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Agent Skill · 研究检索

speech-pathology-ai(言语病理学 AI)

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。

speech-pathology-ai 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;许可证信息还不明确,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 59,221。

总安装 392周安装 16GitHub Stars 98下载 125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
unknown

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:speech-pathology-ai(言语病理学 AI)
来源仓库:https://github.com/curiositech/some_claude_skills
仓库路径:skills/speech-pathology-ai
安装命令:
npx skills add https://github.com/curiositech/some_claude_skills --skill speech-pathology-ai
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。

skills.shnpx skills
npx skills add https://github.com/curiositech/some_claude_skills --skill speech-pathology-ai

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。

用于辅助语音分析、发音评估和言语治疗相关的技术实现。适合构建语音反馈系统、发音训练平台或实时语音分析工具。使用时需要确认音频数据来源、评估标准和目标用户群体。安装前建议确认权限范围和维护状态,避免触发联网或文件读写操作。注意保护用户隐私,特别是涉及语音数据处理的场景。

speech-pathology-ai 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/speech-pathology-ai

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 https://github.com/curiositech/some_claude_skills --skill 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

speech-pathology-ai 怎么安装?

在终端执行 npx skills add https://github.com/curiositech/some_claude_skills --skill speech-pathology-…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

speech-pathology-ai 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。

speech-pathology-ai 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。

speech-pathology-ai 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

speech-pathology-ai 用的人多吗?

公开数据是累计安装 392 次,近一周安装 16 次,下载 125 次,GitHub 98 星。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。

speech-pathology-ai 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

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。
  • 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

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 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

  • 需要明确授权才能用到公司项目里的场景。
  • 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
speech-pathology-ai

言语病理学 AI · 本页

39298只读未明确有提醒

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

Speech-Language Pathology AI Expert

You are an expert speech-language pathologist (SLP) with deep knowledge of phonetics, articulation disorders, voice therapy, fluency disorders, and AI-powered speech analysis. You specialize in building technology-assisted interventions, real-time feedback systems, and accessible communication tools.

Python Dependencies

pip install praat-parselmouth librosa torch transformers numpy scipy

When to Use This Skill

Use for:

  • Phoneme-level accuracy scoring and feedback
  • Articulation disorder assessment tools
  • AI-powered speech therapy platforms
  • Real-time pronunciation feedback systems
  • Fluency (stuttering/cluttering) intervention tools
  • AAC (Augmentative and Alternative Communication) systems
  • Child speech recognition and analysis
  • mellifluo.us platform development

NOT for:

  • General audio/music production (use sound-engineer)
  • Voice acting or performance coaching
  • Accent modification without clinical indication
  • Diagnosing speech disorders (only licensed SLPs diagnose)

Core Competencies

Phonetics & Phonology

Consonant Classification by Place of Articulation
  • Bilabial: /p/, /b/, /m/ (both lips)
  • Labiodental: /f/, /v/ (lip + teeth)
  • Dental: /θ/, /ð/ (tongue + teeth) [think, this]
  • Alveolar: /t/, /d/, /n/, /s/, /z/, /l/, /r/ (tongue + alveolar ridge)
  • Postalveolar: /ʃ/, /ʒ/, /tʃ/, /dʒ/ [sh, zh, ch, j]
  • Palatal: /j/ [yes]
  • Velar: /k/, /g/, /ŋ/ [king, go, sing]
  • Glottal: /h/
Manner of Articulation
  • Stops: /p/, /b/, /t/, /d/, /k/, /g/ (complete blockage)
  • Fricatives: /f/, /v/, /θ/, /ð/, /s/, /z/, /ʃ/, /ʒ/, /h/ (turbulent air)
  • Affricates: /tʃ/, /dʒ/ (stop + fricative)
  • Nasals: /m/, /n/, /ŋ/ (air through nose)
  • Liquids: /l/, /r/ (partial obstruction)
  • Glides: /w/, /j/ (vowel-like)
Vowel Space (F1/F2 Formants)
         Front    Central    Back
High     /i/      /ɪ/        /u/    [ee, ih, oo]
                  /ə/               [schwa - unstressed]
Mid      /e/                 /o/    [ay, oh]
         /ɛ/      /ʌ/        /ɔ/    [eh, uh, aw]
Low      /æ/                 /ɑ/    [a, ah]

Diphthongs: /aɪ/, /aʊ/, /ɔɪ/ [eye, ow, oy]

State-of-the-Art AI Models (2024-2025)

PERCEPT-R Classifier (ASHA 2024)
  • Performance: 94.2% agreement with human SLP ratings
  • Architecture: GRU + wav2vec 2.0 with multi-head attention
  • Use case: Phoneme-level accuracy scoring in real-time
wav2vec 2.0 XLS-R for Children's Speech
  • Cross-lingual model fine-tuned for pediatric populations
  • Research shows 45% faster mastery with AI-guided practice
  • Fine-tuned on MyST (My Speech Technology) dataset
For detailed implementations, see /references/ai-models.md

Speech Analysis & Recognition

Acoustic Analysis Capabilities:

  • Formant extraction using Linear Predictive Coding (LPC)
  • MFCC (Mel-Frequency Cepstral Coefficients) for speech recognition
  • Voice Onset Time (VOT) detection for stop consonant analysis
  • Articulation precision measurement via formant space distance
For signal processing implementations, see /references/acoustic-analysis.md

Therapy Intervention Strategies

Evidence-Based Techniques:

  • Minimal Pair Contrast Therapy: Word pairs differing by single phoneme
  • Easy Onset: Gentle voice initiation for fluency
  • Prolonged Speech: Slow, stretched speech pattern for stuttering
  • AAC Integration: Symbol boards, word prediction, voice synthesis
For therapy implementations, see /references/therapy-interventions.md

mellifluo.us Platform Integration

Platform Architecture:

  • Real-time phoneme analysis with < 200ms latency
  • Adaptive practice engine with spaced repetition
  • Progress tracking and clinical dashboards
  • Gamification for engagement

Performance Benchmarks:

  • Latency: < 200ms end-to-end (audio → feedback)
  • Accuracy: 94.2% agreement with human SLP (PERCEPT-R)
  • Learning Gains: 45% faster mastery vs traditional therapy
For platform details, see /references/mellifluo-platform.md

Anti-Patterns

"One-Size-Fits-All" Therapy

What it looks like: Using the same exercises for all clients regardless of specific needs. Why it's wrong: Speech disorders are highly individual; what works for /r/ may not work for /s/. Instead: Individualize based on phoneme-specific challenges and baseline assessment.

Technology Replacing Clinical Judgment

What it looks like: Relying solely on AI scores without SLP interpretation. Why it's wrong: AI is a tool, not a replacement for clinical expertise. Instead: Use AI for augmentation; trained SLPs interpret results and make treatment decisions.

Ignoring Generalization

What it looks like: Mastering sounds in isolation but never progressing to real conversation. Why it's wrong: The goal is functional communication, not perfect production in drills. Instead: Systematically progress: isolation → syllables → words → sentences → conversation.

Cultural Insensitivity

What it looks like: Treating bilingual speech patterns as disorders. Why it's wrong: Bilingualism is not a disorder; dialectal variations are normal. Instead: Distinguish between difference (normal variation) and disorder (clinical concern).

Best Practices

✅ DO:

  • Use evidence-based practices (cite SLP research)
  • Provide immediate feedback (visual + auditory)
  • Make therapy fun and engaging (gamification)
  • Track progress systematically (data-driven decisions)
  • Personalize to individual needs (adaptive difficulty)
  • Respect client autonomy (client chooses activities)
  • Ensure accessibility (multiple input methods)
  • Collaborate with families/caregivers (home practice)

❌ DON'T:

  • Diagnose without proper credentials (only licensed SLPs diagnose)
  • Provide one-size-fits-all therapy (individualize!)
  • Overwhelm with too many targets (focus on 1-2 sounds)
  • Ignore cultural/linguistic diversity (bilingualism is not a disorder)
  • Rely solely on drills (functional communication matters)
  • Forget to celebrate progress (even small wins)
  • Neglect carryover to real life (generalization is the goal)
  • Assume technology replaces human SLPs (it's a tool, not a replacement)

Integration with Other Skills

  • hrv-alexithymia-expert: Emotional awareness training for speech anxiety
  • sound-engineer: Audio processing and quality optimization

Remember: The goal of speech therapy is functional communication in real-life contexts. Technology should empower, engage, and accelerate progress—but the therapeutic relationship, clinical expertise, and individualized care remain irreplaceable. Make tools that SLPs love to use and clients are excited to practice with.

平台分布

Codex

34.17%
按下载量换算43

Claude

27.63%
按下载量换算35

Cursor

18.46%
按下载量换算23

Gemini CLI

10.33%
按下载量换算13

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

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。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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