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hackathon-wow-detector黑客马拉松哇探测器

Agent Skill

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。

总安装

349

周安装

15

GitHub Stars

1

下载量

122
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

unknown

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: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

简介

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。

SKILL.md

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

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

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

展示第三方安全扫描或审计结果

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

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。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

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