{
  "schemaVersion": "1.0",
  "item": {
    "slug": "perplexity-research",
    "name": "perplexity-research",
    "source": "tencent",
    "type": "skill",
    "category": "开发工具",
    "sourceUrl": "https://clawhub.ai/hushenglang/perplexity-research",
    "canonicalUrl": "https://clawhub.ai/hushenglang/perplexity-research",
    "targetPlatform": "OpenClaw"
  },
  "install": {
    "downloadMode": "redirect",
    "downloadUrl": "/downloads/perplexity-research",
    "sourceDownloadUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=perplexity-research",
    "sourcePlatform": "tencent",
    "targetPlatform": "OpenClaw",
    "installMethod": "Manual import",
    "extraction": "Extract archive",
    "prerequisites": [
      "OpenClaw"
    ],
    "packageFormat": "ZIP package",
    "includedAssets": [
      "README.md",
      "SKILL.md",
      "examples.md",
      "manifest.yaml",
      "reference.md",
      "scripts/perplexity_client.py"
    ],
    "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. Then review README.md for any prerequisites, environment setup, or post-install checks. 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. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run."
        }
      ]
    },
    "sourceHealth": {
      "source": "tencent",
      "status": "healthy",
      "reason": "direct_download_ok",
      "recommendedAction": "download",
      "checkedAt": "2026-05-07T17:22:31.273Z",
      "expiresAt": "2026-05-14T17:22:31.273Z",
      "httpStatus": 200,
      "finalUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=afrexai-annual-report",
      "contentType": "application/zip",
      "probeMethod": "head",
      "details": {
        "probeUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=afrexai-annual-report",
        "contentDisposition": "attachment; filename=\"afrexai-annual-report-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/perplexity-research"
    },
    "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/perplexity-research",
    "agentPageUrl": "https://openagent3.xyz/skills/perplexity-research/agent",
    "manifestUrl": "https://openagent3.xyz/skills/perplexity-research/agent.json",
    "briefUrl": "https://openagent3.xyz/skills/perplexity-research/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. Then review README.md for any prerequisites, environment setup, or post-install checks. 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. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run."
      }
    ]
  },
  "documentation": {
    "source": "clawhub",
    "primaryDoc": "SKILL.md",
    "sections": [
      {
        "title": "Perplexity Research",
        "body": "Research assistant powered by Perplexity Agent API with web search and reasoning capabilities."
      },
      {
        "title": "Quick Start",
        "body": "The Perplexity client is available at scripts/perplexity_client.py in this skill folder.\n\nDefault model: openai/gpt-5.2 (GPT latest)\n\nKey capabilities:\n\nWeb search for current information\nHigh reasoning effort for deep analysis\nMulti-model comparison\nStreaming responses\nCost tracking"
      },
      {
        "title": "1. Deep Research Query",
        "body": "Use for comprehensive analysis requiring web search and reasoning:\n\n# Import from skill scripts folder\nimport sys\nfrom pathlib import Path\nsys.path.insert(0, str(Path(__file__).parent / \"scripts\"))\nfrom perplexity_client import PerplexityClient\n\nclient = PerplexityClient()\nresult = client.research_query(\n    query=\"Your research question here\",\n    model=\"openai/gpt-5.2\",\n    reasoning_effort=\"high\",\n    max_tokens=2000\n)\n\nif \"error\" not in result:\n    print(result[\"answer\"])\n    print(f\"Tokens: {result['tokens']}, Cost: ${result['cost']}\")"
      },
      {
        "title": "2. Quick Web Search",
        "body": "Use for time-sensitive or current information:\n\nresult = client.search_query(\n    query=\"Your question about current events\",\n    model=\"openai/gpt-5.2\",\n    max_tokens=1000\n)"
      },
      {
        "title": "3. Model Comparison",
        "body": "Use when output quality is critical:\n\nresults = client.compare_models(\n    query=\"Your question\",\n    models=[\"openai/gpt-5.2\", \"anthropic/claude-3-5-sonnet\", \"google/gemini-2.0-flash\"],\n    max_tokens=300\n)\n\nfor result in results:\n    if \"error\" not in result:\n        print(f\"\\n{result['model']}: {result['answer']}\")"
      },
      {
        "title": "4. Streaming for Long Responses",
        "body": "Use for better UX with lengthy analysis:\n\nclient.stream_query(\n    query=\"Your question\",\n    model=\"openai/gpt-5.2\",\n    use_search=True,\n    max_tokens=2000\n)"
      },
      {
        "title": "Research Workflow",
        "body": "When conducting research:\n\nInitial exploration: Use research_query() with web search enabled\nValidate findings: Compare key insights across models with compare_models()\nDeep dive: Use streaming for detailed analysis on specific aspects\nCost-aware: Monitor token usage and costs in results"
      },
      {
        "title": "Model Selection",
        "body": "Default: openai/gpt-5.2 (Latest GPT model)\n\nAlternative models:\n\nanthropic/claude-3-5-sonnet - Strong reasoning, balanced performance\ngoogle/gemini-2.0-flash - Fast, cost-effective\nmeta/llama-3.3-70b - Open source alternative\n\nSwitch models based on:\n\nQuality needs (GPT-5.2 for best results)\nSpeed requirements (Gemini Flash for quick answers)\nCost constraints (compare costs in results)"
      },
      {
        "title": "Reasoning Effort Levels",
        "body": "Control analysis depth with reasoning_effort:\n\n\"low\" - Quick answers, minimal reasoning\n\"medium\" - Balanced reasoning (default for most queries)\n\"high\" - Deep analysis, comprehensive research (recommended for research)"
      },
      {
        "title": "Environment Setup",
        "body": "Ensure PERPLEXITY_API_KEY is set:\n\nexport PERPLEXITY_API_KEY='your_api_key_here'\n\nOr create .env file in the skill's scripts/ directory:\n\nPERPLEXITY_API_KEY=your_api_key_here"
      },
      {
        "title": "Error Handling",
        "body": "All methods return error information:\n\nresult = client.research_query(\"Your question\")\n\nif \"error\" in result:\n    print(f\"Error: {result['error']}\")\n    # Handle error appropriately\nelse:\n    # Process successful result\n    print(result[\"answer\"])"
      },
      {
        "title": "Cost Optimization",
        "body": "Use max_tokens to limit response length\nStart with lower reasoning effort, increase if needed\nUse search_query() instead of research_query() for simpler questions\nMonitor costs via result[\"cost\"] field"
      },
      {
        "title": "Investment Research",
        "body": "client = PerplexityClient()\n\n# Market analysis\nresult = client.research_query(\n    query=\"Analyze recent developments in AI chip market and key competitors\",\n    reasoning_effort=\"high\"\n)\n\n# Company deep dive\nresult = client.search_query(\n    query=\"Latest earnings report for NVIDIA Q4 2025\"\n)\n\n# Multi-model validation\nresults = client.compare_models(\n    query=\"What are the biggest risks in the semiconductor industry?\",\n    models=[\"openai/gpt-5.2\", \"anthropic/claude-3-5-sonnet\"]\n)"
      },
      {
        "title": "Trend Analysis",
        "body": "# Current trends with web search\nresult = client.research_query(\n    query=\"Emerging trends in sustainable investing and ESG adoption rates\",\n    reasoning_effort=\"high\",\n    max_tokens=2000\n)\n\n# Stream for real-time updates\nclient.stream_query(\n    query=\"Latest developments in quantum computing commercialization\",\n    use_search=True\n)"
      },
      {
        "title": "Multi-Turn Research",
        "body": "# Build context across multiple queries\nmessages = [\n    {\"role\": \"user\", \"content\": \"What is the current state of fusion energy?\"},\n    {\"role\": \"assistant\", \"content\": \"...previous response...\"},\n    {\"role\": \"user\", \"content\": \"Which companies are leading in this space?\"}\n]\n\nresult = client.conversation(\n    messages=messages,\n    use_search=True\n)"
      },
      {
        "title": "Best Practices",
        "body": "Default to research_query() for most research tasks - it combines web search with high reasoning\nUse streaming for user-facing applications to show progress\nCompare models for critical decisions or when quality is paramount\nSet reasonable max_tokens - 1000 for summaries, 2000+ for deep analysis\nTrack costs - access via result[\"cost\"] and result[\"tokens\"]\nHandle errors gracefully - always check for \"error\" key in results"
      },
      {
        "title": "API Reference",
        "body": "See reference.md for complete API documentation, or scripts/perplexity_client.py for:\n\nFull method signatures\nAdditional parameters\nCLI usage examples\nImplementation details"
      },
      {
        "title": "Command Line Usage",
        "body": "Run from the skill directory:\n\n# Research mode\npython scripts/perplexity_client.py research \"Your question\"\n\n# Web search\npython scripts/perplexity_client.py search \"Your question\"\n\n# Streaming\npython scripts/perplexity_client.py stream \"Your question\"\n\n# Compare models\npython scripts/perplexity_client.py compare \"Your question\""
      }
    ],
    "body": "Perplexity Research\n\nResearch assistant powered by Perplexity Agent API with web search and reasoning capabilities.\n\nQuick Start\n\nThe Perplexity client is available at scripts/perplexity_client.py in this skill folder.\n\nDefault model: openai/gpt-5.2 (GPT latest)\n\nKey capabilities:\n\nWeb search for current information\nHigh reasoning effort for deep analysis\nMulti-model comparison\nStreaming responses\nCost tracking\nCommon Research Patterns\n1. Deep Research Query\n\nUse for comprehensive analysis requiring web search and reasoning:\n\n# Import from skill scripts folder\nimport sys\nfrom pathlib import Path\nsys.path.insert(0, str(Path(__file__).parent / \"scripts\"))\nfrom perplexity_client import PerplexityClient\n\nclient = PerplexityClient()\nresult = client.research_query(\n    query=\"Your research question here\",\n    model=\"openai/gpt-5.2\",\n    reasoning_effort=\"high\",\n    max_tokens=2000\n)\n\nif \"error\" not in result:\n    print(result[\"answer\"])\n    print(f\"Tokens: {result['tokens']}, Cost: ${result['cost']}\")\n\n2. Quick Web Search\n\nUse for time-sensitive or current information:\n\nresult = client.search_query(\n    query=\"Your question about current events\",\n    model=\"openai/gpt-5.2\",\n    max_tokens=1000\n)\n\n3. Model Comparison\n\nUse when output quality is critical:\n\nresults = client.compare_models(\n    query=\"Your question\",\n    models=[\"openai/gpt-5.2\", \"anthropic/claude-3-5-sonnet\", \"google/gemini-2.0-flash\"],\n    max_tokens=300\n)\n\nfor result in results:\n    if \"error\" not in result:\n        print(f\"\\n{result['model']}: {result['answer']}\")\n\n4. Streaming for Long Responses\n\nUse for better UX with lengthy analysis:\n\nclient.stream_query(\n    query=\"Your question\",\n    model=\"openai/gpt-5.2\",\n    use_search=True,\n    max_tokens=2000\n)\n\nResearch Workflow\n\nWhen conducting research:\n\nInitial exploration: Use research_query() with web search enabled\nValidate findings: Compare key insights across models with compare_models()\nDeep dive: Use streaming for detailed analysis on specific aspects\nCost-aware: Monitor token usage and costs in results\nModel Selection\n\nDefault: openai/gpt-5.2 (Latest GPT model)\n\nAlternative models:\n\nanthropic/claude-3-5-sonnet - Strong reasoning, balanced performance\ngoogle/gemini-2.0-flash - Fast, cost-effective\nmeta/llama-3.3-70b - Open source alternative\n\nSwitch models based on:\n\nQuality needs (GPT-5.2 for best results)\nSpeed requirements (Gemini Flash for quick answers)\nCost constraints (compare costs in results)\nReasoning Effort Levels\n\nControl analysis depth with reasoning_effort:\n\n\"low\" - Quick answers, minimal reasoning\n\"medium\" - Balanced reasoning (default for most queries)\n\"high\" - Deep analysis, comprehensive research (recommended for research)\nEnvironment Setup\n\nEnsure PERPLEXITY_API_KEY is set:\n\nexport PERPLEXITY_API_KEY='your_api_key_here'\n\n\nOr create .env file in the skill's scripts/ directory:\n\nPERPLEXITY_API_KEY=your_api_key_here\n\nError Handling\n\nAll methods return error information:\n\nresult = client.research_query(\"Your question\")\n\nif \"error\" in result:\n    print(f\"Error: {result['error']}\")\n    # Handle error appropriately\nelse:\n    # Process successful result\n    print(result[\"answer\"])\n\nCost Optimization\nUse max_tokens to limit response length\nStart with lower reasoning effort, increase if needed\nUse search_query() instead of research_query() for simpler questions\nMonitor costs via result[\"cost\"] field\nIntegration Examples\nInvestment Research\nclient = PerplexityClient()\n\n# Market analysis\nresult = client.research_query(\n    query=\"Analyze recent developments in AI chip market and key competitors\",\n    reasoning_effort=\"high\"\n)\n\n# Company deep dive\nresult = client.search_query(\n    query=\"Latest earnings report for NVIDIA Q4 2025\"\n)\n\n# Multi-model validation\nresults = client.compare_models(\n    query=\"What are the biggest risks in the semiconductor industry?\",\n    models=[\"openai/gpt-5.2\", \"anthropic/claude-3-5-sonnet\"]\n)\n\nTrend Analysis\n# Current trends with web search\nresult = client.research_query(\n    query=\"Emerging trends in sustainable investing and ESG adoption rates\",\n    reasoning_effort=\"high\",\n    max_tokens=2000\n)\n\n# Stream for real-time updates\nclient.stream_query(\n    query=\"Latest developments in quantum computing commercialization\",\n    use_search=True\n)\n\nMulti-Turn Research\n# Build context across multiple queries\nmessages = [\n    {\"role\": \"user\", \"content\": \"What is the current state of fusion energy?\"},\n    {\"role\": \"assistant\", \"content\": \"...previous response...\"},\n    {\"role\": \"user\", \"content\": \"Which companies are leading in this space?\"}\n]\n\nresult = client.conversation(\n    messages=messages,\n    use_search=True\n)\n\nBest Practices\nDefault to research_query() for most research tasks - it combines web search with high reasoning\nUse streaming for user-facing applications to show progress\nCompare models for critical decisions or when quality is paramount\nSet reasonable max_tokens - 1000 for summaries, 2000+ for deep analysis\nTrack costs - access via result[\"cost\"] and result[\"tokens\"]\nHandle errors gracefully - always check for \"error\" key in results\nAPI Reference\n\nSee reference.md for complete API documentation, or scripts/perplexity_client.py for:\n\nFull method signatures\nAdditional parameters\nCLI usage examples\nImplementation details\nCommand Line Usage\n\nRun from the skill directory:\n\n# Research mode\npython scripts/perplexity_client.py research \"Your question\"\n\n# Web search\npython scripts/perplexity_client.py search \"Your question\"\n\n# Streaming\npython scripts/perplexity_client.py stream \"Your question\"\n\n# Compare models\npython scripts/perplexity_client.py compare \"Your question\""
  },
  "trust": {
    "sourceLabel": "tencent",
    "provenanceUrl": "https://clawhub.ai/hushenglang/perplexity-research",
    "publisherUrl": "https://clawhub.ai/hushenglang/perplexity-research",
    "owner": "hushenglang",
    "version": "2.0.0",
    "license": null,
    "verificationStatus": "Indexed source record"
  },
  "links": {
    "detailUrl": "https://openagent3.xyz/skills/perplexity-research",
    "downloadUrl": "https://openagent3.xyz/downloads/perplexity-research",
    "agentUrl": "https://openagent3.xyz/skills/perplexity-research/agent",
    "manifestUrl": "https://openagent3.xyz/skills/perplexity-research/agent.json",
    "briefUrl": "https://openagent3.xyz/skills/perplexity-research/agent.md"
  }
}