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sage-voice圣人之声

Agent Skill

用于辅助音频、音乐、语音转写、语音合成或声音素材处理。它适合让 Agent 生成配乐说明、整理音频流程、调用语音工具或处理播客和视频配音素材。使用时需要确认输入音频来源、输出格式、时长和模型限制;涉及人声克隆、版权音乐或公开发布时,应先核对授权和合规边界。

总安装

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周安装

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OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:sage-voice(圣人之声)
来源仓库:https://github.com/evanl1/sage-voice
安装命令:
openclaw skills install sage-voice
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

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复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install sage-voice

简介

sage-voice 语音学习写作助手,适应读者风格和个人表达习惯。

  • 它帮助优化文本语气和结构,提升沟通效果。
  • 安装后可在 OpenClaw 中调用,需文本输入权限。
  • 使用前应确认输出格式要求,避免风格偏差。
  • 建议对比样例调整参数以获得最佳匹配。sage-voice 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

name
sage-voice
description
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.
version
0.1.0
metadata
openclaw
emoji
🎙️
homepage
https://github.com/nicholasgasior/sage
tags
depends_on

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

DimensionWhat to detectExample
Preferred wordsPhrases they reuse"bottom line", "ship it", "loop in"
Avoided wordsFormal filler they'd never say"utilize", "leverage", "synergize"
Technical vocabularyDomain terms they use naturallyModbus, ROI, sprint, PRD
Hedging levelHow 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. Rhetorical Patterns

  • Analogy user: Do they explain things with metaphors?
  • Data-first: Do they lead with numbers, or with narrative?
  • List maker: Bullets for clarity, or continuous prose?
  • Structural signposting: "First... then... finally" or just flowing?

5. Emotional Register

How do they express:

EmotionTheir pattern
DisagreementIndirect ("I'd push back on X") vs direct ("No, that's wrong")
UrgencyExplicit ("Need this today") vs implicit (short sentences, no sign-off)
AppreciationBrief ("Good work") vs specific ("The part about X was exactly right")
FrustrationSilence, terseness, or explicit statement?

Storage: Save the style fingerprint as a core memory in sage-cognitive with tag voice_profile. Update whenever the user sends a correction.


Audience Adaptation

The user's voice stays consistent — the *register* adapts to the audience. Same person, different frequency.

AudienceAdaptation Rules
Superior (Shawn / Bob / CTO)Conclusions first. Frame as impact / ROI / strategic signal. Trim everything that doesn't serve the decision. Never show the work unless asked.
Team membersDirection, not prescription. Give the "what" and "why", leave the "how" open. Trust is embedded in the framing.
Cross-department peersTranslate your domain terms into their language. Find shared interest before making asks. Don't assume shared context.
External (clients / partners)Professional, concise, no internal jargon. Represent the company, not just the team. Slightly more formal than internal comms.
Peers in same domainCan use technical shorthand freely. Peer-to-peer tone, less hierarchy signaling.

When uncertain about audience: ask once, then remember. Never ask twice.


Writing Modes

Mode 1: Email Draft

Trigger: "Draft an email to X about Y" or "Help me write to X"

Process:

  1. Identify recipient → select audience register
  2. Identify goal: inform / request / escalate / close
  3. Apply user's voice fingerprint
  4. Structure: [Subject line] → [Opening] → [Core message] → [Ask/Next step]

Rules:

  • Subject lines: specific and scannable, not vague
  • Opening: no "Hope this finds you well". Start with purpose.
  • Closing: match the user's typical sign-off tone
  • Length: as short as the goal allows

Example prompt to invoke:

"Draft an email to Shawn about delaying the Q3 release by 2 weeks due to hardware dependency."

Mode 2: Message Reply

Trigger: "Help me reply to this" + [paste of original message]

Process:

  1. Read the original message: what does it want? inform / decide / vent?
  2. Draft a response that matches the user's register for this sender
  3. Keep it short — this is a message, not a memo

Rules:

  • Match the energy of the original (if they wrote 2 sentences, don't write 8)
  • If it's ambiguous whether to reply at all, say so — silence is sometimes the right answer
  • Preserve any relationship subtext (don't resolve tensions that the user might be intentionally holding)

Mode 3: Document / Report

Trigger: "Write a doc about X" / "Help me structure a report on Y"

Process:

  1. Clarify: who reads this? what decision does it serve?
  2. Choose structure based on audience: exec summary first for leadership; full narrative for technical team
  3. Apply user's writing style throughout — not AI-essay style

Structure template (leadership-facing):

## Summary (3 sentences max)
## Context (why this matters now)
## Options / Recommendation
## Risk / Trade-offs
## Next Steps

Rules:

  • No passive voice in section headers
  • Tables for comparisons, bullets for lists, prose for reasoning
  • Avoid "In conclusion" — end with an action, not a summary of the summary

Mode 4: Team Feedback

Trigger: "Help me give feedback to [name] about X"

Process:

  1. Load team member profile from sage-cognitive (if available)
  2. Apply user's management philosophy: direction-giving, not path-prescribing
  3. Draft feedback that is specific, actionable, and respects the person's autonomy

Structure:

Observation: what you saw (behavior, not judgment)
Impact: why it matters (to the team, project, or person's growth)
Direction: what good looks like (not how to get there)

Rules:

  • Never write "you should" — prefer "the bar here is" or "what I need to see"
  • Positive feedback should be as specific as corrective feedback
  • Match formality to relationship: casual for close reports, structured for formal reviews

Voice Calibration

The style fingerprint is a hypothesis, not a fact. The user corrects it over time.

How to Handle Corrections

When the user says "this isn't me" or "I wouldn't say it like that":

  1. Acknowledge: "Got it — what's off?"
  2. Extract the delta: What's wrong? (word choice / tone / structure / length?)
  3. Rewrite immediately: Show the corrected version, don't explain
  4. Update the fingerprint: Save the correction as a memory update to voice_profile

Correction memory format:

voice_correction: [what was wrong] → [the right approach]
Example: "avoid 'I wanted to reach out' — too soft. Use direct opener instead."

Calibration Loop

Draft → User says "not quite" → Extract correction → Rewrite → User approves → Save

After 5+ corrections in the same dimension (e.g., always shortening sentences), promote this to a strong signal in the style fingerprint.

Proactive Calibration Check

After generating any piece of writing, you may optionally append:

"Anything that doesn't sound like you?"

Do this sparingly — maximum once per session. Don't fish for feedback after every output.


Anti-Patterns

These are failure modes to actively avoid:

Anti-PatternWhy It FailsWhat to Do Instead
Over-polished AI proseSmooth, generic, sounds like everyoneIntroduce the user's actual sentence rhythms and vocabulary
Forced formalityUser is direct; AI makes it stiffMatch the real register, not the "professional" default
Hollow openers"I hope this email finds you well"Start with the point
Excessive hedging"It might potentially be possible that..."Match user's actual confidence level
Forced lightnessCasual tone in a serious escalationRead the stakes. Tone should match the situation.
Mirroring to satireExaggerating the user's style until it feels like a parodyReplicate the tendency, don't amplify it to a caricature
Ignoring correctionsRe-making the same style mistakeSave every correction. Make it permanent.
Offering unsolicited editsUser asked you to write; you rewrote their instructionsDo what was asked. Suggest changes only if directly relevant.

Memory Integration with Sage Cognitive

This skill reads and writes to the sage-cognitive memory system:

WhatMemory TierTag
Style fingerprint (stable)corevoice_profile
Audience-specific registercorevoice_audience_[name]
Voice correctionscorevoice_correction
Recent drafts (for consistency)workingvoice_recent_draft
Evolving patternsarchivevoice_evolution

When sage-cognitive runs its Evening Review, it should include a voice summary:

"Today's writing: [X] pieces, style consistency: [high/needs calibration], new corrections: [n]"

Quickstart

To activate voice learning in a new session:

  1. Load the user's core memory from sage-cognitive
  2. Ask: "Want to share a few examples of your writing so I can match your style?" (once, on first use)
  3. If examples are provided, extract the style fingerprint and save to voice_profile
  4. If no examples, use sage-cognitive personality profile as a starting prior and calibrate from corrections
The best style sample is a real email the user is proud of. Ask for one.

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能力 5

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安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

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可疑

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