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Agent Skill · 开发

hackathon-wow-detector(黑客马拉松哇探测器)

hackathon-wow-detector 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。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。

hackathon-wow-detector 是开发类 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,按安装量在开发类的 21,746 个 Skill 里排第 18,252。

总安装 349周安装 15GitHub Stars 1下载 122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:hackathon-wow-detector(黑客马拉松哇探测器)
来源仓库:https://github.com/bernieweb3/hackathon-ai-devkit
仓库路径:skills/hackathon-wow-detector
安装命令:
npx skills add https://github.com/bernieweb3/hackathon-ai-devkit --skill hackathon-wow-detector
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/bernieweb3/hackathon-ai-devkit --skill hackathon-wow-detector

这个 Skill 是做什么的

开发类 Skill 面向写代码、改代码和排查问题,一般会读写仓库文件,部分还会执行构建或测试命令。

用于处理 GitHub 仓库、Issue 和 Pull Request 信息,协助代码协作与变更管理。适合在需要围绕仓库状态或代码变更进行整理时使用,支持多宿主环境。通过 npx skills add 命令从指定仓库安装,具体用法请参考原始 README。安装前应确认权限范围、维护状态,并评估是否会触发联网或文件操作。

注意:避免直接执行未经验证的命令,防止误改生产环境代码。

来源仓库内路径:skills/hackathon-wow-detector

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/bernieweb3/hackathon-ai-devkit --skill hackath…;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

hackathon-wow-detector 怎么安装?

在终端执行 npx skills add https://github.com/bernieweb3/hackathon-ai-devkit --skill hackathon-wow-det…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

hackathon-wow-detector 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。

hackathon-wow-detector 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。

hackathon-wow-detector 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。

hackathon-wow-detector 用的人多吗?

公开数据是累计安装 349 次,近一周安装 15 次,下载 122 次,GitHub 1 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。

hackathon-wow-detector 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。
  • 需要处理开发类任务、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。

开发类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
skill-vetter

技能审查员 · OpenClaw

8,779,018986操作浏览器MIT-0通过
ontology

本体论 · OpenClaw

6,636,960576只读MIT-0通过
entra-app-registration

内部应用程序注册 · Codex、Claude

6,631,872764接触敏感数据未明确通过
entra-app-registration

内部应用程序注册 · Codex、Claude

3,757,680201接触敏感数据未明确通过
nano-banana-pro

纳米香蕉专业版 · OpenClaw

3,526,898364接触敏感数据MIT-0有提醒
hackathon-wow-detector

黑客马拉松哇探测器 · 本页

3491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

hackathon-wow-detector

Goal

Identify and amplify the single strongest wow-factor moment in a hackathon project, ensuring it is front-loaded in the demo and pitch for maximum judge impact.


Trigger Conditions

Use this skill when:

  • MVP features are defined and the demo flow is drafted from hackathon-scope-cutter
  • The team needs to identify which feature to lead with in the demo
  • Multiple candidate features exist and priority must be established
  • The demo narrative lacks a clear climactic moment judges will remember
  • Invoked during Phase 3 (Scope Definition), after hackathon-scope-cutter; output feeds both demo and pitch skills

Inputs

InputTypeRequiredDescription
project_titlestringYesName of the project
mvp_featuresobject[]YesMVP features from hackathon-scope-cutter
mvp_demo_flowobject[]YesDemo steps from hackathon-scope-cutter
target_userstringYesPrimary user segment
evaluation_axesobject[]YesJudging criteria from hackathon-track-analyzer
competitor_ideasstring[]NoOther projects in the same track, if known

Outputs

OutputDescription
wow_momentsAll candidate wow moments ranked by impact
primary_wow_momentThe single strongest moment to lead with
amplification_tacticsHow to make the primary wow moment land harder
demo_placementWhere in the demo/pitch the wow moment should appear
judge_reaction_predictionWhat judges will likely think/say after seeing it
differentiation_statementOne sentence that separates this project from others

Rules

  1. Evaluate each feature for emotional impact, novelty, and relevance to evaluation_axes.
  2. Select primary_wow_moment as the feature with highest combined impact × judging weight.
  3. primary_wow_moment must be demonstrable live, not just described.
  4. amplification_tactics must include at least: visual framing, narration timing, contrast setup.
  5. demo_placement must be within the first 40% of the demo runtime.
  6. differentiation_statement must be falsifiable — it must not apply to generic projects.
  7. If competitor_ideas are known, ensure differentiation_statement is distinct from all of them.

Output Format

wow_moments:
  - rank: <number>
    feature: "<feature name>"
    impact_score: <1-10>
    novelty_score: <1-10>
    judging_relevance: <1-10>
    combined_score: <number>
    description: "<why this wows judges>"

primary_wow_moment:
  feature: "<feature name>"
  description: "<what happens>"
  live_demonstrable: <true|false>

amplification_tactics:
  - tactic: "<name>"
    description: "<how to apply it>"

demo_placement:
  position: "<percent through demo>"
  context: "<what comes immediately before>"

judge_reaction_prediction: "<string>"

differentiation_statement: "<string>"

Example

Input:

project_title: "AnchorAI"
target_user: "College students with anxiety"
mvp_features:
  - feature: "GPT-4 check-in conversation"
  - feature: "Session memory — AI recalls prior emotional context"
  - feature: "Crisis escalation card (mocked)"
evaluation_axes:
  - axis: "Innovation"
    weight: "high"
  - axis: "Impact"
    weight: "high"
  - axis: "Technical Execution"
    weight: "medium"

Output:

wow_moments:
  - rank: 1
    feature: "Session memory — AI recalls prior emotional context"
    impact_score: 9
    novelty_score: 8
    judging_relevance: 9
    combined_score: 26
    description: "Judges will feel the emotional resonance of an AI that 'knows' the user — this feels like magic"
  - rank: 2
    feature: "Crisis escalation card"
    impact_score: 7
    novelty_score: 4
    judging_relevance: 8
    combined_score: 19
    description: "Demonstrates responsible AI and real-world impact; earns trust from judges"

primary_wow_moment:
  feature: "Session memory — AI recalls prior emotional context"
  description: "In a new chat session, the AI opens with a reference to the user's emotional state from a previous session without being prompted."
  live_demonstrable: true

amplification_tactics:
  - tactic: "Contrast setup"
    description: "Before showing memory, demonstrate a generic AI response with no context. Then switch to AnchorAI. The contrast makes the memory recall land 3× harder."
  - tactic: "Narration pause"
    description: "After the AI references prior context, stop talking for 2 seconds. Let judges process what they just saw."
  - tactic: "Visual framing"
    description: "Zoom into or highlight the specific phrase in the AI response that references prior context."

demo_placement:
  position: "38% (approximately 45 seconds into a 2-minute demo)"
  context: "Immediately follows a neutral opening exchange to establish baseline AI behavior"

judge_reaction_prediction: "Judges will lean forward and say 'wait, how does it know that?' — this triggers the key question that lets the team explain their technical approach."

differentiation_statement: "Unlike every other mental health AI demo today, AnchorAI remembers what you told it last week and uses it to open the next conversation — without being asked."

Context Files

Knowledge Base

  • knowledge/hackathon-demo-psychology.md
  • knowledge/hackathon-demo-patterns.md
  • knowledge/hackathon-judging-criteria.md
  • knowledge/hackathon-winning-patterns.md
  • knowledge/hackathon-pitch-strategy.md

Playbooks

  • playbooks/hackathon-workflow.md

平台分布

Codex

32.27%
按下载量换算39

Claude

30.02%
按下载量换算37

Cursor

19.57%
按下载量换算24

Gemini CLI

8.79%
按下载量换算11

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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