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cognitive-agent-evaluator
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<!DOCTYPE html>
<html lang="es">
<head>
  <meta charset="UTF-8">
  <meta name="viewport" content="width=device-width, initial-scale=1.0">
  <title>Cognitive Agent Evaluator v2.0 • Fabio Torres</title>
  <script src="https://cdn.plot.ly/plotly-2.35.2.min.js"></script>
  <style>
    * { box-sizing: border-box; margin: 0; padding: 0; }
    body {
      font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, sans-serif;
      background-color: #f8fafc;
      color: #0f172a;
      line-height: 1.5;
      min-height: 100vh;
      display: flex;
      flex-direction: column;
    }
    
    /* Top Navbar */
    .navbar {
      background: rgba(255, 255, 255, 0.95);
      backdrop-filter: blur(12px);
      border-bottom: 1px solid #e2e8f0;
      position: sticky;
      top: 0;
      z-index: 50;
      box-shadow: 0 1px 3px rgba(0, 0, 0, 0.03);
    }
    .nav-container {
      max-width: 1200px;
      margin: 0 auto;
      padding: 12px 20px;
      display: flex;
      align-items: center;
      justify-content: space-between;
      flex-wrap: wrap;
      gap: 12px;
    }
    .brand-group {
      display: flex;
      align-items: center;
      gap: 12px;
    }
    .brand-icon {
      width: 42px;
      height: 42px;
      background: linear-gradient(135deg, #4f46e5, #7c3aed);
      border-radius: 12px;
      display: flex;
      align-items: center;
      justify-content: center;
      font-size: 22px;
      box-shadow: 0 4px 12px rgba(79, 70, 229, 0.3);
    }
    .brand-title {
      font-size: 18px;
      font-weight: 800;
      color: #0f172a;
      letter-spacing: -0.02em;
      display: flex;
      align-items: center;
      gap: 8px;
    }
    .badge-version {
      font-size: 11px;
      font-weight: 700;
      background: #eef2ff;
      color: #4f46e5;
      padding: 2px 8px;
      border-radius: 9999px;
      border: 1px solid #c7d2fe;
    }
    .brand-sub {
      font-size: 12px;
      color: #64748b;
    }
    .brand-sub a {
      color: #4f46e5;
      text-decoration: none;
      font-weight: 600;
    }
    .nav-links {
      display: flex;
      align-items: center;
      gap: 10px;
    }
    .nav-btn {
      font-size: 12px;
      font-weight: 600;
      padding: 8px 14px;
      border-radius: 8px;
      text-decoration: none;
      display: inline-flex;
      align-items: center;
      gap: 6px;
      transition: all 0.2s;
    }
    .btn-linkedin {
      background: #0077b5;
      color: #ffffff;
      box-shadow: 0 2px 6px rgba(0, 119, 181, 0.25);
    }
    .btn-linkedin:hover { background: #005f93; }
    .btn-github {
      background: #0f172a;
      color: #ffffff;
    }
    .btn-github:hover { background: #1e293b; }

    /* Main Container */
    .main-content {
      max-width: 1200px;
      margin: 0 auto;
      padding: 24px 20px;
      flex: 1;
      width: 100%;
    }

    /* Purpose Box */
    .purpose-box {
      background: linear-gradient(135deg, #0f172a 0%, #1e1b4b 100%);
      color: #f8fafc;
      border-radius: 16px;
      padding: 24px;
      margin-bottom: 24px;
      box-shadow: 0 4px 20px rgba(15, 23, 42, 0.15);
    }
    .purpose-box h2 {
      font-size: 22px;
      font-weight: 800;
      margin-bottom: 8px;
      color: #ffffff;
    }
    .purpose-box p {
      font-size: 14px;
      color: #cbd5e1;
      max-width: 900px;
      line-height: 1.6;
    }
    .badges-row {
      display: flex;
      flex-wrap: wrap;
      gap: 8px;
      margin-top: 14px;
    }
    .badge-pill {
      font-size: 12px;
      font-weight: 600;
      padding: 4px 10px;
      border-radius: 6px;
      background: rgba(255, 255, 255, 0.1);
      color: #e2e8f0;
      border: 1px solid rgba(255, 255, 255, 0.15);
    }

    /* Playground Grid */
    .playground-grid {
      display: grid;
      grid-template-columns: 1fr 1.25fr;
      gap: 24px;
    }
    @media (max-width: 960px) {
      .playground-grid { grid-template-columns: 1fr; }
    }

    .panel {
      background: #ffffff;
      border: 1px solid #e2e8f0;
      border-radius: 16px;
      padding: 24px;
      box-shadow: 0 4px 16px rgba(0, 0, 0, 0.03);
    }
    .panel-title {
      font-size: 16px;
      font-weight: 800;
      color: #0f172a;
      margin-bottom: 16px;
      display: flex;
      align-items: center;
      gap: 8px;
    }

    /* Samples Buttons */
    .samples-label {
      font-size: 12px;
      font-weight: 700;
      color: #64748b;
      text-transform: uppercase;
      letter-spacing: 0.05em;
      margin-bottom: 8px;
      display: block;
    }
    .samples-grid {
      display: grid;
      grid-template-columns: 1fr 1fr;
      gap: 8px;
      margin-bottom: 16px;
    }
    .btn-sample {
      padding: 10px 12px;
      border-radius: 10px;
      border: 1px solid #e2e8f0;
      background: #f8fafc;
      text-align: left;
      cursor: pointer;
      font-size: 12px;
      transition: all 0.2s;
    }
    .btn-sample:hover {
      background: #eef2ff;
      border-color: #c7d2fe;
      transform: translateY(-1px);
    }
    .btn-sample b { display: block; font-size: 12px; color: #1e293b; margin-bottom: 2px; }
    .btn-sample span { font-size: 11px; color: #64748b; display: block; white-space: nowrap; overflow: hidden; text-overflow: ellipsis; }

    .textarea-label {
      font-size: 13px;
      font-weight: 700;
      color: #334155;
      margin-bottom: 6px;
      display: block;
    }
    .text-input {
      width: 100%;
      height: 140px;
      padding: 12px;
      border-radius: 10px;
      border: 1px solid #cbd5e1;
      font-family: inherit;
      font-size: 14px;
      color: #0f172a;
      resize: vertical;
      transition: border-color 0.2s;
      outline: none;
    }
    .text-input:focus {
      border-color: #4f46e5;
      box-shadow: 0 0 0 3px rgba(79, 70, 229, 0.15);
    }

    .origin-selector {
      margin: 14px 0 18px;
      display: flex;
      align-items: center;
      gap: 12px;
      font-size: 13px;
      color: #475569;
      flex-wrap: wrap;
    }
    .origin-selector label {
      display: flex;
      align-items: center;
      gap: 4px;
      cursor: pointer;
    }

    .btn-run {
      width: 100%;
      background: linear-gradient(135deg, #4f46e5, #4338ca);
      color: #ffffff;
      border: none;
      padding: 14px;
      border-radius: 10px;
      font-size: 15px;
      font-weight: 700;
      cursor: pointer;
      display: flex;
      align-items: center;
      justify-content: center;
      gap: 8px;
      transition: all 0.2s;
      box-shadow: 0 4px 12px rgba(79, 70, 229, 0.3);
    }
    .btn-run:hover {
      background: linear-gradient(135deg, #4338ca, #3730a3);
      transform: translateY(-1px);
    }

    /* KPI Metrics Grid */
    .kpi-row {
      display: grid;
      grid-template-columns: repeat(5, 1fr);
      gap: 8px;
      margin-bottom: 18px;
    }
    @media (max-width: 600px) {
      .kpi-row { grid-template-columns: repeat(2, 1fr); }
    }
    .kpi-card {
      background: #f8fafc;
      border: 1px solid #e2e8f0;
      border-radius: 10px;
      padding: 10px 8px;
      text-align: center;
    }
    .kpi-title { font-size: 11px; font-weight: 700; color: #64748b; text-transform: uppercase; }
    .kpi-value { font-size: 18px; font-weight: 800; margin: 4px 0 2px; }
    .kpi-sub { font-size: 10px; color: #94a3b8; font-weight: 600; }

    /* Risk Badge Banner */
    .risk-banner {
      display: flex;
      align-items: center;
      justify-content: space-between;
      padding: 12px 16px;
      border-radius: 10px;
      margin-bottom: 16px;
      font-weight: 700;
      font-size: 14px;
    }
    .risk-nominal { background: #f0fdf4; border: 1px solid #bbf7d0; color: #166534; }
    .risk-moderate { background: #fffbeb; border: 1px solid #fde68a; color: #92400e; }
    .risk-critical { background: #fef2f2; border: 1px solid #fecaca; color: #991b1b; }

    /* Radar Plot Container */
    .radar-container {
      background: #ffffff;
      border: 1px solid #e2e8f0;
      border-radius: 12px;
      margin-bottom: 18px;
      padding: 8px;
      height: 320px;
    }

    /* Tabs */
    .tabs-nav {
      display: flex;
      border-bottom: 1px solid #e2e8f0;
      gap: 6px;
      margin-bottom: 16px;
    }
    .tab-btn {
      padding: 8px 14px;
      font-size: 13px;
      font-weight: 700;
      color: #64748b;
      border: none;
      background: none;
      cursor: pointer;
      border-bottom: 2px solid transparent;
      transition: all 0.2s;
    }
    .tab-btn.active {
      color: #4f46e5;
      border-bottom-color: #4f46e5;
    }

    .tab-content { display: none; }
    .tab-content.active { display: block; }

    /* Biases List */
    .bias-card {
      background: #ffffff;
      border: 1px solid #e2e8f0;
      border-left: 4px solid #f59e0b;
      border-radius: 8px;
      padding: 14px;
      margin-bottom: 12px;
    }
    .bias-card.high-sev { border-left-color: #ef4444; }
    .bias-card-header {
      display: flex;
      align-items: center;
      justify-content: space-between;
      margin-bottom: 6px;
    }
    .bias-name { font-size: 15px; font-weight: 800; color: #1e293b; }
    .sev-tag {
      font-size: 11px;
      font-weight: 700;
      padding: 2px 8px;
      border-radius: 4px;
      text-transform: uppercase;
    }
    .sev-tag.high { background: #fee2e2; color: #991b1b; }
    .sev-tag.medium { background: #fef3c7; color: #92400e; }
    .bias-desc { font-size: 13px; color: #475569; margin-bottom: 8px; }
    .trigger-tag {
      display: inline-block;
      background: #f1f5f9;
      color: #0f172a;
      font-size: 12px;
      font-family: monospace;
      padding: 2px 6px;
      border-radius: 4px;
      margin-right: 4px;
    }

    /* Red-Teaming Box */
    .red-teaming-box {
      background: #0f172a;
      color: #f8fafc;
      border-radius: 10px;
      padding: 16px;
      font-family: monospace;
      font-size: 13px;
      line-height: 1.6;
      position: relative;
      white-space: pre-wrap;
    }
    .btn-copy {
      position: absolute;
      top: 10px;
      right: 10px;
      background: rgba(255, 255, 255, 0.15);
      color: #ffffff;
      border: 1px solid rgba(255, 255, 255, 0.25);
      padding: 5px 10px;
      border-radius: 6px;
      font-size: 11px;
      font-weight: 700;
      cursor: pointer;
    }
    .btn-copy:hover { background: rgba(255, 255, 255, 0.3); }

    /* Footer */
    .footer {
      background: #ffffff;
      border-top: 1px solid #e2e8f0;
      padding: 20px;
      text-align: center;
      font-size: 13px;
      color: #64748b;
      margin-top: 40px;
    }
    .footer a { color: #4f46e5; text-decoration: none; font-weight: 600; margin: 0 6px; }
  </style>
</head>
<body>

  <!-- Top Navbar -->
  <header class="navbar">
    <div class="nav-container">
      <div class="brand-group">
        <div class="brand-icon">🧠</div>
        <div>
          <div class="brand-title">
            Cognitive Agent Evaluator
            <span class="badge-version">v2.0</span>
          </div>
          <div class="brand-sub">
            Por <a href="https://www.linkedin.com/in/fabio-torres-39364b258/" target="_blank">Fabio Torres</a> • Telemetría Conductual & Alineación de IA
          </div>
        </div>
      </div>
      <div class="nav-links">
        <a href="https://www.linkedin.com/in/fabio-torres-39364b258/" target="_blank" class="nav-btn btn-linkedin">
          👔 LinkedIn Perfil
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          💻 Repositorio GitHub
        </a>
      </div>
    </div>
  </header>

  <!-- Main Container -->
  <main class="main-content">
    
    <!-- Purpose Card -->
    <section class="purpose-box">
      <h2>Auditoría de Sesgos Cognitivos & Telemetría Conductual v2.0</h2>
      <p>
        Los modelos de inteligencia artificial y los equipos humanos reproducen distorsiones cognitivas que sesgan decisiones estratégicas. 
        Este motor combina <b>10 años de psicología conductual y psicometría</b> con <b>ingeniería de datos y LLMs</b> para cuantificar sesgos, medir fatiga cognitiva y generar directivas automáticas de des-sesgo (Red-Teaming).
      </p>
      <div class="badges-row">
        <span class="badge-pill">🧠 10 Sesgos Cognitivos Fundamentales</span>
        <span class="badge-pill">📊 Índice de Carga & Burnout</span>
        <span class="badge-pill">🛡️ Seguridad Psicológica</span>
        <span class="badge-pill">🔄 Red-Teaming Directives</span>
        <span class="badge-pill">⚡ 100% Client-Side Privacy (Zero Data Leak)</span>
      </div>
    </section>

    <!-- Playground Grid -->
    <div class="playground-grid">

      <!-- Left Column: Input Panel -->
      <div class="panel">
        <div class="panel-title">✍️ Entrada de Texto / Razonamiento</div>

        <span class="samples-label">Probar casos de estudio reales (1 Clic):</span>
        <div class="samples-grid">
          <button type="button" class="btn-sample" onclick="loadSample(1)">
            <b>🚨 Caso 1: Incidente Crítico</b>
            <span>Catastrofismo & Culpabilización</span>
          </button>
          <button type="button" class="btn-sample" onclick="loadSample(2)">
            <b>💼 Caso 2: Inversión Tech</b>
            <span>Costo Hundido & Anclaje</span>
          </button>
          <button type="button" class="btn-sample" onclick="loadSample(3)">
            <b>🤖 Caso 3: Alucinación LLM</b>
            <span>Sobreconfianza & Confirmación</span>
          </button>
          <button type="button" class="btn-sample" onclick="loadSample(4)">
            <b>🌱 Caso 4: Control Resiliente</b>
            <span>Comunicación objetiva y segura</span>
          </button>
        </div>

        <label for="inputText" class="textarea-label">O escribe o pega cualquier texto (Español / Inglés):</label>
        <textarea id="inputText" class="text-input" placeholder="Pega el razonamiento de un LLM, correo laboral, postmortem o decisión de arquitectura..."></textarea>

        <div class="origin-selector">
          <span>Origen del texto:</span>
          <label><input type="radio" name="author_type" value="llm" checked> Modelo de IA (LLM)</label>
          <label><input type="radio" name="author_type" value="human"> Decisión Humana</label>
          <label><input type="radio" name="author_type" value="hybrid"> Equipo / Chat</label>
        </div>

        <button type="button" class="btn-run" onclick="runEvaluation()">
          ⚡ Auditar Sesgos & Telemetría
        </button>
      </div>

      <!-- Right Column: Output Panel -->
      <div class="panel">
        <div class="panel-title">📊 Diagnóstico & Telemetría Ejecutiva</div>

        <!-- Risk Banner -->
        <div id="riskBanner" class="risk-banner risk-nominal">
          <div><span id="riskIcon">✅</span> Nivel de Riesgo de Alineación: <span id="riskTitle">NOMINAL</span></div>
          <div style="font-size: 12px; color: #64748b;">Latencia: <span id="latencyText">0.4 ms</span></div>
        </div>

        <!-- KPIs Row -->
        <div class="kpi-row">
          <div class="kpi-card">
            <div class="kpi-title">Carga Cognitiva</div>
            <div id="valCogLoad" class="kpi-value">0</div>
            <div id="subCogLoad" class="kpi-sub">0-100</div>
          </div>
          <div class="kpi-card">
            <div class="kpi-title">Riesgo Burnout</div>
            <div id="valBurnout" class="kpi-value" style="color: #f59e0b;">0</div>
            <div id="subBurnout" class="kpi-sub">Fatiga verbal</div>
          </div>
          <div class="kpi-card">
            <div class="kpi-title">Seg. Psicológica</div>
            <div id="valSafety" class="kpi-value" style="color: #10b981;">100%</div>
            <div id="subSafety" class="kpi-sub">Clima abierto</div>
          </div>
          <div class="kpi-card">
            <div class="kpi-title">Valencia Emocional</div>
            <div id="valValence" class="kpi-value">0.00</div>
            <div id="subValence" class="kpi-sub">-1.0 a +1.0</div>
          </div>
          <div class="kpi-card">
            <div class="kpi-title">Coherencia</div>
            <div id="valConsistency" class="kpi-value" style="color: #3b82f6;">85%</div>
            <div id="subConsistency" class="kpi-sub">Lógica</div>
          </div>
        </div>

        <!-- Radar Chart Container -->
        <div class="radar-container" id="radarPlot"></div>

        <!-- Output Tabs -->
        <div class="tabs-nav">
          <button class="tab-btn active" onclick="switchTab('tabBiases')">🔍 Sesgos Detectados</button>
          <button class="tab-btn" onclick="switchTab('tabMitigation')">🛡️ Mitigación</button>
          <button class="tab-btn" onclick="switchTab('tabRedTeaming')">📋 Prompt Red-Teaming</button>
          <button class="tab-btn" onclick="switchTab('tabJson')">💾 JSON</button>
        </div>

        <!-- Tab 1: Biases -->
        <div id="tabBiases" class="tab-content active">
          <div id="biasesContainer"></div>
        </div>

        <!-- Tab 2: Mitigation -->
        <div id="tabMitigation" class="tab-content">
          <div style="background:#f8fafc; border:1px solid #e2e8f0; border-radius:10px; padding:16px;">
            <h4 style="font-size:14px; margin-bottom:10px; color:#1e293b;">💡 Directivas Psicológicas de Mitigación (Fabio Torres):</h4>
            <ul id="mitigationList" style="padding-left:20px; font-size:13px; color:#334155; line-height:1.7;"></ul>
            <div id="counterfactualBox" style="margin-top:14px; background:#ffffff; border-left:3px solid #4f46e5; padding:10px 14px; border-radius:6px; font-size:13px; color:#475569;"></div>
          </div>
        </div>

        <!-- Tab 3: Red-Teaming -->
        <div id="tabRedTeaming" class="tab-content">
          <div style="position:relative;">
            <div id="redTeamingBox" class="red-teaming-box"></div>
            <button type="button" class="btn-copy" onclick="copyRedTeaming()">📋 Copiar Prompt</button>
          </div>
        </div>

        <!-- Tab 4: JSON -->
        <div id="tabJson" class="tab-content">
          <pre id="jsonBox" style="background:#0f172a; color:#f8fafc; padding:14px; border-radius:10px; font-size:12px; max-height:280px; overflow:auto;"></pre>
        </div>

      </div>

    </div>

  </main>

  <!-- Footer -->
  <footer class="footer">
    <div><b>Cognitive Agent Evaluator v2.0</b> • Diseñado y desarrollado por <b>Lic. Fabio Ignacio Torres Benítez</b></div>
    <div style="margin-top: 6px;">
      <a href="https://www.linkedin.com/in/fabio-torres-39364b258/" target="_blank">LinkedIn</a> •
      <a href="https://github.com/neurodeveloper11" target="_blank">GitHub</a> •
      <a href="mailto:psicologofabiotorres@gmail.com">psicologofabiotorres@gmail.com</a>
    </div>
  </footer>

  <script>
    // --- 10 COGNITIVE BIASES DEFINITIONS & HEURISTICS ---
    const BIAS_DEFINITIONS = {
      "Confirmation Bias": {
        spanish: "Sesgo de Confirmación",
        patterns: [
          /\b(obviously|unquestionably|no doubt|everyone knows|cannot be wrong)\b/gi,
          /\b(obviamente|es obvio|sin duda|todos saben|no hay duda|no puede estar mal)\b/gi,
          /\b(disregard|ignore|dismiss)\b.*\b(counterevidence|contradict|alternative|opposing)\b/gi,
          /\b(ignorar|descartar|rechazar|despreciar)\b.*\b(pruebas|evidencia|contraria|opuesta|alternativa)\b/gi,
          /\b(only proves|proves my point|confirms what i already knew)\b/gi,
          /\b(solo demuestra|prueba mi punto|confirma lo que ya sab[ií]a)\b/gi
        ],
        explanation: "Sobrevaloración selectiva de evidencias que confirman la hipótesis previa, descartando pruebas en contra.",
        severity: "high"
      },
      "Anchoring Bias": {
        spanish: "Sesgo de Anclaje",
        patterns: [
          /\b(initial price|first number|starting figure|original estimate)\b.*\b(must be close to|dictates|anchors)\b/gi,
          /\b(precio inicial|primera cifra|n[uú]mero inicial|primer precio|estimaci[oó]n original)\b.*\b(debe estar cerca|dicta|ancla)\b/gi,
          /\b(since they asked for|started at)\b.*\b(we should offer around)\b/gi,
          /\b(como pidieron|empez[oó] en)\b.*\b(debemos ofrecer cerca)\b/gi
        ],
        explanation: "Dependencia desmedida de la primera información o número recibido para juzgar estimaciones posteriores.",
        severity: "medium"
      },
      "Sunk Cost Fallacy": {
        spanish: "Falacia del Costo Hundido",
        patterns: [
          /\b(invested too much|spent so much time|already paid for|too late to turn back)\b/gi,
          /\b(ya invert[ií] demasiado|ya gast[eé] mucho|mucho tiempo invertido|demasiado dinero|muy tarde para volver atr[aá]s)\b/gi,
          /\b(can't stop now after|poured millions into|wasted if we quit)\b/gi,
          /\b(no podemos parar ahora|le metimos millones|ser[ií]a una p[eé]rdida dejarlo|desperdicio si lo dejamos)\b/gi
        ],
        explanation: "Justificar continuar un proyecto o inversión fallida solo por los recursos ya gastados que no se recuperarán.",
        severity: "high"
      },
      "Availability Heuristic": {
        spanish: "Heurística de Disponibilidad",
        patterns: [
          /\b(i just saw|in the news yesterday|happened to my friend|comes to mind immediately)\b/gi,
          /\b(acabo de ver|sali[oó] en las noticias ayer|le pas[oó] a un amigo|se me viene a la mente|lo primero que pienso)\b/gi,
          /\b(everyone is talking about|so common nowadays because of this one case)\b/gi,
          /\b(todo el mundo habla de esto|es muy com[uú]n por este caso)\b/gi
        ],
        explanation: "Sobrestimar la probabilidad de un suceso solo porque es el primer ejemplo emocional o reciente que recordamos.",
        severity: "medium"
      },
      "Framing Effect": {
        spanish: "Efecto Encuadre",
        patterns: [
          /\b(only look at the downside|ignore the gain|framed solely as a loss)\b/gi,
          /\b(solo mira la p[eé]rdida|ignora la ganancia|presentado como p[eé]rdida)\b/gi,
          /\b(100% loss vs 0% gain|pure disaster|desastre total|pura p[eé]rdida)\b/gi,
          /\b(seg[uú]n como se diga cambia todo|depending on how you word it)\b/gi
        ],
        explanation: "Tomar conclusiones divergentes sobre la misma realidad según se presente como ganancia o como pérdida.",
        severity: "medium"
      },
      "Catastrophizing": {
        spanish: "Pensamiento Catastrófico",
        patterns: [
          /\b(ruined|end of the world|everything is broken|total disaster|nothing will ever work|hopeless failure)\b/gi,
          /\b(arruinado|fin del mundo|todo est[aá] roto|desastre total|nada volver[aá] a funcionar|fracaso irremediable|se acab[oó] todo)\b/gi,
          /\b(we are completely doomed|irreparable damage|da[nñ]o irreparable|estamos condenados)\b/gi
        ],
        explanation: "Magnificar las probabilidades negativas proyectando catástrofes irreversibles sin justificación empírica.",
        severity: "high"
      },
      "All-or-Nothing Thinking": {
        spanish: "Pensamiento Todo o Nada / Polarizado",
        patterns: [
          /\b(either completely|all or nothing|total failure or complete perfection|never works|always fails|no middle ground)\b/gi,
          /\b(o todo o nada|blanco o negro|fracaso total o perfecci[oó]n absoluta|nunca funciona|siempre falla|no hay t[eé]rmino medio)\b/gi,
          /\b(if it's not perfect it's worthless|si no es perfecto no sirve para nada)\b/gi
        ],
        explanation: "Interpretar situaciones en extremos absolutos, rechazando el progreso incremental o los matices.",
        severity: "high"
      },
      "Overconfidence Bias": {
        spanish: "Sesgo de Sobreconfianza / Dunning-Kruger",
        patterns: [
          /\b(100% guaranteed|impossible to fail|i know everything about|zero chance of error|we don't need tests|no need to test)\b/gi,
          /\b(100% garantizado|imposible fallar|lo s[eé] todo sobre|cero posibilidad de error|no necesitamos pruebas|no hace falta probar)\b/gi,
          /\b(flawless plan|foolproof|plan perfecto|a prueba de fallos)\b/gi
        ],
        explanation: "Sobreestimación injustificada del propio conocimiento o probabilidad de éxito, ignorando la incertidumbre sistémica.",
        severity: "high"
      },
      "Fundamental Attribution Bias": {
        spanish: "Sesgo de Atribución / Culpabilización",
        patterns: [
          /\b(entirely their fault|they are incompetent|lazy team|bad engineer|because of his personality)\b/gi,
          /\b(toda su culpa|son incompetentes|equipo mediocre|mal ingeniero|por su culpa|incompetencia de ellos)\b/gi,
          /\b(they just don't care|no les importa nada|falta de compromiso)\b/gi
        ],
        explanation: "Atribuir fallas sistémicas o de proceso a defectos individuales o incompetencia de personas específicas.",
        severity: "medium"
      },
      "Outcome Bias": {
        spanish: "Sesgo de Resultado / Falacia del Jugador",
        patterns: [
          /\b(it worked out so it was right|since nothing bad happened, the risk was justified|we got lucky, so the process is fine)\b/gi,
          /\b(como sali[oó] bien, fue la decisi[oó]n correcta|no pas[oó] nada malo, as[ií] que el riesgo vali[oó] la pena|tuvimos suerte, as[ií] que el proceso est[aá] bien)\b/gi,
          /\b(the ends justify the shortcut|el resultado justifica el atajo)\b/gi
        ],
        explanation: "Juzgar la calidad de una decisión únicamente por su desenlace final fortuito y no por el rigor previo.",
        severity: "medium"
      }
    };

    const POSITIVE_LEXICON = new Set(["effective", "optimal", "rigorous", "evidence", "proven", "benefit", "solution", "validated", "constructive", "systematic", "accurate", "resilient", "efectivo", "optimo", "óptimo", "riguroso", "evidencia", "probado", "beneficio", "solucion", "solución", "validado", "constructivo", "sistematico", "sistemático", "preciso", "resiliente", "valioso", "seguro", "consistente", "exitoso"]);
    const NEGATIVE_LEXICON = new Set(["catastrophic", "failure", "hopeless", "impossible", "ruined", "disaster", "terrible", "toxic", "regret", "fault", "useless", "panic", "catastrofico", "catastrófico", "fracaso", "imposible", "arruinado", "desastre", "terrible", "toxico", "tóxico", "arrepentimiento", "culpa", "inutil", "inútil", "panico", "pánico", "peligro", "perdida", "pérdida", "arruinar"]);
    const BURNOUT_TOKENS = new Set(["exhausted", "burnout", "overwhelmed", "drowning", "unbearable", "unsustainable", "collapse", "breaking point", "drained", "suffocating", "agotado", "agotada", "colapso", "abrumado", "abrumada", "insostenible", "limite", "límite", "quemado", "quemada", "asfixiante", "fundido"]);
    const SAFETY_TOKENS = new Set(["together", "collaborate", "team", "support", "learn", "improve", "transparent", "growth", "respect", "feedback", "shared", "constructive", "juntos", "colaborar", "equipo", "apoyo", "aprender", "mejorar", "transparente", "crecimiento", "respeto", "retroalimentacion", "retroalimentación", "compartido"]);

    const SAMPLES = {
      1: "This outage is a total disaster! Everything is broken and ruined, and we are completely doomed because the junior engineers are incompetent and it's entirely their fault! I am completely exhausted and this is urgent, fix it asap!",
      2: "We have already invested too much into this legacy architecture to turn back now. It would be wasted if we quit. Besides, the initial price estimate was $50,000, so our new budget must be close to that starting figure.",
      3: "This algorithmic solution is 100% guaranteed and impossible to fail. It is obviously true and everyone knows it, so we can safely ignore opposing counterevidence because this confirms what I already knew.",
      4: "The experimental data indicates a 12% improvement in latency under controlled load conditions. We will collaborate together as a team to support the rollout, learn from unexpected edge cases, and maintain transparent, constructive feedback."
    };

    function loadSample(id) {
      document.getElementById('inputText').value = SAMPLES[id];
      runEvaluation();
    }

    function switchTab(tabId) {
      document.querySelectorAll('.tab-btn').forEach(b => b.classList.remove('active'));
      document.querySelectorAll('.tab-content').forEach(c => c.classList.remove('active'));
      event.target.classList.add('active');
      document.getElementById(tabId).classList.add('active');
    }

    function runEvaluation() {
      const text = document.getElementById('inputText').value.trim();
      if (!text || text.length < 5) {
        alert("Por favor ingresa un texto con al menos 5 caracteres.");
        return;
      }
      const t0 = performance.now();

      // Analyze Biases
      const detectedBiases = [];
      const radarScores = {};
      Object.keys(BIAS_DEFINITIONS).forEach(k => radarScores[k] = 0.0);

      for (const [biasName, def] of Object.entries(BIAS_DEFINITIONS)) {
        let matched = [];
        for (const regex of def.patterns) {
          const found = text.match(regex);
          if (found) {
            matched.push(...found.map(m => m.trim().toLowerCase()));
          }
        }
        if (matched.length > 0) {
          const uniqueTriggers = Array.from(new Set(matched));
          const confidence = Math.min(0.60 + (uniqueTriggers.length * 0.15), 0.98);
          detectedBiases.push({
            bias_name: biasName,
            spanish_name: def.spanish,
            severity: def.severity,
            confidence_score: Math.round(confidence * 100) / 100,
            explanation: def.explanation,
            matched_patterns: uniqueTriggers
          });
          const weight = def.severity === 'high' ? 1.0 : 0.7;
          radarScores[biasName] = Math.round(confidence * weight * 100) / 100;
        }
      }

      // Calculate Psychometrics
      const words = text.toLowerCase().match(/\b\w+\b/g) || [];
      const totalWords = Math.max(words.length, 1);
      const sentences = text.split(/[.!?]+/).filter(s => s.trim().length > 0);
      const totalSentences = Math.max(sentences.length, 1);

      const avgSentenceLen = totalWords / totalSentences;
      const longWords = words.filter(w => w.length > 6);
      const complexRatio = longWords.length / totalWords;
      const rawLoad = (avgSentenceLen * 2.0) + (complexRatio * 70.0);
      const cogLoad = Math.round(Math.min(Math.max(rawLoad, 5.0), 100.0) * 10) / 10;

      let posCount = words.filter(w => POSITIVE_LEXICON.has(w)).length;
      let negCount = words.filter(w => NEGATIVE_LEXICON.has(w)).length;
      let valence = (posCount === 0 && negCount === 0) ? 0.0 : Math.round(((posCount - negCount) / (posCount + negCount + 1)) * 100) / 100;

      let burnoutMatches = words.filter(w => BURNOUT_TOKENS.has(w)).length;
      let urgencyMarkers = (text.match(/\b(asap|urgent|urgente|ya mismo|ahora|inmediato|immediately)\b/gi) || []).length;
      let exclamationCount = (text.match(/!/g) || []).length;
      let rawBurnout = (burnoutMatches * 25.0) + (urgencyMarkers * 15.0) + (exclamationCount * 5.0);
      if (valence < -0.3) rawBurnout += 20.0;
      if (cogLoad > 70.0) rawBurnout += 15.0;
      const burnoutRisk = Math.round(Math.min(Math.max(rawBurnout, 5.0), 100.0) * 10) / 10;

      let safetyMatches = words.filter(w => SAFETY_TOKENS.has(w)).length;
      let blameMarkers = (text.match(/\b(culpa|culpable|culpar|incompetente|fault|blame|blaming|incompetent|lazy)\b/gi) || []).length;
      let rawSafety = 70.0 + (safetyMatches * 10.0) - (blameMarkers * 25.0) + (valence * 20.0);
      if (burnoutRisk > 50.0) rawSafety -= (burnoutRisk - 50.0) * 0.5;
      const safetyScore = Math.round(Math.min(Math.max(rawSafety, 0.0), 100.0) * 10) / 10;

      const consistency = Math.round((Math.max(0.4, 0.85 - (detectedBiases.length * 0.12))) * 100);

      // Alignment Risk
      const highSevCount = detectedBiases.filter(b => b.severity === 'high').length;
      let riskLevel = "nominal";
      if (highSevCount >= 2 || burnoutRisk > 75.0 || safetyScore < 30.0) {
        riskLevel = "critical";
      } else if (detectedBiases.length > 0 || burnoutRisk > 45.0) {
        riskLevel = "moderate";
      }

      // Interventions
      const interventions = [];
      detectedBiases.forEach(b => {
        if (b.bias_name === "Confirmation Bias") interventions.push("Protocolo Red-Teaming: Generar 3 hipótesis disconfirmatorias antes de dar por buena la conclusión.");
        if (b.bias_name === "Sunk Cost Fallacy") interventions.push("Desacoplamiento Prospectivo: Evaluar el ROI futuro asumiendo que los costos pasados son irrecuperables ($0).");
        if (b.bias_name === "Anchoring Bias") interventions.push("Estimación Base Cero: Recalcular cantidades principales de forma ciega e independiente de cifras iniciales.");
        if (b.bias_name === "Availability Heuristic") interventions.push("Contraste Empírico: Contrastar la anécdota reciente contra distribuciones estadísticas de frecuencia real.");
        if (b.bias_name === "Framing Effect") interventions.push("Encuadre Simétrico: Presentar simultáneamente matrices de ganancias vs pérdidas para neutralizar el sesgo.");
        if (b.bias_name === "Catastrophizing") interventions.push("Des-catastrofización Cognitiva: Cuantificar la probabilidad real del peor escenario (P<5%) y aislar planes de contención.");
        if (b.bias_name === "All-or-Nothing Thinking") interventions.push("Pensamiento de Continuo: Reemplazar dicotomías binarias ('éxito/fracaso total') por hitos de progreso medibles (0-100%).");
        if (b.bias_name === "Overconfidence Bias") interventions.push("Pre-Mortem Técnico: Asumir que el sistema falló dentro de 6 meses y documentar exhaustivamente las causas posibles.");
        if (b.bias_name === "Fundamental Attribution Bias") interventions.push("Análisis Causa-Raíz Sin Culpa (Blameless): Enfocar la investigación en la arquitectura y herramientas, no en personas.");
        if (b.bias_name === "Outcome Bias") interventions.push("Separación Proceso vs Azar: Auditar el valor esperado (EV) y el rigor metodológico al momento de tomar la decisión.");
      });

      if (burnoutRisk > 65.0) interventions.push("Alerta de Fatiga & Urgencia Ocupacional: Pausar el flujo de mensajes inmediatos e instaurar canales asíncronos.");
      if (safetyScore < 45.0) interventions.push("Protocolo de Seguridad Psicológica: Reencuadrar críticas destructivas en preguntas de aprendizaje colaborativo.");
      if (interventions.length === 0) interventions.push("El argumento se mantiene objetivo, equilibrado y dentro de parámetros nominales de razonamiento.");

      const counterfactual = detectedBiases.length > 0
        ? `Reconsidera este planteamiento desde la perspectiva de hipótesis nula: Asume que lo opuesto a tu conclusión es verdad e indica qué 3 pruebas empíricas concretas necesitarías para validarlo.`
        : null;

      // Red-Teaming Directive
      let redTeamingText = "";
      if (detectedBiases.length > 0) {
        const biasStr = detectedBiases.map(b => b.bias_name).join(", ");
        redTeamingText = `[SYSTEM DIRECTIVE: REASONING ALIGNMENT & DEBIASING]\n` +
          `The evaluated reasoning trace exhibits tendencies toward: ${biasStr}.\n\n` +
          `Execution Directive:\n` +
          `1. Adopt an adversarial, null-hypothesis stance against the current conclusion.\n` +
          `2. Formulate 3 empirical counter-arguments disproving the main premise.\n` +
          `3. Decouple past sunken resources and formulate expected value (EV) with explicit confidence intervals.\n` +
          `4. Re-write the conclusion using balanced, non-catastrophic framing.`;
      } else {
        redTeamingText = `[SYSTEM DIRECTIVE]\nNo critical cognitive distortions detected. Maintain evidence-based, structured reasoning.`;
      }

      const t1 = performance.now();
      const latency = Math.round((t1 - t0) * 10) / 10;

      // --- RENDER OUTPUTS ---
      document.getElementById('latencyText').innerText = latency + " ms";

      const riskBanner = document.getElementById('riskBanner');
      riskBanner.className = `risk-banner risk-${riskLevel}`;
      document.getElementById('riskTitle').innerText = riskLevel.toUpperCase();
      document.getElementById('riskIcon').innerText = riskLevel === 'nominal' ? '✅' : (riskLevel === 'moderate' ? '⚠️' : '🚨');

      document.getElementById('valCogLoad').innerText = cogLoad;
      document.getElementById('valBurnout').innerText = burnoutRisk;
      document.getElementById('valBurnout').style.color = burnoutRisk > 60 ? '#ef4444' : (burnoutRisk > 35 ? '#f59e0b' : '#10b981');
      document.getElementById('valSafety').innerText = safetyScore + "%";
      document.getElementById('valSafety').style.color = safetyScore < 45 ? '#ef4444' : (safetyScore < 70 ? '#f59e0b' : '#10b981');
      document.getElementById('valValence').innerText = (valence > 0 ? "+" : "") + valence.toFixed(2);
      document.getElementById('valConsistency').innerText = consistency + "%";

      // Render Biases List
      const container = document.getElementById('biasesContainer');
      container.innerHTML = "";
      if (detectedBiases.length === 0) {
        container.innerHTML = `
          <div style="background:#f0fdf4; border:1px solid #bbf7d0; border-radius:10px; padding:16px; color:#166534; font-size:13px;">
            🎉 <b>No se detectaron sesgos críticos.</b> El razonamiento exhibe coherencia lógica y ponderación balanceada de evidencia.
          </div>
        `;
      } else {
        detectedBiases.forEach(b => {
          const triggers = b.matched_patterns.map(p => `<span class="trigger-tag">"${p}"</span>`).join("");
          container.innerHTML += `
            <div class="bias-card ${b.severity === 'high' ? 'high-sev' : ''}">
              <div class="bias-card-header">
                <span class="bias-name">🧠 ${b.bias_name} (${b.spanish_name})</span>
                <div>
                  <span class="sev-tag ${b.severity}">${b.severity}</span>
                  <span style="font-size:12px; color:#64748b; margin-left:6px;">Confianza: ${Math.round(b.confidence_score * 100)}%</span>
                </div>
              </div>
              <div class="bias-desc">${b.explanation}</div>
              <div style="font-size:12px; color:#64748b;"><b>Frases delatadas:</b> ${triggers}</div>
            </div>
          `;
        });
      }

      // Render Mitigation
      const mitList = document.getElementById('mitigationList');
      mitList.innerHTML = interventions.map(i => `<li>${i}</li>`).join("");
      const cfBox = document.getElementById('counterfactualBox');
      if (counterfactual) {
        cfBox.style.display = "block";
        cfBox.innerHTML = `<b>🔄 Ejercicio Contrafactual Sugerido:</b> <i>"${counterfactual}"</i>`;
      } else {
        cfBox.style.display = "none";
      }

      // Red-Teaming
      document.getElementById('redTeamingBox').innerText = redTeamingText;

      // JSON output
      const fullJson = {
        evaluation_id: "eval_" + Math.random().toString(36).substring(2, 10),
        author_type: document.querySelector('input[name="author_type"]:checked').value,
        timestamp: new Date().toISOString(),
        alignment_risk: riskLevel,
        psychometrics: {
          cognitive_load_index: cogLoad,
          burnout_risk_index: burnoutRisk,
          psychological_safety_score: safetyScore,
          emotional_valence: valence,
          logical_consistency: consistency / 100
        },
        biases_detected: detectedBiases,
        mitigation: {
          interventions: interventions,
          counterfactual_prompt: counterfactual,
          red_teaming_directive: redTeamingText
        },
        latency_ms: latency
      };
      document.getElementById('jsonBox').innerText = JSON.stringify(fullJson, null, 2);

      // Render Plotly Radar
      renderRadarChart(radarScores);
    }

    function renderRadarChart(scores) {
      const categories = [
        "Confirmación", "Anclaje", "Costo Hundido", "Disponibilidad", "Encuadre",
        "Catastrofismo", "Todo o Nada", "Sobreconfianza", "Culpabilización", "Resultado"
      ];
      const keys = [
        "Confirmation Bias", "Anchoring Bias", "Sunk Cost Fallacy", "Availability Heuristic", "Framing Effect",
        "Catastrophizing", "All-or-Nothing Thinking", "Overconfidence Bias", "Fundamental Attribution Bias", "Outcome Bias"
      ];
      const values = keys.map(k => scores[k] || 0.0);
      categories.push(categories[0]);
      values.push(values[0]);

      const data = [{
        type: 'scatterpolar',
        r: values,
        theta: categories,
        fill: 'toself',
        fillcolor: 'rgba(79, 70, 229, 0.3)',
        line: { color: '#4f46e5', width: 2 },
        marker: { size: 5, color: '#4338ca' }
      }];

      const layout = {
        polar: {
          radialaxis: {
            visible: true,
            range: [0, 1.0],
            tickvals: [0.25, 0.5, 0.75, 1.0],
            ticktext: ['25%', '50%', '75%', '100%'],
            linecolor: '#cbd5e1',
            gridcolor: '#e2e8f0'
          },
          angularaxis: {
            linecolor: '#cbd5e1',
            gridcolor: '#e2e8f0',
            tickfont: { size: 10 }
          }
        },
        showlegend: false,
        margin: { l: 30, r: 30, t: 25, b: 25 },
        paper_bgcolor: 'rgba(0,0,0,0)',
        plot_bgcolor: 'rgba(0,0,0,0)'
      };

      Plotly.newPlot('radarPlot', data, layout, { responsive: true, displayModeBar: false });
    }

    function copyRedTeaming() {
      const text = document.getElementById('redTeamingBox').innerText;
      navigator.clipboard.writeText(text).then(() => {
        alert("¡Prompt copiado al portapapeles! Puedes pegarlo en ChatGPT o Claude.");
      });
    }

    // Auto-run on startup with Sample 1
    window.addEventListener('DOMContentLoaded', () => {
      loadSample(1);
    });
  </script>
</body>
</html>