# WeChat Ecosystem Data Intelligence (`scrupulous_buckler/wechat-ecosystem-data-intelligence`) Actor Audit WeChat Official Account, Channels, and Huxuan creator data with evidence-linked performance, creator, schema health, freshness, and SLA reports. - **URL**: https://apify.com/scrupulous\_buckler/wechat-ecosystem-data-intelligence.md - **Developed by:** [Peng Lyu](https://apify.com/scrupulous_buckler) (community) - **Categories:** Social media, Automation, Other - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $4.99 / full wechat ecosystem intelligence report This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. 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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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 ## WeChat Ecosystem Data Intelligence Audit authorized WeChat Official Account, WeChat Channels, and Huxuan creator data without publishing or sharing a platform Cookie. **Unofficial tool. This Actor is not affiliated with, sponsored by, or endorsed by Tencent, WeChat, Weixin, Channels, Huxuan, or any source platform.** ### What this Actor sells - Official Account content and performance audits - WeChat Channels account and video-history audits - Huxuan creator shortlists with performance consistency, topic fit, commercial content share, risk flags, and source evidence - private pipeline checks for volume, freshness, required-field coverage, schema drift, and SLA status - a full ecosystem report combining every module This is the stable analysis and maintenance layer. It does not claim that WeChat is freely scrapeable. ### Private-data-safe delivery Choose either: 1. paste authorized JSON into `uploadedRecords`; or 2. provide one or more `sourceDatasetIds` from your own Apify account. The Actor uses **Limited permissions**. It can read only storages explicitly supplied to the run and cannot browse unrelated datasets in your account. No Cookie input exists. Collection connectors, scheduled private tasks, custom field mapping, maintenance, and SLA delivery can be scoped separately through Apify messaging. Customer credentials must stay in the customer's private task or secret store. ### Modes - `demo`: safe sample across Official Accounts, Channels, and Huxuan - `officialAccountAudit`: article cadence, views, engagement, themes, and evidence - `videoChannelAudit`: video reach, interaction rate, consistency, and breakouts - `huxuanCreatorAudit`: creator selection score, topic fit, sponsored share, and collaboration risk flags - `pipelineHealth`: freshness, volume, completeness, schema drift, and SLA status - `fullAudit`: every module in one report ### Example using a private Dataset ```json { "mode": "fullAudit", "sourceDatasetIds": ["YOUR_PRIVATE_DATASET_ID"], "topicKeywords": ["血糖管理", "家庭健康", "真实测评"], "requiredFields": [ "recordId", "accountType", "accountName", "text", "publishedAt", "views" ], "targetFreshnessHours": 48, "minimumExpectedRecords": 100 } ```` ### Accepted records The normalizer recognizes common article, video, and creator-history fields, including `articleId`, `videoId`, `postId`, `accountName`, `creatorName`, `title`, `content`, `description`, `publishedAt`, `readCount`, `playCount`, `views`, `likes`, `comments`, `shares`, `saves`, `followers`, and nested `stats`, `metrics`, or `engagement` objects. Label records with `accountType` as `officialAccount`, `videoChannel`, or `huxuanCreator`. Chinese aliases such as `公众号`, `视频号`, and `互选达人` work. ### Pricing | Report | Price | | --- | ---: | | Official Account or Channels audit | $1.99 | | Huxuan creator audit | $2.99 | | Private pipeline health & SLA | $0.99 | | Full ecosystem audit | $4.99 | Platform usage is included in the report price. ### Analytical boundaries - Results describe the supplied history, not all of WeChat. - Interaction rates use supplied views or follower counts and are not compared blindly across content types. - Risk flags are screening signals, not proof of fake followers, fraud, sales lift, or creator misconduct. - The Actor does not verify private audience demographics or guarantee campaign performance. - SLA status measures the configured delivery thresholds; a commercial maintenance SLA requires a separate agreed scope. ### Responsible use Only process data you are authorized to use. Follow platform terms, privacy, advertising, intellectual-property, and data-protection rules. Do not collect private messages, sensitive profiles, or credentials through this public Actor. ### Local validation ```bash python3 -m unittest discover -s tests -v ``` # Actor input Schema ## `mode` (type: `string`): Run the demo or select one focused or full audit. ## `uploadedRecords` (type: `array`): Paste authorized article, video, post, or creator-history JSON. Common English, Chinese, and scraper field aliases are recognized. ## `sourceDatasetIds` (type: `array`): Optional private datasets explicitly supplied to this run. The Actor uses Limited permissions and cannot browse unrelated account data. ## `accountNames` (type: `array`): Optional names to include. Leave empty to audit every account or creator found in the supplied data. ## `topicKeywords` (type: `array`): Keywords used to measure topic coverage and creator fit. ## `requiredFields` (type: `array`): Used in SLA mode. Supported values: recordId, accountType, accountName, text, publishedAt, views, likes, comments, shares, saves. ## `targetFreshnessHours` (type: `integer`): Maximum acceptable age of the newest published record. ## `minimumExpectedRecords` (type: `integer`): Minimum normalized records expected for the private task or scheduled delivery. ## `maxRecords` (type: `integer`): Hard total across uploads and private datasets. ## `maxEvidence` (type: `integer`): Limits source-linked content examples in each report section. ## Actor input object example ```json { "mode": "demo", "uploadedRecords": [], "sourceDatasetIds": [], "accountNames": [], "topicKeywords": [], "requiredFields": [ "recordId", "accountType", "accountName", "text", "publishedAt" ], "targetFreshnessHours": 48, "minimumExpectedRecords": 20, "maxRecords": 5000, "maxEvidence": 10 } ``` # Actor output Schema ## `reports` (type: `string`): No description # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("scrupulous_buckler/wechat-ecosystem-data-intelligence").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 = {} # Run the Actor and wait for it to finish run = client.actor("scrupulous_buckler/wechat-ecosystem-data-intelligence").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 '{}' | apify call scrupulous_buckler/wechat-ecosystem-data-intelligence --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=scrupulous_buckler/wechat-ecosystem-data-intelligence", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "WeChat Ecosystem Data Intelligence", "description": "Audit WeChat Official Account, Channels, and Huxuan creator data with evidence-linked performance, creator, schema health, freshness, and SLA reports.", "version": "0.1", "x-build-id": "LU5pfeMNFJ4VYx2TH" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/scrupulous_buckler~wechat-ecosystem-data-intelligence/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-scrupulous_buckler-wechat-ecosystem-data-intelligence", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/scrupulous_buckler~wechat-ecosystem-data-intelligence/runs": { "post": { "operationId": "runs-sync-scrupulous_buckler-wechat-ecosystem-data-intelligence", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/scrupulous_buckler~wechat-ecosystem-data-intelligence/run-sync": { "post": { "operationId": "run-sync-scrupulous_buckler-wechat-ecosystem-data-intelligence", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "properties": { "mode": { "title": "Audit mode", "enum": [ "demo", "officialAccountAudit", "videoChannelAudit", "huxuanCreatorAudit", "pipelineHealth", "fullAudit" ], "type": "string", "description": "Run the demo or select one focused or full audit.", "default": "demo" }, "uploadedRecords": { "title": "Uploaded WeChat ecosystem records", "type": "array", "description": "Paste authorized article, video, post, or creator-history JSON. Common English, Chinese, and scraper field aliases are recognized.", "default": [] }, "sourceDatasetIds": { "title": "Private Apify Dataset IDs", "type": "array", "description": "Optional private datasets explicitly supplied to this run. The Actor uses Limited permissions and cannot browse unrelated account data.", "default": [], "items": { "type": "string" } }, "accountNames": { "title": "Accounts or creators to audit", "type": "array", "description": "Optional names to include. Leave empty to audit every account or creator found in the supplied data.", "default": [], "items": { "type": "string" } }, "topicKeywords": { "title": "Category and audience-fit keywords", "type": "array", "description": "Keywords used to measure topic coverage and creator fit.", "default": [], "items": { "type": "string" } }, "requiredFields": { "title": "Normalized fields required by the private pipeline", "type": "array", "description": "Used in SLA mode. Supported values: recordId, accountType, accountName, text, publishedAt, views, likes, comments, shares, saves.", "default": [ "recordId", "accountType", "accountName", "text", "publishedAt" ], "items": { "type": "string" } }, "targetFreshnessHours": { "title": "Freshness SLA in hours", "minimum": 1, "maximum": 720, "type": "integer", "description": "Maximum acceptable age of the newest published record.", "default": 48 }, "minimumExpectedRecords": { "title": "Minimum records per delivery", "minimum": 1, "maximum": 5000, "type": "integer", "description": "Minimum normalized records expected for the private task or scheduled delivery.", "default": 20 }, "maxRecords": { "title": "Maximum records to analyze", "minimum": 1, "maximum": 5000, "type": "integer", "description": "Hard total across uploads and private datasets.", "default": 5000 }, "maxEvidence": { "title": "Maximum evidence rows per section", "minimum": 3, "maximum": 50, "type": "integer", "description": "Limits source-linked content examples in each report section.", "default": 10 } } }, "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 } } } } } } } } } } ```