hackathon-judge-simulator

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Chinese

hackathon-judge-simulator

黑客松评委模拟器

Goal

目标

Simulate a panel of hackathon judges evaluating a project, generating likely questions, critical objections, and a predicted scoring outcome so the team can strengthen their pitch and demo.

模拟黑客松评委团对项目进行评估,生成可能的问题、关键异议和预测评分结果,帮助团队优化路演和演示内容。

Trigger Conditions

触发条件

Use this skill when:
  • The pitch deck is drafted and the demo is recorded
  • The team needs to stress-test the pitch before live judging
  • Adversarial questions and rebuttal strategies must be prepared
  • Predicted scores reveal gaps that can be addressed before presentation
  • Invoked during Phase 7 (Evaluation); re-invoke after pitch improvements are made for a second simulation pass

在以下场景使用该技能:
  • 项目路演PPT已完成初稿,演示内容已录制
  • 团队需要在正式评审前对路演进行压力测试
  • 需准备针对性问题及反驳策略
  • 预测分数可揭示路演中需改进的漏洞
  • 在第7阶段(评估阶段)调用;路演优化完成后可再次调用进行第二轮模拟

Inputs

输入项

InputTypeRequiredDescription
project_title
stringYesName of the project
problem_statement
stringYesThe problem being solved
solution_summary
stringYesHow the project solves it
mvp_features
string[]YesWhat was built
tech_stack
string[]YesTechnologies used
evaluation_axes
object[]YesJudging criteria from
hackathon-track-analyzer
pitch_content
stringNoDraft pitch or slide content for more targeted simulation
judge_personas
string[]NoTypes of judges expected (e.g., technical, business, domain expert)

输入项类型是否必填描述
project_title
string项目名称
problem_statement
string项目解决的问题
solution_summary
string项目解决方案概述
mvp_features
string[]已完成的MVP功能
tech_stack
string[]使用的技术栈
evaluation_axes
object[]来自
hackathon-track-analyzer
的评审标准
pitch_content
string路演初稿或幻灯片内容,用于更精准的模拟
judge_personas
string[]预期的评委类型(如技术型、商业型、领域专家型)

Outputs

输出项

OutputDescription
judge_personas_used
Simulated judge types with their likely priorities
questions
Expected judge questions with recommended answers
objections
Critical concerns judges are likely to raise
predicted_scores
Score per evaluation axis with reasoning
overall_verdict
Simulated overall impression and ranking likelihood
pitch_improvements
Specific changes to address predicted weaknesses

输出项描述
judge_personas_used
模拟的评委类型及其优先级
questions
预期的评委问题及推荐回答
objections
评委可能提出的关键顾虑
predicted_scores
各评审维度的得分及理由
overall_verdict
模拟的整体评价及排名可能性
pitch_improvements
针对预测短板的具体改进建议

Rules

规则

  1. Simulate at least 3 distinct judge personas if
    judge_personas
    is not provided.
  2. Generate at least 2 questions per evaluation axis.
  3. Include at least one question that targets a weakness in the solution.
  4. predicted_scores
    must use the same 1–5 scale as
    hackathon-idea-scoring
    .
  5. objections
    must be paired with a recommended rebuttal strategy.
  6. pitch_improvements
    must be actionable within the remaining hackathon time.
  7. Do not simulate only favorable outcomes; include at least one skeptical judge perspective.

  1. 若未提供
    judge_personas
    ,至少模拟3种不同的评委角色
  2. 每个评审维度至少生成2个问题
  3. 至少包含1个针对解决方案短板的问题
  4. predicted_scores
    必须使用与
    hackathon-idea-scoring
    一致的1-5分制
  5. 每个
    objections
    需搭配对应的推荐反驳策略
  6. pitch_improvements
    必须是在剩余黑客松时间内可落地的行动
  7. 不能仅模拟正面结果,需包含至少1个持怀疑态度的评委视角

Output Format

输出格式

yaml
judge_personas_used:
  - persona: "<type>"
    priorities:
      - "<priority>"

questions:
  - judge_persona: "<type>"
    question: "<question text>"
    recommended_answer: "<suggested response>"
    difficulty: "<easy|medium|hard>"

objections:
  - objection: "<concern>"
    likelihood: "<high|medium|low>"
    rebuttal_strategy: "<how to address>"

predicted_scores:
  - axis: "<axis name>"
    score: <1-5>
    reasoning: "<why>"

overall_verdict:
  impression: "<string>"
  ranking_likelihood: "<top-3|mid-field|long-shot>"
  key_strengths:
    - "<strength>"
  key_weaknesses:
    - "<weakness>"

pitch_improvements:
  - issue: "<problem>"
    action: "<what to change>"
    priority: "<high|medium|low>"

yaml
judge_personas_used:
  - persona: "<type>"
    priorities:
      - "<priority>"

questions:
  - judge_persona: "<type>"
    question: "<question text>"
    recommended_answer: "<suggested response>"
    difficulty: "<easy|medium|hard>"

objections:
  - objection: "<concern>"
    likelihood: "<high|medium|low>"
    rebuttal_strategy: "<how to address>"

predicted_scores:
  - axis: "<axis name>"
    score: <1-5>
    reasoning: "<why>"

overall_verdict:
  impression: "<string>"
  ranking_likelihood: "<top-3|mid-field|long-shot>"
  key_strengths:
    - "<strength>"
  key_weaknesses:
    - "<weakness>"

pitch_improvements:
  - issue: "<problem>"
    action: "<what to change>"
    priority: "<high|medium|low>"

Example

示例

Input:
yaml
project_title: "AnchorAI"
problem_statement: "College students wait 6 weeks for counseling while anxiety compounds."
solution_summary: "GPT-4 companion with Redis-backed session memory for persistent emotional context."
mvp_features: ["GPT-4 check-in conversation", "Session memory", "Crisis escalation (mocked)"]
tech_stack: ["Python", "FastAPI", "React", "Redis", "OpenAI GPT-4"]
evaluation_axes:
  - axis: "Innovation"
  - axis: "Impact"
  - axis: "Technical Execution"
Output:
yaml
judge_personas_used:
  - persona: "Technical judge"
    priorities: ["Working implementation", "Appropriate tech choices", "Scalability awareness"]
  - persona: "Impact/domain judge"
    priorities: ["Real user need", "Safety guardrails", "Clinical validity concerns"]
  - persona: "Business judge"
    priorities: ["Market size", "Differentiation", "Go-to-market path"]

questions:
  - judge_persona: "Technical judge"
    question: "How does the memory actually work — what are you storing and retrieving?"
    recommended_answer: "We store a GPT-4 generated summary of each session in Redis, keyed by user ID. On the next session, we prepend that summary to the system prompt. It's simple and it works for the demo."
    difficulty: "medium"
  - judge_persona: "Impact/domain judge"
    question: "Is this safe? What happens if someone is in genuine crisis?"
    recommended_answer: "We detect distress signals and surface hotline resources immediately. We're explicit that AnchorAI is not a therapist. In the roadmap, we escalate to human counselors."
    difficulty: "hard"
  - judge_persona: "Business judge"
    question: "How is this different from Woebot or BetterHelp?"
    recommended_answer: "Woebot has no memory and uses rigid scripts. BetterHelp is $80/week. AnchorAI is the only free, always-available companion that actually knows your history."
    difficulty: "medium"

objections:
  - objection: "This could give vulnerable users dangerous advice"
    likelihood: "high"
    rebuttal_strategy: "Acknowledge directly. Explain the safety guardrails and the explicit non-therapist framing. Offer to show the crisis card in the demo."
  - objection: "Memory feature is just system prompt injection — not novel"
    likelihood: "medium"
    rebuttal_strategy: "Agree it's a simple mechanism. Pivot to impact: the novelty is the UX, not the implementation. Show the user experience, not the code."

predicted_scores:
  - axis: "Innovation"
    score: 4
    reasoning: "Memory-based continuity in mental health context is genuinely novel for a hackathon"
  - axis: "Impact"
    score: 5
    reasoning: "Large, underserved audience with real demonstrated need"
  - axis: "Technical Execution"
    score: 4
    reasoning: "Working live demo with real API integration; Redis memory functions correctly"

overall_verdict:
  impression: "Strong emotional narrative with a memorable live demo moment. Safety question will come up — being prepared for it is the difference between winning and not."
  ranking_likelihood: "top-3"
  key_strengths:
    - "Emotionally resonant problem framing"
    - "Live working wow moment (memory recall)"
    - "Clear differentiation from existing tools"
  key_weaknesses:
    - "Safety and clinical validity will be scrutinized"
    - "Business model not addressed"

pitch_improvements:
  - issue: "No mention of how AnchorAI avoids causing harm"
    action: "Add one sentence to the solution slide: 'AnchorAI is not a therapist — it's a bridge to one.'"
    priority: "high"
  - issue: "No business or monetization framing"
    action: "Add one bullet to vision slide: 'Free tier for students; licensed model for universities'"
    priority: "medium"

输入:
yaml
project_title: "AnchorAI"
problem_statement: "College students wait 6 weeks for counseling while anxiety compounds."
solution_summary: "GPT-4 companion with Redis-backed session memory for persistent emotional context."
mvp_features: ["GPT-4 check-in conversation", "Session memory", "Crisis escalation (mocked)"]
tech_stack: ["Python", "FastAPI", "React", "Redis", "OpenAI GPT-4"]
evaluation_axes:
  - axis: "Innovation"
  - axis: "Impact"
  - axis: "Technical Execution"
输出:
yaml
judge_personas_used:
  - persona: "Technical judge"
    priorities: ["Working implementation", "Appropriate tech choices", "Scalability awareness"]
  - axis: "Impact/domain judge"
    priorities: ["Real user need", "Safety guardrails", "Clinical validity concerns"]
  - persona: "Business judge"
    priorities: ["Market size", "Differentiation", "Go-to-market path"]

questions:
  - judge_persona: "Technical judge"
    question: "How does the memory actually work — what are you storing and retrieving?"
    recommended_answer: "We store a GPT-4 generated summary of each session in Redis, keyed by user ID. On the next session, we prepend that summary to the system prompt. It's simple and it works for the demo."
    difficulty: "medium"
  - judge_persona: "Impact/domain judge"
    question: "Is this safe? What happens if someone is in genuine crisis?"
    recommended_answer: "We detect distress signals and surface hotline resources immediately. We're explicit that AnchorAI is not a therapist. In the roadmap, we escalate to human counselors."
    difficulty: "hard"
  - judge_persona: "Business judge"
    question: "How is this different from Woebot or BetterHelp?"
    recommended_answer: "Woebot has no memory and uses rigid scripts. BetterHelp is $80/week. AnchorAI is the only free, always-available companion that actually knows your history."
    difficulty: "medium"

objections:
  - objection: "This could give vulnerable users dangerous advice"
    likelihood: "high"
    rebuttal_strategy: "Acknowledge directly. Explain the safety guardrails and the explicit non-therapist framing. Offer to show the crisis card in the demo."
  - objection: "Memory feature is just system prompt injection — not novel"
    likelihood: "medium"
    rebuttal_strategy: "Agree it's a simple mechanism. Pivot to impact: the novelty is the UX, not the implementation. Show the user experience, not the code."

predicted_scores:
  - axis: "Innovation"
    score: 4
    reasoning: "Memory-based continuity in mental health context is genuinely novel for a hackathon"
  - axis: "Impact"
    score: 5
    reasoning: "Large, underserved audience with real demonstrated need"
  - axis: "Technical Execution"
    score: 4
    reasoning: "Working live demo with real API integration; Redis memory functions correctly"

overall_verdict:
  impression: "Strong emotional narrative with a memorable live demo moment. Safety question will come up — being prepared for it is the difference between winning and not."
  ranking_likelihood: "top-3"
  key_strengths:
    - "Emotionally resonant problem framing"
    - "Live working wow moment (memory recall)"
    - "Clear differentiation from existing tools"
  key_weaknesses:
    - "Safety and clinical validity will be scrutinized"
    - "Business model not addressed"

pitch_improvements:
  - issue: "No mention of how AnchorAI avoids causing harm"
    action: "Add one sentence to the solution slide: 'AnchorAI is not a therapist — it's a bridge to one.'"
    priority: "high"
  - issue: "No business or monetization framing"
    action: "Add one bullet to vision slide: 'Free tier for students; licensed model for universities'"
    priority: "medium"

Context Files

上下文文件

Knowledge Base

知识库

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

Playbooks

操作手册

  • playbooks/hackathon-workflow.md
  • playbooks/hackathon-workflow.md