Descubrir puntos finales ocultos de la API del sitio web utilizando expresiones regulares e IA

• •Pueden requerirse credenciales de autenticación para la API de destino

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  "name": "Discover Hidden Website API Endpoints Using Regex and AI",
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        "content": "# 1. API Endpoints Extraction With a Predefined Regex  \n* Paste the **URL** of the target website into the **URL** field of the **Configuration** node.\n* Fetch the **HTML page** of the site and extract the **URLs of the JS files**.  \n* Keep relevant JS files with potential **API endpoints** (e.g. local, bundled, CDN sources). You can customise the logic to your needs: add or remove URL conditions.\n* Load JS content from kept URLs: convert references to JS files into properly formatted, complete URLs.\n* Apply predefined **regex** to extract API endpoints. \n* Keep JS files with enough URLs **(>4 endpoints)**. Assume that JS files with fewer URLs do not contain relevant API endpoints. Discard them to send only the necessary data to the LLM.\n* Clean up the extracted URLs by removing duplicated items. \n\n"
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        "schemaType": "manual",
        "inputSchema": "{\n\t\"type\": \"object\",\n\t\"properties\": {\n\t\t\"n8nexpression\": {\n\t\t\t\"type\": \"A valid regex to extract clean API endpoints. Example {{ $json.data.match([PUT REGEX HERE])}}\"\n\t\t}\n\t}\n}"
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            {
              "name": "query.n8nexpression"
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            {
              "name": "Filename"
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          ]
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      "typeVersion": 1.1
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      "id": "e30b3def-09c1-42c8-afbb-c1e0421146fb",
      "name": "Cargar endpoints de referencia",
      "type": "n8n-nodes-base.readWriteFile",
      "position": [
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      "parameters": {
        "options": {},
        "fileSelector": "={{ $json.Filename }}"
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      "typeVersion": 1
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    {
      "id": "5ebb63c2-8a99-47f8-8ea4-87ff2aff674b",
      "name": "Extraer texto de archivo de endpoints",
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      "parameters": {
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        "mode": "combine",
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              "name": "Evaluation results",
              "type": "string",
              "value": "={{ $evaluateExpression($json['query.n8nexpression']) }}"
            }
          ]
        },
        "excludeFields": "data",
        "includeOtherFields": true
      },
      "typeVersion": 3.4
    },
    {
      "id": "3fac22e4-4c2d-4f3e-b644-09ef9904cd26",
      "name": "Combinar análisis inicial de IA y salida del agente de IA",
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      "name": "Preparar datos para extracción con regex",
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      "position": [
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              "type": "string",
              "value": "={{ $json.choices[0].message.content }}"
            },
            {
              "id": "232536f2-4081-43e8-8650-24d5d72a2187",
              "name": "output.n8nexpression",
              "type": "string",
              "value": "={{ !!$json?.output?.n8nexpression ? $json?.output?.n8nexpression : \"\" }}"
            }
          ]
        },
        "includeFields": "output, fileName, Source",
        "includeOtherFields": true
      },
      "typeVersion": 3.4
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    {
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      "name": "Ejecutar regex de LLM",
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              "name": "Endpoints",
              "type": "array",
              "value": "={{ $evaluateExpression($json.output.n8nexpression) }}"
            },
            {
              "id": "fede8667-f874-4200-a3aa-0ce18cbc52ab",
              "name": "LLM",
              "type": "boolean",
              "value": true
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      "name": "Dividir endpoints extraídos por LLM",
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      "parameters": {
        "include": "selectedOtherFields",
        "options": {},
        "fieldToSplitOut": "Endpoints",
        "fieldsToInclude": "LLM, fileName, Source"
      },
      "typeVersion": 1
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    {
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      "name": "Eliminar duplicados de endpoints de LLM",
      "type": "n8n-nodes-base.removeDuplicates",
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      "parameters": {
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      "name": "Combinar endpoints originales y de LLM",
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      "parameters": {
        "mode": "combine",
        "options": {},
        "joinMode": "keepEverything",
        "fieldsToMatchString": "Endpoints, Source"
      },
      "typeVersion": 3.1
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    {
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      "name": "Combinación final con nombre de archivo enriquecido",
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      "parameters": {
        "mode": "combine",
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            "values": {
              "resolveClash": "preferLast",
              "overrideEmpty": true
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          "multipleMatches": "all"
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        "joinMode": "enrichInput1",
        "fieldsToMatchString": "Source"
      },
      "typeVersion": 3.1
    },
    {
      "id": "c6039111-8f99-40d3-9d84-56674b53e74f",
      "name": "Ordenar endpoints combinados",
      "type": "n8n-nodes-base.sort",
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      "parameters": {
        "options": {},
        "sortFieldsUi": {
          "sortField": [
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              "fieldName": "Endpoints"
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          ]
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      "typeVersion": 1
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      "name": "Reordenar campos de salida",
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              "name": "Endpoints",
              "type": "string",
              "value": "={{ $json.Endpoints }}"
            },
            {
              "id": "f6f00797-2d07-49f1-a2df-e044e82acd9a",
              "name": "JS",
              "type": "string",
              "value": "={{ $json.JS }}"
            },
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              "id": "8a5acf2e-f362-4811-95b6-f876dfc6c9d2",
              "name": "Source",
              "type": "string",
              "value": "={{ $json.Source }}"
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              "name": "fileName",
              "type": "string",
              "value": "={{ $json.fileName }}"
            },
            {
              "id": "d8f566c0-8d5e-486b-bfb8-a2c36d2271da",
              "name": "LLM",
              "type": "string",
              "value": "={{ $json.LLM }}"
            }
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      "typeVersion": 3.4
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    {
      "id": "b44b2577-e937-43ea-bdd5-403070caa50e",
      "name": "Exportar resultados de comparación a Excel",
      "type": "n8n-nodes-base.convertToFile",
      "position": [
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      "parameters": {
        "options": {
          "fileName": "api_comparison.xlsx",
          "headerRow": true
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        "operation": "xlsx"
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    {
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      "parameters": {
        "compare": "selectedFields",
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        "fieldsToCompare": "Source, fileName"
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    {
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      "parameters": {
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        "height": 520,
        "content": "# The workflow helps to automatically find hidden API endpoints in web platforms that don't provide public APIs by analysing their JavaScript code.\n ## Modern web applications often use internal API endpoints for frontend-backend communication but don't provide them as documented public APIs. These endpoints are often embedded in JavaScript files and can be extracted for different use cases. When none or few relevant endpoints can be identified, we can still understand the site architecture, routing patterns and internal organisation. \n\n"
      },
      "typeVersion": 1
    },
    {
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      "position": [
        20,
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      ],
      "parameters": {
        "width": 760,
        "height": 140,
        "content": "## Regex extracts all URL-like strings with API-focused filtering\n* Scans the code of each JS file for quoted URL strings\n* Captures relative, absolute URLs & internal routes\n* **Excludes:** static files (.js, .css, .png, .jpg, etc.), short strings (< 3 characters), non-path strings (without '/')\n* Cleans URLs (removes query parameters, slashes)"
      },
      "typeVersion": 1
    },
    {
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      "name": "Nota adhesiva 6",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        20,
        220
      ],
      "parameters": {
        "width": 760,
        "height": 140,
        "content": "## The workflow is best suited for:\n* Knockout.js & Next.js/Nuxt.js applications with embedded API calls \n* SPA platforms that use fetch/axios to communicate with the backend\n* Modern web apps with bundled JavaScript containing endpoint strings\n* Platforms that using standard patterns (/api/, /v*, internal routes, etc.)"
      },
      "typeVersion": 1
    },
    {
      "id": "5dc66bb7-3645-4959-a52e-73658f5fa09e",
      "name": "Nota adhesiva 7",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        780,
        220
      ],
      "parameters": {
        "width": 740,
        "height": 140,
        "content": "## The workflow may not be suited for:\n* Dynamically generated endpoints (runtime build)\n* WebSocket only communication without REST endpoints\n* Fully obfuscated code\n* Server-side only architecture with no client-side API calls "
      },
      "typeVersion": 1
    },
    {
      "id": "8ad235ab-fdb5-4eee-bc5e-b0be081244ef",
      "name": "Nota adhesiva 8",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        500,
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      ],
      "parameters": {
        "color": 3,
        "width": 580,
        "height": 120,
        "content": "## Requirements:\n* The target platform must embed API endpoints in JS as string literals \n* JS files must be accessible and not heavily obfuscated\n* The platform must use standard endpoint patterns (/api/, versioned paths, etc.)"
      },
      "typeVersion": 1
    },
    {
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      "name": "Nota adhesiva 9",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
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      "parameters": {
        "width": 1540,
        "height": 800,
        "content": "# 2. AI-supported API Endpoints Extraction  \n\n"
      },
      "typeVersion": 1
    },
    {
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      "name": "Nota adhesiva 1",
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      "position": [
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      "parameters": {
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        "width": 700,
        "content": "## Stage 2. LLM Regex Generation\n* Feed the analysis results from the 1st LLM to the 2nd LLM via the **Tools Agent**.\n* By default, **Claude 3.7 Sonnet** is used to generate regex.\n* The LLM creates custom regexes based on the analysis of each AI JS file using the Agent's system prompt.  "
      },
      "typeVersion": 1
    },
    {
      "id": "7b1d73c9-c7e1-4b75-8f1e-a08fcd76a577",
      "name": "Nota adhesiva",
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      "position": [
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      "parameters": {
        "color": 4,
        "width": 700,
        "height": 260,
        "content": "## Stage 3. LLM Regex Validation \n* The **Agent** calls the **Validation tool** which checks the LLM regex accuracy. \n* The **Validation tool** calls a sub-workflow to test the LLM regex against the reference endpoint files prepared by the 1st LLM.\n* The **Validation tool** extracts the endpoints from the reference file using the LLM regex and sends the evaluation results back to the **Agent**.\n* The **Agent** compares the LLM regex evaluation results with the incoming data from the 1st LLM. \n* If the results do not match, the LLM creates a new regex and the Agent repeats the validation process.  \n* The **Agent** can iterate up to 4 times till it finds the optimal regex. "
      },
      "typeVersion": 1
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    {
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      "name": "Nota adhesiva 10",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        2160,
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      "parameters": {
        "color": 6,
        "width": 700,
        "height": 200,
        "content": "## Stage 1. Initial AI Analysis\n* Send JS file content to **LLM** via the **OpenRouter API**.\n* LLM performs a comprehensive JS code analysis for each kept file to identify API endpoints with detailed descriptions, HTTP methods and query parameters. The default LLM is **Gemini 2.5 Pro-Preview**. You can specify a different model available through the OpenRouter API in the JSON body of the **HTTP Request node**.\n* **Loop Over Items** node saves the analysis result on the n8n instance drive one by one. This is necessary for the subsequent step when the **AI agent** validates the regex using the **Tool** node.\n"
      },
      "typeVersion": 1
    },
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      "id": "ea7dd713-a97d-4a29-86d6-885a3c24a5cf",
      "name": "Nota adhesiva 11",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        3060,
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      ],
      "parameters": {
        "color": 3,
        "width": 520,
        "height": 180,
        "content": "## Warning!\n* The **Agent** does not generate regex in 100% of situations. Sometimes it gives the message **‘The Agent stopped due to max iterations’** or creates a regex which does not retrieve the relevant endpoints. \n* For these situations, we have the results of a fixed predefined regex and a detailed description of each API endpoint found by the 1st LLM.   "
      },
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      "name": "Nota adhesiva 13",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        2900,
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      ],
      "parameters": {
        "color": 6,
        "width": 760,
        "content": "## Stage 4. LLM Regex Execution\n* After the **Agent** node, merge the results of the 1st LLM and the **Agent** output. \n* Execute a final LLM regex on the output of the 1st LLM similar to how the **Validation tool** did it.\n* Remove duplicates and clean up extracted endpoints."
      },
      "typeVersion": 1
    },
    {
      "id": "99853c4d-20d0-4465-ad6a-c97d1b83cb1e",
      "name": "Nota adhesiva 12",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
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      "parameters": {
        "color": 3,
        "width": 860,
        "height": 200,
        "content": "# Final comparison \n* Combine endpoints from both extraction approaches (predefined regex + LLM extraction).\n* Add filename references to the combined result.\n* Sort and organise results for side-by-side comparison.\n* Export merged comparison results to .xlsx."
      },
      "typeVersion": 1
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    {
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      "name": "Nota adhesiva 15",
      "type": "n8n-nodes-base.stickyNote",
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        "content": "\n\n\n\n\n\n\n* https://www.sofaconcerts.org (**Knockout.js framework**). A music booking platform and starting point for this workflow. The **Google AI Studio** model helped define **most relevant endpoints** for automating booking communication between the platform and Slack: login, cookie session extension, fetching full event details, event ID and full dialog history endpoints. One endpoint for sending messages back to the platform as well as a detailed request format had to be explored using the **Network Tab in the Dev Tools** console.\n\n* https://bandcamp.com (**Knockout.js framework**). Found **20+ API endpoints**, some working without authentication, e.g. https://bandcamp.com/api/salesfeed/1/get_initial. **Warning:** in this example, due to inconsistent model results, had to manually upload LLM analysis results before sending them to the **Agent**. \n\n* https://www.otto.de (backend is written in **Rust**, frontend is likely custom). Discovered **search autocomplete API endpoint** that allows exploration of product categories and specific items sold on the platform. \n\n* https://www.airbnb.com (complex **React**-based architecture). Found over **500** URL strings with dozens of potential API endpoints. The workflow makes it much faster to understand the sophisticated structure of the platform by analyzing the code of JS files.   \n\n* https://www.jameda.de (**FOSJsRoutingBundle in Symfony**). Found several **ajax endpoints** for general information on specific doctors and their available time slots, e.g. https://www.jameda.de/ajax/doctor/slots-from-nearest-day/392937. To learn how to make requests, review the JS file + page source code for the required ID values. E.g. 327423 in the example URL is the code for a specific doctor.\n\n* https://www.espn.co.uk (**jQuery/Bootstrap** with **Node.js** components). Identified over **300** URL strings, some matching API endpoints listed on **GitHub:** https://gist.github.com/akeaswaran/b48b02f1c94f873c6655e7129910fc3b. LLM analysis revealed several fan API endpoints. "
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      "parameters": {
        "height": 80,
        "content": "# Examples: "
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      "position": [
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