Token导航 LogoToken导航TokenDH.com
研究检索只读github未标认证来源可访问clear审计未展示

essay-to-speech文章到演讲

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

225

周安装

9

GitHub Stars

公开资料未说明

下载量

73
CodexClaudeCursorGemini CLI

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

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:essay-to-speech(文章到演讲)
来源仓库:https://github.com/leegonzales/aiskills
仓库路径:skills/essay-to-speech
安装命令:
npx skills add leegonzales/aiskills --skill "essay-to-speech"
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。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 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。

AgentSkills.tonpx skills
npx skills add leegonzales/aiskills --skill "essay-to-speech"

简介

用于辅助音频、音乐、语音转写、语音合成或声音素材处理。

  • 适合生成配乐说明、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。
  • essay-to-speech 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
essay-to-speech
description
Transform written essays into spoken word presentations while preserving source material. Use when adapting essays for verbal delivery, creating talk tracks, or preparing content for presentation slides.

Essay to Speech

Transform written essays into spoken word presentations. Outputs both the original text and talk track in connected chunks, ready for downstream slide generation.

When to Use

Invoke when user:

  • Wants to turn an essay into a presentation talk track
  • Needs to adapt written content for verbal delivery
  • Is preparing a speech from written material
  • Uses /essay-to-speech command

Transformation Intensity

Default to full transformation unless the essay is already conversational.

ModeWhen to UseApproach
Full (default)Academic, formal, or dense proseAggressive rewrite for natural speech
LightAlready conversational, personal voicePreserve author's voice, minimal changes

Auto-detect: If the essay uses "I", contractions, and short sentences, use light mode. If it uses passive voice, complex clauses, and formal language, use full mode.

Core Process

1. Segment the Essay

Break the essay into atomic chunks based on:

  • Existing structure first: Honor headings, sections, paragraph breaks
  • Argument units: Each chunk = one coherent point or idea
  • Slide-sized thinking: Could this chunk support one slide?

Typical segmentation:

  • Introduction → Opening hook chunk
  • Each major section → 1-3 chunks depending on density
  • Conclusion → Landing chunk

2. Transform Each Chunk

Convert written prose to spoken language:

Written PatternSpoken Pattern
"This essay examines...""Today I want to share..."
"As previously mentioned...""Remember when I said..."
"It is important to note that...""Here's what matters..."
"In conclusion, this paper has demonstrated...""So what does this mean for you?"
Complex nested clausesShorter, punchier sentences
Passive voiceActive voice
Academic hedgingConfident assertions
Dense paragraphsBreathing room, varied rhythm

Statistical Notation → Plain English

Critical: Convert all statistical notation to spoken-friendly language. Nobody says "rho equals negative 0.91" on stage.

Written (Academic)Spoken (Natural)
"ρ = -0.91""almost a perfect inverse—when one goes up, the other goes down"
"β = 0.27, p < 0.001""a significant effect—this isn't random chance"
"95% CI: 0.01-1.29""we can be confident this relationship is real"
"ΔELPD = 50.9 (SE = 10.2)""the statistical evidence was overwhelming"
"coefficient: 0.65""a strong positive relationship"
"r² = 0.73""this explains most of the variation"
"n = 667""nearly 700 people"

Keep the *meaning* of statistics, drop the *notation*. The audience needs to understand the insight, not verify the math.

Rhythm and Breath

Vary sentence length deliberately. Pattern: Long → Short → Medium

Too uniform (written):

The researchers found that Theory of Mind predicted collaboration. The correlation was significant. The effect was strong.

Varied rhythm (spoken):

The researchers found something surprising about what predicts AI collaboration success. Theory of Mind. Not technical skill—a social skill.

Breath points: Insert natural pauses by breaking at:

  • After a key insight (let it land)
  • Before a contrast ("But here's the thing...")
  • After rhetorical questions (let audience think)

3. Assess Images (if present)

For essays containing images, apply a critical eye—not everything works on a slide at 20 feet.

Detection: Identify all images referenced in the essay (inline, figures, diagrams)

Assessment questions (be skeptical):

  • Can the audience read this from the back row?
  • Does this make sense without the surrounding text?
  • Is this a data dump or a clear visual?
  • Would a simpler version communicate better?

Common issues to flag:

Image TypeCommon ProblemLikely Rating
Scatter plotsToo many points, tiny labelsADAPT or RECREATE
TablesText-heavy, not visualRECREATE as chart
ScreenshotsLow resolution, clutteredADAPT (crop) or SKIP
FlowchartsToo many boxes, small textADAPT (simplify)
Bar/pie chartsUsually fine if not too busyUSE or ADAPT
Conceptual diagramsOften goodUSE
Decorative imagesNo information valueSKIP

Ratings:

  • USE - Genuinely presentation-ready (rare for academic figures)
  • ADAPT - Good concept, needs work (most common)
  • RECREATE - Valuable data, wrong format (tables, dense plots)
  • SKIP - Doesn't add value to spoken presentation

Be honest: Rating everything "USE" isn't helpful. Most academic figures need adaptation.

4. Preserve the Connection

Critical: Output BOTH versions for each chunk. The original is never modified.

Output Format

Filename Convention

Generate a logical filename from the essay title:

  • {slugified-title}-presentation.md
  • Drop common stop words (the, a, an, in, of, and, or) for concise filenames
  • Example: "The Future of Remote Work" → future-remote-work-presentation.md

Document Structure

# [Essay Title]: Presentation Version

**Source**: [Original essay title/description]
**Chunks**: [N sections]
**Generated**: [Date]
**Mode**: [Full/Light transformation]

---

## Section 1: [Descriptive Title]

### Original
[Verbatim essay text for this section - unchanged]

### Talk Track
[HOOK] Let me start with a question: what if everything you thought you knew was wrong?

[KEY_POINT] The real issue isn't what people say—it's what they actually do.

[EVIDENCE] A recent study found a 50% gap between reported intentions and actual behavior.

[TRANSITION] Now that we understand the problem, let's look at what we can do about it.

### Images
- `figure1.png`: USE - Clear diagram, good resolution
- `chart2.png`: ADAPT - Needs larger labels for projection

### Slide Ideas
- [Suggested visual, chart type, or slide concept]
- [Alternative approach or supporting visual]

---

## Section 2: [Descriptive Title]

### Original
[Verbatim essay text for this section]

### Talk Track
[Tagged spoken content...]

### Slide Ideas
- [Visual suggestions for this chunk]

---

[Continue for all sections...]

Talk Track Semantic Tags

Use tags to mark key structural moments—not every sentence.

TagPurposeExample
[HOOK]Opening attention-grabber[HOOK] Let me ask you something...
[KEY_POINT]Core argument or insight[KEY_POINT] This changes everything about...
[EVIDENCE]Data, examples, proof[EVIDENCE] Nearly 700 people were tested...
[STORY]Narrative or anecdote[STORY] I met a manager who...
[TRANSITION]Bridge between ideas[TRANSITION] So that's the problem. Now let's talk solutions.
[CALLBACK]Reference to earlier point[CALLBACK] Remember that study I mentioned?
[LANDING]Section or final conclusion[LANDING] And that's why this matters.
[CTA]Call to action[CTA] Starting tomorrow, I want you to...

Tagging guidelines:

  • 2-4 tags per section is typical
  • Don't tag every paragraph—it becomes noise
  • Untagged sentences flow naturally between tagged moments
  • Tags help the slide-builder know where visual anchors belong

Images Section (when applicable)

If the original essay contains images, each chunk includes a ### Images section:

### Images
- `fig1-market-share.png`: USE - Clean pie chart, large labels, works as-is
- `fig2-scatter.png`: ADAPT - Good data but axis labels too small, needs enlargement
- `fig3-table.png`: RECREATE - Important data but tables don't work on slides, convert to horizontal bar chart
- `header-decorative.jpg`: SKIP - Decorative only, no value for presentation

No images? Omit the ### Images section entirely for text-only chunks.

Slide Ideas Section

Each chunk includes a ### Slide Ideas section with 1-3 suggestions:

  • Chart/graph types with specific data to visualize
  • Key quote or stat callouts
  • Comparison frameworks (before/after, A vs B)
  • Visual metaphors or imagery concepts
  • "No slide needed" if the chunk is purely transitional

When original images exist, slide ideas should reference them:

  • "Use fig1-market-share.png as primary visual"
  • "Recreate fig3-table.png as horizontal bar chart showing top 5 only"

These are suggestions, not requirements—the slide-builder skill makes final decisions.

Delivery Markup (Optional)

When user requests annotated output, add delivery cues:

  • [PAUSE] - Breath/emphasis pause (after key insights, before contrasts)
  • *word* - Vocal emphasis
  • [SLOW] / [FASTER] - Pacing shifts
  • [LOOK UP] - Eye contact moment

What This Skill Does NOT Do

  • Design slides or visuals (that's the next skill)
  • Edit or modify images (assesses only, slide-builder handles modifications)
  • Fundamentally rewrite arguments or add new content
  • Coach on public speaking technique
  • Change the essay's core message or stance

Best Practices

  1. Preserve meaning: The talk track conveys the same arguments, just spoken
  2. Respect structure: Don't arbitrarily merge or split the author's sections
  3. Natural chunking: Each section should feel like a complete thought
  4. Opening matters: Transform bland thesis statements into hooks
  5. Landing matters: End with impact, not "in conclusion"
  6. Kill the jargon: Statistical notation, acronyms, and academic language don't belong in speech
  7. Vary the rhythm: Mix sentence lengths—monotony kills engagement
  8. Be a critical friend on images: Most academic figures need work; say so

Example Transformation

Original (written):

The study found ToM strongly predicted collaborative ability with AI (coefficient: 0.65, 95% CI: 0.01-1.29). Translation: higher Theory of Mind scores meant better AI collaboration, and we can be 95% confident this relationship is real—the confidence interval stays positive.

Talk Track (full transformation):

[EVIDENCE] The study found a strong link between Theory of Mind and AI collaboration success. And this isn't a maybe—the statistics are clear. Higher Theory of Mind means better results with AI. [KEY_POINT] We can be confident this relationship is real.

What changed:

  • "coefficient: 0.65, 95% CI: 0.01-1.29" → "a strong link" + "the statistics are clear"
  • "confidence interval stays positive" → "we can be confident this relationship is real"
  • Complex sentence broken into two
  • Same meaning, speakable language

Images

  • correlation-scatter.png: ADAPT - Data points clear but axis labels "ToM Score" and "κ (Collaborative Ability)" need plain English labels

Slide Ideas

  • Single insight slide: "Theory of Mind → Better AI Collaboration"
  • Visual: simplified scatter with trend line, labeled "Social Skill" → "AI Success"

References

  • references/transformation-patterns.md - Detailed written→spoken patterns
  • references/examples.md - Full before/after essay transformations

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

04

需要参考平台分布和安装热度时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

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。

平台分布

Claude Code

27.32%
按下载量换算20

windsurf

21.68%
按下载量换算16

OpenCode

17.61%
按下载量换算13

Codex

13.34%
按下载量换算10

Antigravity

8.25%
按下载量换算6

Gemini CLI

3.43%
按下载量换算3

安全审计

暂无安全审计结果可展示。

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

继续浏览同类 Skills