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
- 01为候选亮点排序
- 02选出优先展示的演示亮点
- 03评估影响力、新颖度和评审相关性
- 04建议强化策略与展示位置
- 05生成项目差异化陈述
适用场景
- 01决定先演示哪个 MVP 功能
- 02明确演示中的核心记忆点
- 03让演示契合评审标准
- 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_title | string | 是 | 项目名称 |
mvp_features | object[] | 是 | 来自 hackathon-scope-cutter 的 MVP 功能 |
mvp_demo_flow | object[] | 是 | 来自 hackathon-scope-cutter 的演示步骤 |
target_user | string | 是 | 主要用户群体 |
evaluation_axes | object[] | 是 | 来自 hackathon-track-analyzer 的评审标准 |
competitor_ideas | string[] | 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
- 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。 - 将综合影响力 × 评审权重最高的功能选为
primary_wow_moment。 primary_wow_moment必须能现场演示,而不只是口头描述。amplification_tactics至少必须包含:视觉呈现、解说时机、对比铺垫。demo_placement必须位于演示时长的前 40% 以内。differentiation_statement必须可证伪,不能适用于一般项目。- 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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