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Agent Skill · 研究检索

hum-discourse(嗡嗡声话语)

hum-discourse 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

hum-discourse 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,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 59,803。

总安装 376周安装 16GitHub Stars 125下载 132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:hum-discourse(嗡嗡声话语)
来源仓库:https://github.com/asgard-ai-platform/skills
仓库路径:skills/hum-discourse
安装命令:
npx skills add https://github.com/asgard-ai-platform/skills --skill hum-discourse
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/asgard-ai-platform/skills --skill hum-discourse

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。

hum-discourse 用于查找、检索和筛选相关信息,支持基于关键词快速定位结果。适用于信息搜集、任务场景匹配和研究线索梳理场景。通过 npx skills add 命令从 GitHub 仓库安装,需确认权限范围。使用前建议核实是否会触发联网或文件读写操作。可结合原始 README 继续核验具体功能和使用限制。

来源仓库内路径:skills/hum-discourse

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/asgard-ai-platform/skills --skill hum-discours…;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

hum-discourse 怎么安装?

在终端执行 npx skills add https://github.com/asgard-ai-platform/skills --skill hum-discourse。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

hum-discourse 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。

hum-discourse 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。

hum-discourse 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。

hum-discourse 用的人多吗?

公开数据是累计安装 376 次,近一周安装 16 次,下载 132 次,GitHub 125 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。

hum-discourse 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

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。
  • 第三方扫描已通过,常见的高风险行为没有命中。
  • 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
hum-discourse

嗡嗡声话语 · 本页

376125只读未明确通过

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

Discourse Analysis

Overview

Discourse analysis examines how language shapes (not just reflects) reality. It reveals how texts construct identities, power relationships, and social norms through word choice, framing, and what is left unsaid. Especially valuable for analyzing media, corporate communications, policy documents, and political speech.

Framework

IRON LAW: Language Constructs Reality, Not Just Describes It

The choice of words is never neutral. "Restructuring" vs "layoffs",
"enhanced interrogation" vs "torture", "undocumented workers" vs
"illegal aliens" — same events, different realities constructed.

Discourse analysis examines WHAT language does, not just what it says.

Key Analytical Dimensions

  1. Framing: How is the issue defined? What metaphors are used? What's included/excluded?
  2. Subject positions: Who is positioned as agent (active doer) vs patient (passive receiver)?
  3. Presupposition: What is taken for granted without being stated?
  4. Intertextuality: What other texts/discourses does this reference or draw upon?
  5. Power relations: Whose voice is amplified? Whose is silenced?

Analysis Steps

  1. Select the text: What specific communication are you analyzing?
  2. Context: Who produced it? For whom? In what setting? What's the purpose?
  3. Lexical analysis: What word choices are notable? What alternatives were available?
  4. Structural analysis: How is the text organized? What comes first? What's emphasized?
  5. What's absent: What is NOT said? Who is NOT represented?
  6. Power mapping: Who benefits from this particular framing?

Output Format

# Discourse Analysis: {Text/Document}

## Context
- Producer: {who created this text}
- Audience: {intended recipients}
- Purpose: {stated and unstated goals}
- Genre: {press release / policy doc / speech / ad}

## Framing Analysis
| Element | In the Text | Alternative Framing | Effect |
|---------|------------|-------------------|--------|
| {key term} | "{actual language}" | "{what could have been said}" | {how this shapes perception} |

## Subject Positions
- Agent (active): {who does things}
- Patient (passive): {who has things done to them}
- Absent: {who is not mentioned}

## Presuppositions
- {what the text assumes without stating}

## Power Analysis
- This framing benefits: {who}
- This framing disadvantages: {who}

## Key Findings
{What the discourse analysis reveals that a surface reading misses}

Examples

Correct Application

Scenario: Analyzing a tech company's layoff announcement

Text: "We are making the difficult decision to right-size our organization to better position ourselves for long-term growth."

ElementTextAlternativeEffect
"right-size"Implies current size is wrong"lay off 500 employees"Euphemism hides human impact
"our organization"Company as abstract entity"our colleagues"Removes personal connection to affected people
"difficult decision"Positions management as suffering"decision that will displace 500 families"Redirects sympathy toward decision-makers
"long-term growth"Forward-looking justificationNo mention of what caused over-hiringSkips accountability for the situation

Subject positions: Management = agents making "difficult decisions" (sympathetic). Employees = completely absent as subjects. ✓

Incorrect Application

  • "This press release is biased" → Too vague. Which words? What framing? Bias toward what? Discourse analysis requires specific textual evidence. Violates Iron Law: analyze what language DOES.

Gotchas

  • All language is "constructed": Discourse analysis doesn't mean the text is "lying." Even honest communication makes framing choices. The question is what those choices reveal and conceal.
  • Analyst's own discourse: Your analysis is also a discourse with its own framing and assumptions. Be reflexive about your own position.
  • Context is everything: The same words mean different things in different contexts. "We need to move fast" in a startup vs in a hospital has very different implications.
  • Don't over-read: Not every word choice is strategic. Sometimes "right-size" is just corporate jargon the writer learned, not a deliberate framing strategy. Consider intentionality.
  • Discourse analysis is not fact-checking: It examines how meaning is constructed, not whether claims are true. Pair with evidence-based analysis for a complete picture.

References

  • For Fairclough's Critical Discourse Analysis framework, see references/cda-framework.md

平台分布

Codex

32.24%
按下载量换算43

Claude

29.16%
按下载量换算38

Cursor

20.2%
按下载量换算27

Gemini CLI

9.95%
按下载量换算13

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

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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