# Automation Workflow Audit Tool — n8n & Make (`spqr79/automation-workflow-audit-tool`) Actor Paste any n8n workflow export or Make blueprint and get a structured AI-powered audit: risks, quality score, error-handling gaps, improvement recommendations, and an optional test plan — in DE, EN, FR, or ES. - **URL**: https://apify.com/spqr79/automation-workflow-audit-tool.md - **Developed by:** [Spqr79](https://apify.com/spqr79) (community) - **Categories:** AI, Developer tools, Automation - **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $0.45 / audit report This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Automation Workflow Audit Tool — n8n & Make Paste any **n8n workflow export** or **Make (Integromat) blueprint** and receive a structured, AI-powered audit report in seconds. ### What you get - **Executive summary** — what the workflow does and its overall risk level - **Step-by-step flow analysis** — each node explained in plain language - **Risk analysis** — missing error handlers, fragile triggers, external dependency risks - **Concrete issues** — specific weaknesses and ambiguities flagged directly - **Improvement recommendations** — architecture, stability, monitoring - **Quality score** — 1–10 with explanation - **Operations & maintenance advice** - **Optional test plan** — happy path, failure, duplicate trigger, credential issues Output is **Markdown**, pushed to the Actor dataset and ready to copy into Notion, Confluence, or any docs tool. --- ### Input | Field | Required | Description | |---|---|---| | `platform` | ✅ | `n8n` or `make` | | `workflowJson` | ✅ | Exported JSON from n8n or Make | | `model` | — | `claude-sonnet-4-5` (default, recommended) or `claude-haiku-4-5` (faster, cheaper) | | `includeTestPlan` | — | `true` (default) — append a test checklist to the report | | `language` | — | `de`, `en`, `fr`, `es` — auto-detected from workflow content if not set | --- ### Quickstart #### 1. Export your workflow **n8n:** Open your workflow → ⋯ menu → *Download* → saves as `.json` **Make:** Open your scenario → ⋯ menu → *Export Blueprint* → saves as `.json` #### 2. Run the Actor Paste the JSON into the **Workflow JSON** field in the Apify UI, select your platform, and hit **Start**. #### 3. Retrieve the report The audit report is saved in **two places** after each run: After each run, three files are available under **Storage → Key-Value Store**: | File | Format | Best for | |---|---|---| | `report.pdf` | PDF | Sending to clients, attaching to tickets | | `report.docx` | Word | Editing, adding company branding | | `report.md` | Markdown | Pasting into Notion, Confluence, GitHub | Go to the run page → **Storage → Key-Value Store** → click the file → **Download**. The full audit is also available as a JSON record in **Storage → Dataset** (`documentation` field) for programmatic access. --- ### Example input (UI / JSON mode) ```json { "platform": "n8n", "workflowJson": "{\"name\":\"Invoice → Slack Notification\",\"nodes\":[{\"name\":\"Webhook\",\"type\":\"n8n-nodes-base.webhook\"},{\"name\":\"Parse Invoice\",\"type\":\"n8n-nodes-base.function\"},{\"name\":\"Send to Slack\",\"type\":\"n8n-nodes-base.slack\"}],\"connections\":{}}", "model": "claude-sonnet-4-5", "includeTestPlan": true, "language": "en" } ```` ### Example output (excerpt) ```markdown ## Workflow Audit: Invoice → Slack Notification ### Executive Summary This workflow receives invoice data via a webhook, processes it in a function node, and sends a Slack notification. Overall risk level: **medium**. No error handler is present — a failed Slack call will silently drop the event. ### Quality Score **4 / 10** — The core logic is sound but the workflow lacks error handling, retry logic, and any form of logging. Not production-ready as-is. ... ``` *** ### Environment variables Set in **Actor → Settings → Environment variables**: | Variable | Required | Description | |---|---|---| | `ANTHROPIC_API_KEY` | ✅ | Your Anthropic API key — get one at console.anthropic.com | *** ### Limits - Maximum workflow JSON size: **500 KB** - Supported platforms: **n8n**, **Make (Integromat)** - Supported output languages: **DE, EN, FR, ES** (auto-detected or manual) *** ### Local development ```bash ## Install dependencies npm install ## Type-check only npm run type-check ## Run with tsx (no build step) ANTHROPIC_API_KEY=sk-... npm run dev ## Build TypeScript → dist/ npm run build ## Run compiled output npm start ``` ### Deploy to Apify ```bash npm install -g apify-cli apify login apify push --force ``` # Actor input Schema ## `platform` (type: `string`): Which automation platform did you export from? ## `workflowJson` (type: `string`): Paste your n8n workflow JSON or Make blueprint JSON here. ## `includeTestPlan` (type: `boolean`): Include a compact validation checklist in the output ## `language` (type: `string`): Force a specific output language. If not set, the language is auto-detected from the workflow content. ## `model` (type: `string`): Which Claude model to use for the audit. Sonnet produces more nuanced reports; Haiku is faster and cheaper. ## Actor input object example ```json { "platform": "n8n", "workflowJson": "{\"name\":\"Example Workflow\",\"nodes\":[{\"id\":\"1\",\"name\":\"Webhook\",\"type\":\"n8n-nodes-base.webhook\",\"position\":[240,300],\"parameters\":{}},{\"id\":\"2\",\"name\":\"Send Email\",\"type\":\"n8n-nodes-base.gmail\",\"position\":[460,300],\"parameters\":{}},{\"id\":\"3\",\"name\":\"Error Trigger\",\"type\":\"n8n-nodes-base.errorTrigger\",\"position\":[240,500],\"parameters\":{}}],\"connections\":{\"Webhook\":{\"main\":[[{\"node\":\"Send Email\",\"type\":\"main\",\"index\":0}]]}},\"active\":true,\"settings\":{}}", "includeTestPlan": true, "model": "claude-sonnet-4-5" } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "workflowJson": "{\"name\":\"Example Workflow\",\"nodes\":[{\"id\":\"1\",\"name\":\"Webhook\",\"type\":\"n8n-nodes-base.webhook\",\"position\":[240,300],\"parameters\":{}},{\"id\":\"2\",\"name\":\"Send Email\",\"type\":\"n8n-nodes-base.gmail\",\"position\":[460,300],\"parameters\":{}},{\"id\":\"3\",\"name\":\"Error Trigger\",\"type\":\"n8n-nodes-base.errorTrigger\",\"position\":[240,500],\"parameters\":{}}],\"connections\":{\"Webhook\":{\"main\":[[{\"node\":\"Send Email\",\"type\":\"main\",\"index\":0}]]}},\"active\":true,\"settings\":{}}" }; // Run the Actor and wait for it to finish const run = await client.actor("spqr79/automation-workflow-audit-tool").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "workflowJson": "{\"name\":\"Example Workflow\",\"nodes\":[{\"id\":\"1\",\"name\":\"Webhook\",\"type\":\"n8n-nodes-base.webhook\",\"position\":[240,300],\"parameters\":{}},{\"id\":\"2\",\"name\":\"Send Email\",\"type\":\"n8n-nodes-base.gmail\",\"position\":[460,300],\"parameters\":{}},{\"id\":\"3\",\"name\":\"Error Trigger\",\"type\":\"n8n-nodes-base.errorTrigger\",\"position\":[240,500],\"parameters\":{}}],\"connections\":{\"Webhook\":{\"main\":[[{\"node\":\"Send Email\",\"type\":\"main\",\"index\":0}]]}},\"active\":true,\"settings\":{}}" } # Run the Actor and wait for it to finish run = client.actor("spqr79/automation-workflow-audit-tool").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "workflowJson": "{\\"name\\":\\"Example Workflow\\",\\"nodes\\":[{\\"id\\":\\"1\\",\\"name\\":\\"Webhook\\",\\"type\\":\\"n8n-nodes-base.webhook\\",\\"position\\":[240,300],\\"parameters\\":{}},{\\"id\\":\\"2\\",\\"name\\":\\"Send Email\\",\\"type\\":\\"n8n-nodes-base.gmail\\",\\"position\\":[460,300],\\"parameters\\":{}},{\\"id\\":\\"3\\",\\"name\\":\\"Error Trigger\\",\\"type\\":\\"n8n-nodes-base.errorTrigger\\",\\"position\\":[240,500],\\"parameters\\":{}}],\\"connections\\":{\\"Webhook\\":{\\"main\\":[[{\\"node\\":\\"Send Email\\",\\"type\\":\\"main\\",\\"index\\":0}]]}},\\"active\\":true,\\"settings\\":{}}" }' | apify call spqr79/automation-workflow-audit-tool --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=spqr79/automation-workflow-audit-tool", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Automation Workflow Audit Tool — n8n & Make", "description": "Paste any n8n workflow export or Make blueprint and get a structured AI-powered audit: risks, quality score, error-handling gaps, improvement recommendations, and an optional test plan — in DE, EN, FR, or ES.", "version": "1.0", "x-build-id": "1qy8p9aQPex46gV9L" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/spqr79~automation-workflow-audit-tool/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-spqr79-automation-workflow-audit-tool", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/spqr79~automation-workflow-audit-tool/runs": { "post": { "operationId": "runs-sync-spqr79-automation-workflow-audit-tool", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/spqr79~automation-workflow-audit-tool/run-sync": { "post": { "operationId": "run-sync-spqr79-automation-workflow-audit-tool", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "platform", "workflowJson" ], "properties": { "platform": { "title": "Platform", "enum": [ "n8n", "make" ], "type": "string", "description": "Which automation platform did you export from?", "default": "n8n" }, "workflowJson": { "title": "Workflow JSON", "type": "string", "description": "Paste your n8n workflow JSON or Make blueprint JSON here." }, "includeTestPlan": { "title": "Include Test Plan", "type": "boolean", "description": "Include a compact validation checklist in the output", "default": true }, "language": { "title": "Output Language", "enum": [ "de", "en", "fr", "es" ], "type": "string", "description": "Force a specific output language. If not set, the language is auto-detected from the workflow content." }, "model": { "title": "Claude Model", "enum": [ "claude-sonnet-4-5", "claude-haiku-4-5" ], "type": "string", "description": "Which Claude model to use for the audit. Sonnet produces more nuanced reports; Haiku is faster and cheaper.", "default": "claude-sonnet-4-5" } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```