Analizar sostenibilidad corporativa y bienestar animal con automatización de IA

Intermedio

Este es unMarket Research, AI Summarizationflujo de automatización del dominio deautomatización que contiene 15 nodos.Utiliza principalmente nodos como Set, Merge, Aggregate, ExecuteWorkflow, ChainLlm. Analizar sostenibilidad y bienestar animal de la empresa con OpenRouter AI y investigación multisource

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Copie la siguiente configuración JSON en n8n para importar y usar este flujo de trabajo
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              "message": "# Comprehensive Animal Welfare and Sustainability Analysis\n\nYou are a specialized research analyst with expertise in corporate sustainability, environmental impact assessment, and animal welfare evaluation. Your task is to conduct a thorough investigation of a given organization's practices and policies, synthesizing information from multiple authoritative sources to provide an evidence-based assessment.\n\n## Research Methodology\n\n**Source Prioritization (use in this order):**\n1. **Primary Official Sources**: Company sustainability reports, annual reports, ESG disclosures, policy documents\n2. **Regulatory Filings**: SEC 10-K forms, CDP submissions, GRI reports, SASB disclosures\n3. **Third-Party Certifications**: B-Corp, LEED, ISO standards, industry-specific certifications\n4. **Independent Ratings**: CDP Climate Scores, MSCI ESG ratings, Sustainalytics, Bloomberg ESG\n5. **NGO Evaluations**: PETA ratings, Greenpeace scorecards, WWF assessments, industry watchdog reports\n6. **Academic Sources**: Peer-reviewed research, university sustainability rankings, scholarly assessments\n7. **Credible Media**: Financial press (Bloomberg, Reuters), specialized sustainability publications, investigative journalism\n\n**Research Standards:**\n- Prioritize data from the last 24 months for maximum relevance\n- Cross-verify claims across at least 2-3 independent sources\n- Distinguish between aspirational commitments and demonstrated results\n- Look for quantitative metrics and progress tracking\n- Identify potential greenwashing by comparing marketing claims to actual performance\n- Note any controversies, lawsuits, or NGO criticisms\n- Check for industry context and peer benchmarking data\n\n## Data Collection Framework\n\n### Organizational Classification\nDetermine the primary category: university, corporation, government agency, NGO, or other. Consider hybrid organizations and note if classification is complex.\n\n### Environmental Assessment\n**Policy Analysis:**\n- Comprehensive climate commitments (net-zero targets, science-based targets)\n- Environmental management systems and certifications\n- Renewable energy adoption and targets\n- Circular economy and waste reduction initiatives\n- Water stewardship and biodiversity conservation efforts\n- Supply chain environmental standards\n\n**Carbon Footprint Evaluation:**\n- Most recent GHG emissions data (Scope 1, 2, and 3 when available)\n- Year-over-year emission trends and reduction achievements\n- Carbon intensity metrics relative to revenue/activity\n- Renewable energy percentage and expansion plans\n- Carbon offset strategies and quality of offset projects\n- Link to the most comprehensive and recent emissions report\n\n### Animal Welfare Investigation\n**Policy Framework:**\n- Formal animal welfare policies and ethical standards\n- Laboratory animal use guidelines and 3Rs implementation (Replace, Reduce, Refine)\n- Food procurement standards for animal products\n- Supply chain animal welfare requirements and auditing\n- Partnerships with animal welfare organizations\n- Employee/student education on animal welfare issues\n\n**Implementation Evidence:**\n- Third-party animal welfare certifications in supply chain\n- Concrete actions taken to improve animal treatment\n- Investment in animal testing alternatives\n- Transparency in animal welfare reporting\n- Response to animal welfare controversies or campaigns\n\n### Vegan Accommodation Analysis\n**Food Service Evaluation:**\n- Availability, variety, and nutritional quality of plant-based options\n- Proportion of menu items that are vegan/vegetarian\n- Labeling clarity and allergen information\n- Pricing parity with animal-based options\n- Staff training on vegan options and dietary restrictions\n- Institutional policies supporting plant-based eating\n- Special events and vegan food initiatives\n\n### Certification and Recognition Audit\nIdentify and verify all relevant certifications:\n- Environmental: ISO 14001, LEED, Energy Star, Carbon Trust\n- Animal welfare: Certified Humane, Animal Welfare Approved, SPCA Certified\n- Food service: Plant Based Foods Association, Vegan Society certification\n- General sustainability: B-Corp, Fair Trade, Rainforest Alliance\n- Industry-specific: STARS (universities), GRESB (real estate), etc.\n\n### Third-Party Rating Analysis\n**Major Rating Systems:**\n- **CDP Climate Change**: Score (A, A-, B, etc.) and key performance areas\n- **MSCI ESG Rating**: Overall rating and component scores for environmental and social factors\n- **Sustainalytics ESG Risk Rating**: Risk level and risk management assessment\n- **Other Specialized Assessments**: Industry rankings, NGO scorecards, peer benchmarks\n\n## Scoring Methodology\n\n**Environmental Impact Score (0-100%):**\n- Policy comprehensiveness and ambition (25%)\n- Measurable progress against targets (30%)\n- Third-party validation and transparency (20%)\n- Industry leadership and innovation (15%)\n- Supply chain environmental integration (10%)\n\n**Animal Welfare Score (0-100%):**\n- Policy existence and strength (30%)\n- Implementation evidence and verification (25%)\n- Third-party certifications and audits (20%)\n- Transparency and reporting quality (15%)\n- Response to stakeholder concerns (10%)\n\n**Vegan Accommodation Score (0-100% or \"NA\" if not applicable):**\n- Availability and variety of options (40%)\n- Institutional support and policies (25%)\n- Quality and nutritional adequacy (20%)\n- Accessibility and pricing (15%)\n\n**Overall Scoring:**\n- Calculate weighted average: Environmental (40%), Animal Welfare (35%), Vegan Accommodation (25%)\n- Adjust for industry context and organizational type\n- Grade assignment: A+ (95-100%), A (85-94%), B+ (75-84%), B (65-74%), C+ (55-64%), C (45-54%), D (below 45%)\n\n## Output Requirements\n\nStructure your response exactly as shown in the example schema, ensuring:\n\n1. **Accuracy**: All data points must be verifiable through provided sources\n2. **Recency**: Prioritize the most current information available\n3. **Specificity**: Include concrete metrics, dates, and quantifiable achievements\n4. **Balance**: Present both strengths and areas for improvement\n5. **Context**: Consider industry standards and peer performance\n6. **Source Quality**: Ensure all URLs are functional and lead directly to relevant information\n\n**Critical Quality Checks:**\n- Verify all URLs are accessible and contain referenced information\n- Ensure numerical scores are justified by evidence presented\n- Cross-check claims against multiple sources when possible\n- Note any significant data gaps or limitations in assessment\n- Flag potential conflicts of interest in sources\n- Distinguish between company claims and independent verification\n\n**Assessment Narrative Guidelines:**\n- Environmental: Focus on measurable impact, policy ambition, and implementation effectiveness\n- Animal Welfare: Evaluate policy comprehensiveness, implementation evidence, and stakeholder responsiveness  \n- Vegan: Assess practical accessibility, institutional support, and quality of offerings\n\nRemember to maintain objectivity while providing actionable insights that help stakeholders understand the organization's true performance in these critical areas."
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      "typeVersion": 3.4
    },
    {
      "id": "1b33808e-39c0-4deb-ba5a-f1e20a00bbaf",
      "name": "OpenRouter Chat Model2",
      "type": "@n8n/n8n-nodes-langchain.lmChatOpenRouter",
      "position": [
        -1200,
        220
      ],
      "parameters": {
        "model": "anthropic/claude-sonnet-4",
        "options": {}
      },
      "credentials": {
        "openRouterApi": {
          "id": "QqgKARn52qQiKICN",
          "name": "OpenRouter account"
        }
      },
      "typeVersion": 1
    },
    {
      "id": "cdedec8e-1fa1-4d96-a6a9-49b2be5c3362",
      "name": "Nota adhesiva",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -1880,
        -360
      ],
      "parameters": {
        "width": 360,
        "height": 380,
        "content": "## **Company Sustainability Assessment Workflow**\n- **Purpose**: Automated end-to-end analysis of company environmental and animal welfare practices\n- **Input**: Company name (string)\n- **Process**: Multi-source research → Parallel analysis → Combined output\n- **Final Output**: JSON structured data + HTML report with scores, grades, and comprehensive assessment\n- **Use Cases**: ESG screening, investment decisions, consumer guidance, compliance reporting\n- **Validates**: Non-empty company name input\n- **Passes to**: Research Agent"
      },
      "typeVersion": 1
    },
    {
      "id": "5718d343-4671-4e30-a441-110c431a39dc",
      "name": "Nota adhesiva1",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -1800,
        320
      ],
      "parameters": {
        "width": 280,
        "height": 420,
        "content": "## 🔍 Research Agent\n**Multi-Source Data Collection Hub**\n- **PREREQUISITE**: Download and install subworkflow from: \n  https://creators.n8n.io/workflows/5588\n- **Setup**: Save as workflow in your n8n instance, then link this node to that flow\n- **Function**: Executes comprehensive research using specialized tools:\n  - OpenPaws database for animal welfare data\n  - Web search for sustainability reports\n  - Web scraping for policy documents\n  - Social media monitoring for recent developments"
      },
      "typeVersion": 1
    },
    {
      "id": "70268511-761f-4b71-851c-3d72852c91c4",
      "name": "Nota adhesiva2",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -1400,
        -460
      ],
      "parameters": {
        "width": 300,
        "height": 280,
        "content": "## 📊 LLM Chain 1: Structured Scoring\n**JSON Analysis Engine**\n- Uses: Schema-based prompt\n- Processes: Research data → structured scores\n- Generates: JSON with percentages, grades, ratings\n- Output: Standardized assessment object"
      },
      "typeVersion": 1
    },
    {
      "id": "b6e27b2c-4284-43ed-a8ce-5b0ef81a573c",
      "name": "Nota adhesiva3",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -1380,
        600
      ],
      "parameters": {
        "width": 300,
        "height": 260,
        "content": "## 📄 LLM Chain 2: HTML Report\n**Report Generation Engine**\n- Uses: Report formatting prompt\n- Processes: Research data → narrative analysis\n- Generates: Formatted HTML report with insights\n- Output: Human-readable assessment document"
      },
      "typeVersion": 1
    },
    {
      "id": "ca9473e0-ec30-4933-84c6-4198ce719041",
      "name": "Nota adhesiva4",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -900,
        -140
      ],
      "parameters": {
        "width": 360,
        "height": 200,
        "content": "## 🔗 Merge & Aggregate\n**Final Output Combiner**\n- Receives: JSON scores + HTML report\n- Combines: Structured data with narrative\n- Validates: Data consistency between outputs\n- Outputs: Complete assessment package\n- Format: Combined JSON + embedded HTML"
      },
      "typeVersion": 1
    }
  ],
  "active": false,
  "pinData": {},
  "settings": {
    "executionOrder": "v1"
  },
  "versionId": "d48262e1-1bae-4c40-ae78-892c9ba72b67",
  "connections": {
    "Merge": {
      "main": [
        [
          {
            "node": "Aggregate",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Aggregate": {
      "main": [
        [
          {
            "node": "Set Output",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "eb75ff84-bc0a-42e1-bc49-d570a735ab8b": {
      "main": [
        [
          {
            "node": "Merge",
            "type": "main",
            "index": 1
          }
        ]
      ]
    },
    "56364f47-02a4-4c56-9f09-5f253ed00859": {
      "ai_languageModel": [
        [
          {
            "node": "f20d64d2-07ae-41a7-8ce1-b28ea70ff9a6",
            "type": "ai_languageModel",
            "index": 0
          },
          {
            "node": "02616056-a3a2-4d34-b812-c06b3314733b",
            "type": "ai_languageModel",
            "index": 0
          }
        ]
      ]
    },
    "1b33808e-39c0-4deb-ba5a-f1e20a00bbaf": {
      "ai_languageModel": [
        [
          {
            "node": "eb75ff84-bc0a-42e1-bc49-d570a735ab8b",
            "type": "ai_languageModel",
            "index": 0
          }
        ]
      ]
    },
    "Trigger Research Agent": {
      "main": [
        [
          {
            "node": "f20d64d2-07ae-41a7-8ce1-b28ea70ff9a6",
            "type": "main",
            "index": 0
          },
          {
            "node": "eb75ff84-bc0a-42e1-bc49-d570a735ab8b",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "02616056-a3a2-4d34-b812-c06b3314733b": {
      "ai_outputParser": [
        [
          {
            "node": "f20d64d2-07ae-41a7-8ce1-b28ea70ff9a6",
            "type": "ai_outputParser",
            "index": 0
          }
        ]
      ]
    },
    "f20d64d2-07ae-41a7-8ce1-b28ea70ff9a6": {
      "main": [
        [
          {
            "node": "Merge",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "f5f6363a-3691-403b-a026-541d625e3f8e": {
      "main": [
        [
          {
            "node": "Trigger Research Agent",
            "type": "main",
            "index": 0
          }
        ]
      ]
    }
  }
}
Preguntas frecuentes

¿Cómo usar este flujo de trabajo?

Copie el código de configuración JSON de arriba, cree un nuevo flujo de trabajo en su instancia de n8n y seleccione "Importar desde JSON", pegue la configuración y luego modifique la configuración de credenciales según sea necesario.

¿En qué escenarios es adecuado este flujo de trabajo?

Intermedio - Investigación de mercado, Resumen de IA

¿Es de pago?

Este flujo de trabajo es completamente gratuito, puede importarlo y usarlo directamente. Sin embargo, tenga en cuenta que los servicios de terceros utilizados en el flujo de trabajo (como la API de OpenAI) pueden requerir un pago por su cuenta.

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Información del flujo de trabajo
Nivel de dificultad
Intermedio
Número de nodos15
Categoría2
Tipos de nodos9
Descripción de la dificultad

Adecuado para usuarios con experiencia intermedia, flujos de trabajo de complejidad media con 6-15 nodos

Autor
Open Paws

Open Paws

@openpaws

Open Paws is a nonprofit building open-source AI tools to accelerate animal advocacy. We create machine learning models and automation workflows trained on real-world campaign data to help end industrial animal exploitation. All tools are free, transparent, and designed for real-world impact.

Enlaces externos
Ver en n8n.io

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Categorías

Categorías: 34