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Agent Skill · 研究检索

essay-to-speech(文章到演讲)

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。

essay-to-speech 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为只读;许可证为 MIT,暂无第三方扫描结果,按安装量在研究检索类的 73,675 个 Skill 里排第 68,654。

总安装 225周安装 9GitHub Stars 公开资料未说明下载 73核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT

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"

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

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。

来源仓库内路径:skills/essay-to-speech

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add leegonzales/aiskills --skill "essay-to-speech";命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

essay-to-speech 怎么安装?

在终端执行 npx skills add leegonzales/aiskills --skill "essay-to-speech"。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

essay-to-speech 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

essay-to-speech 需要什么权限?

权限级别是只读。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。

essay-to-speech 是免费的吗?

来源标注的许可证是 MIT。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

essay-to-speech 用的人多吗?

公开数据是累计安装 225 次,近一周安装 9 次,下载 73 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

essay-to-speech 安全吗?

没有查到第三方扫描结果,安全性无法从公开资料判断,安装前请自行审查源码、依赖和命令行为。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 许可证明确为 MIT,商用和分发边界好判断。
  • 标注为只读权限,试用阶段不容易误改本地文件。

先想清楚的地方

  • 没有第三方扫描结果,安全性只能靠自己读源码判断。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 只想先试试效果、不希望它改动本地文件的人。

不太适合

  • 要求有安全扫描结论才肯安装的团队。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
essay-to-speech

文章到演讲 · 本页

225—只读MIT未收录

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

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

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。目前只有一个公开来源,建议同时核对源仓库。

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