{
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  "item": {
    "slug": "ft-reader",
    "name": "Financial Times Deep Reader",
    "source": "tencent",
    "type": "skill",
    "category": "内容创作",
    "sourceUrl": "https://clawhub.ai/zhouziyue233/ft-reader",
    "canonicalUrl": "https://clawhub.ai/zhouziyue233/ft-reader",
    "targetPlatform": "OpenClaw"
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    "sourcePlatform": "tencent",
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    "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": {
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      "steps": [
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        "Paste one of the prompts below and point your agent at the extracted folder."
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      "prompts": [
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          "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."
        }
      ]
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      "checkedAt": "2026-04-23T16:43:11.935Z",
      "expiresAt": "2026-04-30T16:43:11.935Z",
      "httpStatus": 200,
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        "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/ft-reader"
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        "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."
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    "downloadPageUrl": "https://openagent3.xyz/downloads/ft-reader",
    "agentPageUrl": "https://openagent3.xyz/skills/ft-reader/agent",
    "manifestUrl": "https://openagent3.xyz/skills/ft-reader/agent.json",
    "briefUrl": "https://openagent3.xyz/skills/ft-reader/agent.md"
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  "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."
      }
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  "documentation": {
    "source": "clawhub",
    "primaryDoc": "SKILL.md",
    "sections": [
      {
        "title": "Financial Times Deep Reader (ft-reader)",
        "body": "Use this skill to perform deep, structured, and bilingual analysis of top articles from Financial Times (ft.com). This skill automates login, article selection, and high-quality summarization suitable for academic and professional use."
      },
      {
        "title": "Capabilities",
        "body": "Automated Access: Logs into FT.com using stored credentials via Browser tool.\nStrategic Selection: Identifies \"Most Read\" based on user preference.\nBilingual Synthesis: Provides high-fidelity English-Chinese summaries with a focus on core arguments.\nAcademic Rigor: Extracts specific data, quotes, and important charts in the article."
      },
      {
        "title": "Configuration & Credentials",
        "body": "Browser Profile: Use openclaw profile to maintain session persistence.\nCredentials:\n\nUser: xxxxxx\nPass: xxxxxx"
      },
      {
        "title": "Phase 1: Authentication & Navigation",
        "body": "Open https://www.ft.com/login.\nEnter email and password.\nNavigate to the homepage or a specific section requested by the user."
      },
      {
        "title": "Phase 2: Content Extraction",
        "body": "Use evaluate to identify the top N articles from the homepage (targeting .o-teaser__heading or most-read sections).\n\n\nFor each target article:\n\n\nNavigate to the article URL.\n\n\nUse evaluate with the following JavaScript to extract clean content:\n() => {\n  const title = document.querySelector('h1')?.innerText;\n  const standfirst = document.querySelector('div[class*=\"standfirst\"]')?.innerText;\n  const paragraphs = Array.from(document.querySelectorAll('div[class*=\"article-body\"] p, article p'))\n    .map(p => p.innerText.trim())\n    .filter(text => text.length > 0);\n  return { title, summary: standfirst, content: paragraphs.join('\\n\\n') };\n}"
      },
      {
        "title": "Phase 3: Analysis & Reporting",
        "body": "For each article, generate a report (around 600 words) using the following structure:\n\nTitle (Bilingual)\nCore Opinion (Bilingual)\nArguments (Bilingual)\nConclusion (Bilingual)"
      },
      {
        "title": "Constraints",
        "body": "Style: Professional, academic, and fluff-free (follow SOUL.md).\nLanguage: Always provide both English and Chinese translations for technical terms and core ideas.\nIndependent Reading: Treat each article as a standalone piece unless cross-analysis is requested.\nToken Management: If many articles are requested, split the delivery into multiple turns to avoid truncation."
      },
      {
        "title": "Usage Examples",
        "body": "\"Lulu, use ft-reader to analyze the top 3 Most Read articles from today.\"\n\"Perform a deep dive into the top story on FT regarding AI productivity using the ft-reader skill.\""
      }
    ],
    "body": "Financial Times Deep Reader (ft-reader)\n\nUse this skill to perform deep, structured, and bilingual analysis of top articles from Financial Times (ft.com). This skill automates login, article selection, and high-quality summarization suitable for academic and professional use.\n\nCapabilities\nAutomated Access: Logs into FT.com using stored credentials via Browser tool.\nStrategic Selection: Identifies \"Most Read\" based on user preference.\nBilingual Synthesis: Provides high-fidelity English-Chinese summaries with a focus on core arguments.\nAcademic Rigor: Extracts specific data, quotes, and important charts in the article.\nConfiguration & Credentials\nBrowser Profile: Use openclaw profile to maintain session persistence.\nCredentials:\nUser: xxxxxx\nPass: xxxxxx\nWorkflow (Mandatory Steps)\nPhase 1: Authentication & Navigation\nOpen https://www.ft.com/login.\nEnter email and password.\nNavigate to the homepage or a specific section requested by the user.\nPhase 2: Content Extraction\n\nUse evaluate to identify the top N articles from the homepage (targeting .o-teaser__heading or most-read sections).\n\nFor each target article:\n\nNavigate to the article URL.\n\nUse evaluate with the following JavaScript to extract clean content:\n\n() => {\n  const title = document.querySelector('h1')?.innerText;\n  const standfirst = document.querySelector('div[class*=\"standfirst\"]')?.innerText;\n  const paragraphs = Array.from(document.querySelectorAll('div[class*=\"article-body\"] p, article p'))\n    .map(p => p.innerText.trim())\n    .filter(text => text.length > 0);\n  return { title, summary: standfirst, content: paragraphs.join('\\n\\n') };\n}\n\nPhase 3: Analysis & Reporting\n\nFor each article, generate a report (around 600 words) using the following structure:\n\nTitle (Bilingual)\nCore Opinion (Bilingual)\nArguments (Bilingual)\nConclusion (Bilingual)\nConstraints\nStyle: Professional, academic, and fluff-free (follow SOUL.md).\nLanguage: Always provide both English and Chinese translations for technical terms and core ideas.\nIndependent Reading: Treat each article as a standalone piece unless cross-analysis is requested.\nToken Management: If many articles are requested, split the delivery into multiple turns to avoid truncation.\nUsage Examples\n\"Lulu, use ft-reader to analyze the top 3 Most Read articles from today.\"\n\"Perform a deep dive into the top story on FT regarding AI productivity using the ft-reader skill.\""
  },
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    "sourceLabel": "tencent",
    "provenanceUrl": "https://clawhub.ai/zhouziyue233/ft-reader",
    "publisherUrl": "https://clawhub.ai/zhouziyue233/ft-reader",
    "owner": "zhouziyue233",
    "version": "1.0.0",
    "license": null,
    "verificationStatus": "Indexed source record"
  },
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    "downloadUrl": "https://openagent3.xyz/downloads/ft-reader",
    "agentUrl": "https://openagent3.xyz/skills/ft-reader/agent",
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}