{
  "schemaVersion": "1.0",
  "item": {
    "slug": "gpt-analyzer",
    "name": "Gpt Analyzer",
    "source": "tencent",
    "type": "skill",
    "category": "AI 智能",
    "sourceUrl": "https://clawhub.ai/raghulpasupathi/gpt-analyzer",
    "canonicalUrl": "https://clawhub.ai/raghulpasupathi/gpt-analyzer",
    "targetPlatform": "OpenClaw"
  },
  "install": {
    "downloadMode": "redirect",
    "downloadUrl": "/downloads/gpt-analyzer",
    "sourceDownloadUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=gpt-analyzer",
    "sourcePlatform": "tencent",
    "targetPlatform": "OpenClaw",
    "installMethod": "Manual import",
    "extraction": "Extract archive",
    "prerequisites": [
      "OpenClaw"
    ],
    "packageFormat": "ZIP package",
    "includedAssets": [
      "SKILL.md"
    ],
    "primaryDoc": "SKILL.md",
    "quickSetup": [
      "Download the package from Yavira.",
      "Extract the archive and review SKILL.md first.",
      "Import or place the package into your OpenClaw setup."
    ],
    "agentAssist": {
      "summary": "Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.",
      "steps": [
        "Download the package from Yavira.",
        "Extract it into a folder your agent can access.",
        "Paste one of the prompts below and point your agent at the extracted folder."
      ],
      "prompts": [
        {
          "label": "New install",
          "body": "I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete."
        },
        {
          "label": "Upgrade existing",
          "body": "I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run."
        }
      ]
    },
    "sourceHealth": {
      "source": "tencent",
      "status": "healthy",
      "reason": "direct_download_ok",
      "recommendedAction": "download",
      "checkedAt": "2026-04-30T16:55:25.780Z",
      "expiresAt": "2026-05-07T16:55:25.780Z",
      "httpStatus": 200,
      "finalUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=network",
      "contentType": "application/zip",
      "probeMethod": "head",
      "details": {
        "probeUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=network",
        "contentDisposition": "attachment; filename=\"network-1.0.0.zip\"",
        "redirectLocation": null,
        "bodySnippet": null
      },
      "scope": "source",
      "summary": "Source download looks usable.",
      "detail": "Yavira can redirect you to the upstream package for this source.",
      "primaryActionLabel": "Download for OpenClaw",
      "primaryActionHref": "/downloads/gpt-analyzer"
    },
    "validation": {
      "installChecklist": [
        "Use the Yavira download entry.",
        "Review SKILL.md after the package is downloaded.",
        "Confirm the extracted package contains the expected setup assets."
      ],
      "postInstallChecks": [
        "Confirm the extracted package includes the expected docs or setup files.",
        "Validate the skill or prompts are available in your target agent workspace.",
        "Capture any manual follow-up steps the agent could not complete."
      ]
    },
    "downloadPageUrl": "https://openagent3.xyz/downloads/gpt-analyzer",
    "agentPageUrl": "https://openagent3.xyz/skills/gpt-analyzer/agent",
    "manifestUrl": "https://openagent3.xyz/skills/gpt-analyzer/agent.json",
    "briefUrl": "https://openagent3.xyz/skills/gpt-analyzer/agent.md"
  },
  "agentAssist": {
    "summary": "Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.",
    "steps": [
      "Download the package from Yavira.",
      "Extract it into a folder your agent can access.",
      "Paste one of the prompts below and point your agent at the extracted folder."
    ],
    "prompts": [
      {
        "label": "New install",
        "body": "I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete."
      },
      {
        "label": "Upgrade existing",
        "body": "I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run."
      }
    ]
  },
  "documentation": {
    "source": "clawhub",
    "primaryDoc": "SKILL.md",
    "sections": [
      {
        "title": "GPT Analyzer",
        "body": "Specialized detection for GPT-generated content with model-specific pattern recognition."
      },
      {
        "title": "Implementation",
        "body": "/**\n * Analyze text for GPT-specific patterns and fingerprints\n * @param {string} text - Text to analyze\n * @param {object} options - Configuration options\n * @returns {object} Analysis result with model identification\n */\nasync function analyzeGPTContent(text, options = {}) {\n  const {\n    detectVersion = true,\n    checkWatermarks = true,\n    minConfidence = 0.7\n  } = options;\n\n  const normalizedText = text.toLowerCase();\n  const wordCount = text.split(/\\s+/).length;\n\n  // GPT-specific phrases (stronger indicators)\n  const gptPhrases = {\n    'gpt-4': [\n      'delve into', 'landscape of', 'realm of', 'it\\'s important to note',\n      'multifaceted', 'nuanced', 'comprehensive', 'holistic approach'\n    ],\n    'gpt-3.5': [\n      'as an ai language model', 'i don\\'t have personal', 'i apologize for',\n      'certainly', 'absolutely', 'furthermore', 'moreover'\n    ],\n    'common': [\n      'it\\'s worth noting', 'keep in mind', 'in conclusion',\n      'to summarize', 'in summary', 'navigate the', 'tapestry of'\n    ]\n  };\n\n  // Model fingerprinting\n  let gpt4Score = 0;\n  let gpt35Score = 0;\n  let commonScore = 0;\n  const foundPhrases = [];\n\n  // Check GPT-4 specific patterns\n  for (const phrase of gptPhrases['gpt-4']) {\n    if (normalizedText.includes(phrase)) {\n      gpt4Score += 0.2;\n      foundPhrases.push({ phrase, model: 'gpt-4' });\n    }\n  }\n\n  // Check GPT-3.5 specific patterns\n  for (const phrase of gptPhrases['gpt-3.5']) {\n    if (normalizedText.includes(phrase)) {\n      gpt35Score += 0.2;\n      foundPhrases.push({ phrase, model: 'gpt-3.5' });\n    }\n  }\n\n  // Check common GPT patterns\n  for (const phrase of gptPhrases['common']) {\n    if (normalizedText.includes(phrase)) {\n      commonScore += 0.1;\n      foundPhrases.push({ phrase, model: 'common' });\n    }\n  }\n\n  // Structure analysis\n  const hasNumberedLists = (text.match(/\\n\\d+\\./g) || []).length >= 3;\n  const hasBulletPoints = (text.match(/\\n[•\\-\\*]/g) || []).length >= 3;\n  const structureScore = (hasNumberedLists || hasBulletPoints) ? 0.15 : 0;\n\n  // Sentence uniformity\n  const sentences = text.split(/[.!?]+/).filter(s => s.trim());\n  const avgLength = sentences.reduce((sum, s) => sum + s.length, 0) / sentences.length;\n  const variance = sentences.reduce((sum, s) => sum + Math.pow(s.length - avgLength, 2), 0) / sentences.length;\n  const uniformityScore = variance < 500 ? 0.1 : 0;\n\n  // Calculate confidence\n  const totalScore = gpt4Score + gpt35Score + commonScore + structureScore + uniformityScore;\n  const confidence = Math.min(totalScore, 1.0);\n\n  // Determine model\n  let detectedModel = 'unknown';\n  if (gpt4Score > gpt35Score && gpt4Score > 0) {\n    detectedModel = 'gpt-4';\n  } else if (gpt35Score > gpt4Score && gpt35Score > 0) {\n    detectedModel = 'gpt-3.5';\n  } else if (commonScore > 0) {\n    detectedModel = 'gpt-family';\n  }\n\n  const isGPT = confidence >= minConfidence;\n\n  return {\n    isGPT,\n    confidence: Math.round(confidence * 100),\n    detectedModel: isGPT ? detectedModel : 'not-gpt',\n    scores: {\n      gpt4: Math.round(gpt4Score * 100) / 100,\n      gpt35: Math.round(gpt35Score * 100) / 100,\n      common: Math.round(commonScore * 100) / 100,\n      structure: Math.round(structureScore * 100) / 100,\n      uniformity: Math.round(uniformityScore * 100) / 100\n    },\n    indicators: {\n      foundPhrases: foundPhrases.length,\n      hasStructure: hasNumberedLists || hasBulletPoints,\n      avgSentenceLength: Math.round(avgLength),\n      sentenceVariance: Math.round(variance)\n    },\n    recommendation: confidence >= 0.85 ? 'Very likely GPT' :\n                     confidence >= 0.70 ? 'Likely GPT' :\n                     confidence >= 0.50 ? 'Possibly GPT' :\n                     'Unlikely GPT or human-written'\n  };\n}\n\n// Export for OpenClaw\nmodule.exports = {\n  analyzeGPTContent\n};"
      },
      {
        "title": "Usage",
        "body": "const result = await skills.gptAnalyzer.analyzeGPTContent(text);\n\nif (result.isGPT) {\n  console.log(`GPT detected: ${result.detectedModel} (${result.confidence}% confidence)`);\n}"
      },
      {
        "title": "Configuration",
        "body": "{\n  \"detectVersion\": true,\n  \"minConfidence\": 0.7\n}"
      }
    ],
    "body": "GPT Analyzer\n\nSpecialized detection for GPT-generated content with model-specific pattern recognition.\n\nImplementation\n/**\n * Analyze text for GPT-specific patterns and fingerprints\n * @param {string} text - Text to analyze\n * @param {object} options - Configuration options\n * @returns {object} Analysis result with model identification\n */\nasync function analyzeGPTContent(text, options = {}) {\n  const {\n    detectVersion = true,\n    checkWatermarks = true,\n    minConfidence = 0.7\n  } = options;\n\n  const normalizedText = text.toLowerCase();\n  const wordCount = text.split(/\\s+/).length;\n\n  // GPT-specific phrases (stronger indicators)\n  const gptPhrases = {\n    'gpt-4': [\n      'delve into', 'landscape of', 'realm of', 'it\\'s important to note',\n      'multifaceted', 'nuanced', 'comprehensive', 'holistic approach'\n    ],\n    'gpt-3.5': [\n      'as an ai language model', 'i don\\'t have personal', 'i apologize for',\n      'certainly', 'absolutely', 'furthermore', 'moreover'\n    ],\n    'common': [\n      'it\\'s worth noting', 'keep in mind', 'in conclusion',\n      'to summarize', 'in summary', 'navigate the', 'tapestry of'\n    ]\n  };\n\n  // Model fingerprinting\n  let gpt4Score = 0;\n  let gpt35Score = 0;\n  let commonScore = 0;\n  const foundPhrases = [];\n\n  // Check GPT-4 specific patterns\n  for (const phrase of gptPhrases['gpt-4']) {\n    if (normalizedText.includes(phrase)) {\n      gpt4Score += 0.2;\n      foundPhrases.push({ phrase, model: 'gpt-4' });\n    }\n  }\n\n  // Check GPT-3.5 specific patterns\n  for (const phrase of gptPhrases['gpt-3.5']) {\n    if (normalizedText.includes(phrase)) {\n      gpt35Score += 0.2;\n      foundPhrases.push({ phrase, model: 'gpt-3.5' });\n    }\n  }\n\n  // Check common GPT patterns\n  for (const phrase of gptPhrases['common']) {\n    if (normalizedText.includes(phrase)) {\n      commonScore += 0.1;\n      foundPhrases.push({ phrase, model: 'common' });\n    }\n  }\n\n  // Structure analysis\n  const hasNumberedLists = (text.match(/\\n\\d+\\./g) || []).length >= 3;\n  const hasBulletPoints = (text.match(/\\n[•\\-\\*]/g) || []).length >= 3;\n  const structureScore = (hasNumberedLists || hasBulletPoints) ? 0.15 : 0;\n\n  // Sentence uniformity\n  const sentences = text.split(/[.!?]+/).filter(s => s.trim());\n  const avgLength = sentences.reduce((sum, s) => sum + s.length, 0) / sentences.length;\n  const variance = sentences.reduce((sum, s) => sum + Math.pow(s.length - avgLength, 2), 0) / sentences.length;\n  const uniformityScore = variance < 500 ? 0.1 : 0;\n\n  // Calculate confidence\n  const totalScore = gpt4Score + gpt35Score + commonScore + structureScore + uniformityScore;\n  const confidence = Math.min(totalScore, 1.0);\n\n  // Determine model\n  let detectedModel = 'unknown';\n  if (gpt4Score > gpt35Score && gpt4Score > 0) {\n    detectedModel = 'gpt-4';\n  } else if (gpt35Score > gpt4Score && gpt35Score > 0) {\n    detectedModel = 'gpt-3.5';\n  } else if (commonScore > 0) {\n    detectedModel = 'gpt-family';\n  }\n\n  const isGPT = confidence >= minConfidence;\n\n  return {\n    isGPT,\n    confidence: Math.round(confidence * 100),\n    detectedModel: isGPT ? detectedModel : 'not-gpt',\n    scores: {\n      gpt4: Math.round(gpt4Score * 100) / 100,\n      gpt35: Math.round(gpt35Score * 100) / 100,\n      common: Math.round(commonScore * 100) / 100,\n      structure: Math.round(structureScore * 100) / 100,\n      uniformity: Math.round(uniformityScore * 100) / 100\n    },\n    indicators: {\n      foundPhrases: foundPhrases.length,\n      hasStructure: hasNumberedLists || hasBulletPoints,\n      avgSentenceLength: Math.round(avgLength),\n      sentenceVariance: Math.round(variance)\n    },\n    recommendation: confidence >= 0.85 ? 'Very likely GPT' :\n                     confidence >= 0.70 ? 'Likely GPT' :\n                     confidence >= 0.50 ? 'Possibly GPT' :\n                     'Unlikely GPT or human-written'\n  };\n}\n\n// Export for OpenClaw\nmodule.exports = {\n  analyzeGPTContent\n};\n\nUsage\nconst result = await skills.gptAnalyzer.analyzeGPTContent(text);\n\nif (result.isGPT) {\n  console.log(`GPT detected: ${result.detectedModel} (${result.confidence}% confidence)`);\n}\n\nConfiguration\n{\n  \"detectVersion\": true,\n  \"minConfidence\": 0.7\n}"
  },
  "trust": {
    "sourceLabel": "tencent",
    "provenanceUrl": "https://clawhub.ai/raghulpasupathi/gpt-analyzer",
    "publisherUrl": "https://clawhub.ai/raghulpasupathi/gpt-analyzer",
    "owner": "raghulpasupathi",
    "version": "1.0.0",
    "license": null,
    "verificationStatus": "Indexed source record"
  },
  "links": {
    "detailUrl": "https://openagent3.xyz/skills/gpt-analyzer",
    "downloadUrl": "https://openagent3.xyz/downloads/gpt-analyzer",
    "agentUrl": "https://openagent3.xyz/skills/gpt-analyzer/agent",
    "manifestUrl": "https://openagent3.xyz/skills/gpt-analyzer/agent.json",
    "briefUrl": "https://openagent3.xyz/skills/gpt-analyzer/agent.md"
  }
}