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hackathon-wow-detector

github.com/bernieweb3营销与增长49 次安装
代码仓库

关于 hackathon-wow-detector

hackathon-wow-detector 用于从黑客松项目中挑选最值得展示、可现场演示的亮点,并将其安排在演示或路演前段。它依据评审标准评估候选功能,并给出呈现策略与差异化陈述。

主要功能

  1. 01为候选亮点排序
  2. 02选出优先展示的演示亮点
  3. 03评估影响力、新颖度和评审相关性
  4. 04建议强化策略与展示位置
  5. 05生成项目差异化陈述

适用场景

  1. 01决定先演示哪个 MVP 功能
  2. 02明确演示中的核心记忆点
  3. 03让演示契合评审标准
  4. 04区分项目与已知竞品

安装

复制下面的命令,在终端中运行。

$npx skills add https://github.com/bernieweb3/hackathon-ai-devkit --skill hackathon-wow-detector

安装后在 Claude Code 中输入/hackathon-wow-detector确认已加载。

详细介绍

hackathon-wow-detector

目标

找出并强化黑客松项目中最令人惊叹的时刻,确保它在演示和路演中率先呈现,从而最大限度地打动评委。


触发条件

在以下情况下使用此技能:

  • 已确定 MVP 功能,并根据 hackathon-scope-cutter 起草了演示流程
  • 团队需要确定演示时应率先展示哪项功能
  • 存在多个候选功能,需要确定优先级
  • 演示叙事缺少一个能让评委记住的清晰高潮时刻
  • 在阶段 3(范围定义)期间调用,即使用 hackathon-scope-cutter 之后;输出将供演示和路演技能使用

输入

输入类型必填描述
project_titlestring是项目名称
mvp_featuresobject[]是来自 hackathon-scope-cutter 的 MVP 功能
mvp_demo_flowobject[]是来自 hackathon-scope-cutter 的演示步骤
target_userstring是主要用户群体
evaluation_axesobject[]是来自 hackathon-track-analyzer 的评审标准
competitor_ideasstring[]否同一赛道中的其他项目(如已知)

输出

输出描述
wow_moments按影响力排序的所有候选惊艳时刻
primary_wow_moment应率先展示的最强时刻
amplification_tactics如何让主要惊艳时刻更具冲击力
demo_placement惊艳时刻应在演示/路演中的何处呈现
judge_reaction_prediction评委看到后可能会有什么想法或评价
differentiation_statement用一句话说明该项目与其他项目的区别

规则

  1. 根据情感冲击力、新颖性以及与 evaluation_axes 的相关性,评估每项功能。
  2. 将综合影响力 × 评审权重最高的功能选为 primary_wow_moment。
  3. primary_wow_moment 必须能现场演示,而不只是口头描述。
  4. amplification_tactics 至少必须包含:视觉呈现、解说时机、对比铺垫。
  5. demo_placement 必须位于演示时长的前 40% 以内。
  6. differentiation_statement 必须可证伪,不能适用于一般项目。
  7. 如果已知 competitor_ideas,则确保 differentiation_statement 与所有竞品项目都有所区别。

输出格式

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>"

示例

输入:

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"

输出:

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."

上下文文件

知识库
  • ../hackathon-shared-resources/knowledge/hackathon-demo-psychology.md
  • ../hackathon-shared-resources/knowledge/hackathon-demo-patterns.md
  • ../hackathon-shared-resources/knowledge/hackathon-judging-criteria.md
  • ../hackathon-shared-resources/knowledge/hackathon-winning-patterns.md
  • ../hackathon-shared-resources/knowledge/hackathon-pitch-strategy.md
操作手册
  • ../hackathon-shared-resources/playbooks/hackathon-workflow.md

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