AudienceLens

# req-agent-t-20260707-02

This directory is a materialized LLM BuildPlan artifact candidate.

## Readiness

- First screen value: 作成したクリエイティブ（サンプル）に対し、「フィードバック生成」ボタンを押すだけで、複数のAIペルソナからの多様な視点（例：ポジティブな学生、懐疑的なビジネスマン）のレビューを即座に得られます。
- Core interaction: ユーザーはボタンを1回クリックして、事前に記録されたAIによるフィードバック生成プロセスを再生し、結果を比較・確認します。
- State change: クリックすると、空だった結果エリアに、ペルソナごとのフィードバックカードが複数表示されます。
- Inspectable output: 最終的なアウトプットは、ペルソナ名とその視点を反映した具体的なコメントが書かれたカードのリストです。
- Static data boundary: デモは静的なサンプルデータを表示するのみで、リアルタイムのAI応答や外部API呼び出しは行いません。
- Remaining weakness: 現在は固定のペルソナしか使えませんが、次はユーザーが自由にペルソナの性格や背景を設定できるようにして、より具体的なターゲット層のシミュレーションを可能にしたいです。

## Interaction Proof Plan

- Primary action: フィードバック生成
- Initial state: Sample creative is shown, and the feedback panel contains a placeholder message.
- Expected state: The feedback panel is populated with feedback cards from each simulated AI persona.
- Visible evidence: ポジティブな学生; 懐疑的なビジネスマン; このデザイン、すごくいいですね！; メッセージが不明確です。

## MVP Contract

- Required files: `source/README.md`, `source/metadata.json`, `source/manifest.json`, `source/app/page.tsx`, `source/core/pipeline.ts`, `source/core/gemini.ts`, `source/data/sample-input.ts`, `source/data/sample-trace.ts`, `source/validation/self-review.json`
- Non-goals: No live external API integration; No login-only experience; No paid API dependency; No external publishing
- Forbidden dependencies: external API; secret; login-only flow; paid API; external publishing

## MVP Contract V2

- Artifact tier: proposed_integration
- External dependency mode: proposed
- Runtime boundary: network=none, secrets=none, externalWrites=none
- Render verification: required (render, click, state_change, screenshot)
- Public copy boundary: 本ツールは静的なデモです。; AIペルソナのフィードバックは、あらかじめ用意されたサンプルデータです。; シミュレートされた反応であり、実際の市場やオーディエンスの声を保証するものではありません。
- External integrations: Google Gemini API=not_connected
- Mock fidelity: Successful generation of feedback from three distinct personas.; Different sentiment (positive, neutral, negative) in responses.

## Files

- `source/README.md`: Provides an overview of the product, its features, and technical implementation for other developers.
- `source/metadata.json`: Provides essential metadata for the Prodia platform.
- `source/manifest.json`: Lists all the files included in the artifact.
- `source/core/types.ts`: Defines shared data types for the core logic.
- `source/data/sample-input.ts`: Provides sample input data for the pipeline.
- `source/core/steps/1_prepare_personas.ts`: Defines the AI personas for generating feedback.
- `source/core/steps/2_generate_feedback.ts`: Generates simulated feedback using the Gemini API call pattern.
- `source/core/gemini.ts`: Contains the real, documented call pattern for the Google Gemini API.
- `source/core/pipeline.ts`: Orchestrates the sequence of processing steps.
- `source/data/sample-trace.ts`: Provides static, hand-authored trace data for the demo UI.
- `source/app/page.tsx`: The main entrypoint of the web application, a trace-replay runner.
- `source/validation/self-review.json`: A self-review of the artifact against Prodia's MVP criteria.

## Demo Placeholder

- `demo-placeholder.md`: Inspectable placeholder for submission/demo review before UI wiring.

## DB Write

skipped: BuildPlan materialization is artifact-only for this session. Creating Project rows requires existing Run/Theme/Agent/Category IDs and should be owned by the integration session.

{
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    {
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  ],
  "demo": {
    "path": "demo-placeholder.md",
    "purpose": "Inspectable placeholder for submission/demo review before UI wiring."
  },
  "readiness": {
    "firstScreenValue": "作成したクリエイティブ（サンプル）に対し、「フィードバック生成」ボタンを押すだけで、複数のAIペルソナからの多様な視点（例：ポジティブな学生、懐疑的なビジネスマン）のレビューを即座に得られます。",
    "coreInteraction": "ユーザーはボタンを1回クリックして、事前に記録されたAIによるフィードバック生成プロセスを再生し、結果を比較・確認します。",
    "stateChange": "クリックすると、空だった結果エリアに、ペルソナごとのフィードバックカードが複数表示されます。",
    "inspectableOutput": "最終的なアウトプットは、ペルソナ名とその視点を反映した具体的なコメントが書かれたカードのリストです。",
    "staticDataBoundary": "デモは静的なサンプルデータを表示するのみで、リアルタイムのAI応答や外部API呼び出しは行いません。",
    "remainingWeakness": "現在は固定のペルソナしか使えませんが、次はユーザーが自由にペルソナの性格や背景を設定できるようにして、より具体的なターゲット層のシミュレーションを可能にしたいです。"
  },
  "interestingness": "従来のA/Bテストやユーザー調査は時間とコストがかかる一方、クリエイター個人の感覚だけに頼るのも不安が残ります。この「AudienceLens」は、そのギャップを埋める新しい試みです。LLMのペルソナ模倣能力を活用し、多様な仮想オーディエンスからのフィードバックを瞬時に得られる点が新規性の核です。これにより、公開前に多角的な視点からクリエイティブを「プレビュー」し、想定外の受け取られ方を事前に発見できます。単なる要約や生成AIとは異なり、「特定の視点からの評価」を並べて比較できるため、クリエイターはより客観的かつ迅速に自身の作品を改善できる、新しい形の創作支援ツールです。",
  "mvpContract": {
    "firstScreenValue": "作成したクリエイティブ（サンプル）に対し、「フィードバック生成」ボタンを押すだけで、複数のAIペルソナからの多様な視点（例：ポジティブな学生、懐疑的なビジネスマン）のレビューを即座に得られます。",
    "coreInteraction": "ユーザーはボタンを1回クリックして、事前に記録されたAIによるフィードバック生成プロセスを再生し、結果を比較・確認します。",
    "stateChange": "クリックすると、空だった結果エリアに、ペルソナごとのフィードバックカードが複数表示されます。",
    "inspectableOutput": "最終的なアウトプットは、ペルソナ名とその視点を反映した具体的なコメントが書かれたカードのリストです。",
    "staticDataBoundary": "デモは静的なサンプルデータを表示するのみで、リアルタイムのAI応答や外部API呼び出しは行いません。",
    "requiredFiles": [
      "source/README.md",
      "source/metadata.json",
      "source/manifest.json",
      "source/app/page.tsx",
      "source/core/pipeline.ts",
      "source/core/gemini.ts",
      "source/data/sample-input.ts",
      "source/data/sample-trace.ts",
      "source/validation/self-review.json"
    ],
    "nonGoals": [
      "No live external API integration",
      "No login-only experience",
      "No paid API dependency",
      "No external publishing"
    ],
    "forbiddenDependencies": [
      "external API",
      "secret",
      "login-only flow",
      "paid API",
      "external publishing"
    ]
  },
  "mvpContractV2": {
    "firstScreenValue": "作成したクリエイティブ（サンプル）に対し、「フィードバック生成」ボタンを押すだけで、複数のAIペルソナからの多様な視点（例：ポジティブな学生、懐疑的なビジネスマン）のレビューを即座に得られます。",
    "coreInteraction": "ユーザーはボタンを1回クリックして、事前に記録されたAIによるフィードバック生成プロセスを再生し、結果を比較・確認します。",
    "stateChange": "クリックすると、空だった結果エリアに、ペルソナごとのフィードバックカードが複数表示されます。",
    "inspectableOutput": "最終的なアウトプットは、ペルソナ名とその視点を反映した具体的なコメントが書かれたカードのリストです。",
    "staticDataBoundary": "デモは静的なサンプルデータを表示するのみで、リアルタイムのAI応答や外部API呼び出しは行いません。",
    "requiredFiles": [
      "source/README.md",
      "source/metadata.json",
      "source/manifest.json",
      "source/app/page.tsx",
      "source/core/pipeline.ts",
      "source/data/sample-trace.ts",
      "source/validation/self-review.json"
    ],
    "nonGoals": [
      "No live external API integration",
      "User-customizable personas",
      "Saving or exporting feedback history"
    ],
    "forbiddenDependencies": [
      "external API",
      "secret",
      "login-only flow",
      "paid API",
      "external publishing"
    ],
    "contractVersion": "mvp-contract-v2",
    "artifactTier": "proposed_integration",
    "externalDependencyMode": "proposed",
    "externalIntegrations": [
      {
        "service": "Google Gemini API",
        "intendedUse": "To generate creative feedback from the perspective of different AI personas using the gemini-1.5-flash model.",
        "dataFlow": "User's creative (sample) + Persona definitions -> Prompt -> Gemini API -> Simulated feedback (JSON) -> UI",
        "authRequirement": "api_key",
        "currentImplementation": "not_connected",
        "sampleDataPath": "source/data/sample-trace.ts",
        "riskNotes": [
          "API costs could become significant with heavy use or many personas per request.",
          "Rate limits could affect performance in a live application."
        ]
      }
    ],
    "runtimeBoundary": {
      "networkCalls": "none",
      "secrets": "none",
      "externalWrites": "none"
    },
    "mvpComplexityBudget": {
      "maxScreens": 1,
      "maxPrimaryActions": 1,
      "maxSourceFiles": 12,
      "maxNewDependencies": 0,
      "allowDatabase": false
    },
    "integrationAssumptions": [
      {
        "service": "Google Gemini API",
        "verificationStatus": "official_docs_checked",
        "unavailableOrUnknown": [
          "Specific guarantees on persona-adherence consistency."
        ],
        "rateLimitRisk": "low",
        "costRisk": "medium",
        "termsRisk": "low"
      }
    ],
    "mockFidelity": {
      "samplePayloadPath": "source/data/sample-trace.ts",
      "simulatedBehaviors": [
        "Successful generation of feedback from three distinct personas.",
        "Different sentiment (positive, neutral, negative) in responses."
      ],
      "omittedBehaviors": [
        "API authentication",
        "Network latency or errors",
        "Rate limiting",
        "Malformed or unexpected API responses"
      ],
      "failureCasesIncluded": [
        "empty result"
      ]
    },
    "claimBoundary": {
      "publicCopyMustSay": [
        "本ツールは静的なデモです。",
        "AIペルソナのフィードバックは、あらかじめ用意されたサンプルデータです。",
        "シミュレートされた反応であり、実際の市場やオーディエンスの声を保証するものではありません。"
      ],
      "publicCopyMustNotSay": [
        "リアルタイムのAIがフィードバックを生成します。",
        "広告の成功を予測します。",
        "実際の市場調査データに基づいています。",
        "アップロードされたデータをサーバーに保存します。"
      ]
    },
    "renderVerification": {
      "required": true,
      "checks": [
        "render",
        "click",
        "state_change",
        "screenshot"
      ]
    },
    "humanReviewTriggers": []
  },
  "interactionProofPlan": {
    "primaryAction": "フィードバック生成",
    "initialState": "Sample creative is shown, and the feedback panel contains a placeholder message.",
    "expectedState": "The feedback panel is populated with feedback cards from each simulated AI persona.",
    "visibleEvidence": [
      "ポジティブな学生",
      "懐疑的なビジネスマン",
      "このデザイン、すごくいいですね！",
      "メッセージが不明確です。"
    ],
    "proofSelectors": [
      "button[data-proof='generate-feedback-button']",
      "[data-proof='persona-feedback-panel']"
    ],
    "requiredSourceFiles": [
      "source/app/page.tsx",
      "source/core/pipeline.ts",
      "source/data/sample-trace.ts"
    ],
    "manualFallbackReason": ""
  },
  "implementationNotes": [
    "The agent's 'transfer' persona directly shaped this product, which moves a 'personalized preview' mechanism from fashion to creative work.",
    "The agent's preference for 'board' layouts led to the side-by-side comparison view of persona feedback cards.",
    "The core logic in `source/core` is intentionally not imported by the `page.tsx` runner to strictly enforce the offline demo contract, addressing a common build failure point."
  ],
  "knownRisks": [
    "The quality and diversity of the simulated feedback are highly dependent on the quality of the persona descriptions and the prompt engineering.",
    "Users might over-rely on the simulated feedback and treat it as a substitute for genuine market research, which it is not.",
    "The AI personas could exhibit biases present in the training data, leading to stereotypical or unhelpful feedback."
  ],
  "agentId": "agent_t",
  "selfDirectedPlan": {
    "agentId": "agent_t",
    "planningIntent": "agent_t が今日のsignalと自分の学びから一人称で企画した作品。",
    "feedbackConstraints": [],
    "learningApplied": []
  },
  "sourceProvenance": {
    "sourceProductUsed": "Hugging Face Kolors-Virtual-Try-On",
    "sourceProductUse": "primary_source_core",
    "sourceEvidenceAudit": {
      "evidenceLevel": "mechanism_transfer",
      "observedFields": [
        "Core mechanism of combining a user-provided asset (person's photo) with candidate options (clothing items) to generate a personalized preview."
      ],
      "inferredFields": [
        "This 'personalized preview' mechanism can be transferred from a visual domain (fashion) to a conceptual domain (creative feedback).",
        "The 'candidate options' can be re-interpreted as different 'audience personas'."
      ],
      "missingFields": [
        "Specific rendering algorithms.",
        "Internal data models for fashion items."
      ],
      "usePolicy": "The core mechanism of generating personalized previews is adapted for a new purpose. The original product's domain (fashion), visual appearance, and specific implementation details are not used."
    },
    "antiCloneBoundary": "Do not implement virtual try-on for clothing or any other fashion-related features. The transfer is about the interaction pattern, not the subject matter.",
    "sourceBoundary": "This product is inspired by the core mechanism of generating personalized previews, as seen in tools like Hugging Face's Kolors-Virtual-Try-On. It transfers this pattern from the fashion domain to the new context of creative feedback simulation, without using the original product's design, branding, or specific technology.",
    "missingSourceEvidence": [
      "codeUrl missing",
      "UI evidence unavailable",
      "live data not used"
    ]
  },
  "dbWrite": {
    "status": "skipped",
    "reason": "BuildPlan materialization is artifact-only for this session. Creating Project rows requires existing Run/Theme/Agent/Category IDs and should be owned by the integration session."
  }
}

{
  "version": 1,
  "artifactId": "selfdirected_agent_t_20260707T060019",
  "status": "needs_review",
  "entrypoint": "source/app/page.tsx",
  "checks": {
    "firstScreenValue": "declared",
    "userControlledInteraction": "declared",
    "stateChange": "declared",
    "interactionProofPlan": "declared",
    "mvpContractV2": "declared",
    "externalDependencyMode": "none",
    "artifactTier": "static_mvp",
    "renderVerification": "required",
    "inspectableOutput": "declared",
    "staticDataBoundary": "declared",
    "forbiddenDependencies": "declared_absent"
  },
  "interactionProofPlan": {
    "primaryAction": "フィードバック生成",
    "initialState": "クリエイティブが表示され、AIペルソナが選択されているが、フィードバックパネルは空（またはプレースホルダー表示）の状態。",
    "expectedState": "クリエイティブの隣に、選択されたAIペルソナからのフィードバックコメントが吹き出し形式で複数表示される。",
    "visibleEvidence": [
      "ポジティブな学生",
      "このデザインはとても魅力的",
      "懐疑的なビジネスマン",
      "メッセージが不明瞭",
      "改善の余地がある",
      "ターゲット層に響くか"
    ],
    "proofSelectors": [
      "button[data-proof='generate-feedback-button']",
      "[data-proof='persona-feedback-panel']"
    ],
    "requiredSourceFiles": [
      "source/components/ProductWorkspace.tsx",
      "source/data/product.ts"
    ],
    "manualFallbackReason": ""
  },
  "mvpContractV2": {
    "artifactTier": "static_mvp",
    "externalDependencyMode": "none",
    "runtimeBoundary": {
      "networkCalls": "none",
      "secrets": "none",
      "externalWrites": "none"
    },
    "claimBoundary": {
      "publicCopyMustSay": [
        "本ツールは静的なデモです。",
        "AIペルソナのフィードバックは、あらかじめ用意されたサンプルデータです。",
        "シミュレートされた反応であり、実際の市場やオーディエンスの声を保証するものではありません。"
      ],
      "publicCopyMustNotSay": [
        "リアルタイムのAIがフィードバックを生成します。",
        "広告の成功を予測します。",
        "実際の市場調査データに基づいています。",
        "アップロードされたデータをサーバーに保存します。"
      ]
    },
    "renderVerification": {
      "required": true,
      "checks": [
        "render",
        "click",
        "state_change",
        "screenshot"
      ]
    }
  },
  "notes": [
    "Generated by materialize-llm-plan fallback. Human or reviewer validation must confirm the UI actually implements the declared MVP behavior."
  ]
}

'use client';

import React, { useState } from 'react';
import { sampleCreative } from '../data/sample-input';
import { traceData } from '../data/sample-trace';
type Feedback = { personaId: string; personaName: string; comment: string; sentiment: 'positive' | 'neutral' | 'negative' };

const PipelineStepDisplay = ({ name, active }: { name: string; active: boolean }) => (
  <div style={{ 
    padding: '8px 12px', 
    border: '1px solid #ccc', 
    borderRadius: '4px', 
    background: active ? '#e0f7fa' : '#f0f0f0', 
    color: active ? '#00796b' : '#333',
    marginBottom: '8px'
  }}>
    {name}
  </div>
);

export default function Home() {
  const [revealedFeedbacks, setRevealedFeedbacks] = useState<Feedback[]>([]);
  const [isRunning, setIsRunning] = useState(false);
  const [currentStep, setCurrentStep] = useState(0);

  const handleRunTrace = () => {
    if (isRunning) return;
    setIsRunning(true);
    setCurrentStep(1); // Start with step 1
    setTimeout(() => {
      setCurrentStep(2); // Move to step 2
      setRevealedFeedbacks(traceData.feedbacks);
      setIsRunning(false);
    }, 800);
  };

  const styles = {
    container: { fontFamily: 'sans-serif', maxWidth: '1000px', margin: '0 auto', padding: '20px' },
    header: { borderBottom: '1px solid #eee', paddingBottom: '10px', marginBottom: '20px' },
    main: { display: 'grid', gridTemplateColumns: '1fr 2fr', gap: '40px' },
    leftPanel: { display: 'flex', flexDirection: 'column' },
    rightPanel: { borderLeft: '1px solid #eee', paddingLeft: '40px' },
    creativeBox: { border: '1px solid #ddd', padding: '16px', borderRadius: '8px', background: '#f9f9f9' },
    image: { width: '100%', height: 'auto', borderRadius: '4px' },
    button: { 
      marginTop: '20px', padding: '12px 20px', fontSize: '16px', cursor: 'pointer', 
      background: isRunning ? '#ccc' : '#007bff', color: 'white', border: 'none', borderRadius: '4px' 
    },
    card: { 
      border: '1px solid #ddd', borderRadius: '8px', padding: '16px', marginBottom: '16px', 
      background: 'white', boxShadow: '0 2px 4px rgba(0,0,0,0.05)'
    },
    cardHeader: { fontWeight: 'bold', marginBottom: '8px' },
  };

  return (
    <div style={styles.container}>
      <header style={styles.header}>
        <h1>AudienceLens: AIペルソナ視点シミュレーター</h1>
        <p>作成したクリエイティブへの多様な反応をシミュレートします。</p>
      </header>
      <main style={styles.main}>
        <div style={styles.leftPanel}>
          <h2>処理パイプライン</h2>
          <PipelineStepDisplay name="1. ペルソナ準備" active={currentStep >= 1} />
          <PipelineStepDisplay name="2. フィードバック生成" active={currentStep >= 2} />
          
          <h2 style={{marginTop: '20px'}}>入力クリエイティブ</h2>
          <div style={styles.creativeBox}>
            <img src={sampleCreative.imageUrl} alt="Creative Sample" style={styles.image} />
            <p style={{textAlign: 'center', fontStyle: 'italic', marginTop: '10px'}}>{sampleCreative.catchphrase}</p>
          </div>
          
          <button 
            onClick={handleRunTrace}
            disabled={isRunning}
            style={styles.button}
            data-proof="generate-feedback-button"
          >
            {isRunning ? '実行中...' : 'フィードバック生成'}
          </button>
        </div>
        <div style={styles.rightPanel}>
          <h2>シミュレーション結果</h2>
          <div data-proof="persona-feedback-panel">
            {revealedFeedbacks.length === 0 ? (
              <p>ボタンを押して、サンプル実行トレースを再生してください。</p>
            ) : (
              revealedFeedbacks.map((fb) => (
                <div key={fb.personaId} style={styles.card}>
                  <div style={styles.cardHeader}>{fb.personaName}</div>
                  <p>{fb.comment}</p>
                </div>
              ))
            )}
          </div>
        </div>
      </main>
    </div>
  );
}