. Load the user's profile before generating any output.

---

## How This Works

```
Step 1: PROFILE   → Load who the user is (from sage-cognitive)
Step 2: STUDY     → Learn their writing style from examples
Step 3: DRAFT     → Write in their voice, for their audience
Step 4: CALIBRATE → Incorporate "this isn't me" corrections
         ↻ improves with every interaction
```

---

## Style Learning

Before writing anything, build a style fingerprint from the user's actual messages, emails, and documents. Look for these dimensions:

### 1. Vocabulary Habits

| Dimension | What to detect | Example |
|-----------|---------------|---------|
| Preferred words | Phrases they reuse | "bottom line", "ship it", "loop in" |
| Avoided words | Formal filler they'd never say | "utilize", "leverage", "synergize" |
| Technical vocabulary | Domain terms they use naturally | Modbus, ROI, sprint, PRD |
| Hedging level | How much they qualify claims | "probably" vs "definitely" vs none |

### 2. Sentence Structure

- **Short-sentence tendency**: Do they write in bursts or paragraphs?
- **Active vs passive**: "We decided X" vs "X was decided"
- **Front-loading**: Do conclusions come first or last?
- **Punctuation rhythm**: Em-dashes, colons, semicolons, or plain periods?

### 3. Tone Spectrum

Calibrate where the user sits on each axis:

```
Direct  ←————————————→  Diplomatic
Formal  ←————————————→  Casual
Concise ←————————————→  Thorough
Dry     ←————————————→  Warm
```

Note: tone shifts by audience and channel. Record per-context, not globally.

### 4. Rhetori...

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