# X Creator Influence & Authenticity Intelligence (`quanmatrix/x-creator-influence-authenticity-intelligence`) Actor Rank X creators and profiles by reach, engagement quality, growth, authenticity signals and campaign-fit evidence. - **URL**: https://apify.com/quanmatrix/x-creator-influence-authenticity-intelligence.md - **Developed by:** [Rafael Barreto Haddad](https://apify.com/quanmatrix) (community) - **Categories:** Social media, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $2.45 / 1,000 results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event ## What's an Apify Actor? Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows. 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. - **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use). - **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`. - **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`. - **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`. - **Any other language** — 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 ## X Creator Influence & Authenticity Intelligence Rank X creators and profiles by reach, engagement quality, growth, authenticity signals and campaign-fit evidence. This Actor is designed as a decision layer over current and historical datasets, so teams can reuse extraction they already trust instead of paying twice for the same collection work. ### Why use this Actor Raw marketplace exports are useful, but they usually stop at rows and fields. X Creator Influence & Authenticity Intelligence adds deterministic scoring, explicit change detection, and action-ready output. It accepts inline records or an Apify Dataset, compares a previous snapshot when supplied, and returns a consistent decision surface for dashboards, workflows, agents, and recurring monitoring. The design deliberately separates extraction from intelligence. That makes the Actor easier to maintain when a source website changes and lets users combine it with any compatible upstream scraper. Scores are transparent and based on documented fields rather than an opaque mandatory LLM call. ### Key features - Dataset-first workflow that works with upstream Apify Actors or your own normalized exports. - Deterministic `signalScore`, `changeScore`, `agentAction`, and `agentReason` on every result. - Current-versus-previous snapshot comparison for recurring intelligence. - Low-memory 256 MB runtime and predictable pay-per-result economics. - Structured Dataset output plus an `INTELLIGENCE_REPORT` key-value summary. - Useful defaults for one-click testing before wiring a production Dataset. ### Input Provide `currentItems` for inline analysis or `currentDatasetId` for an upstream Apify Dataset. For change intelligence, also provide `previousItems` or `previousDatasetId`. `maxItems` limits how many records are analyzed in one run. The Actor does not require credentials for the source platform because the stable workflow starts from supplied or upstream-collected data. ### Output Each row contains the original source record plus `signalScore`, `changeScore`, `agentAction`, `agentReason`, normalized metrics, detected field changes, and an observation timestamp. This makes the Dataset suitable for filtering, automation, alerts, portfolio review, and AI-agent tool chains. ### Example A recurring workflow can run an upstream scraper daily, pass its Dataset ID as `currentDatasetId`, pass yesterday's Dataset as `previousDatasetId`, and use only records whose `agentAction` indicates a material opportunity or risk. This avoids rebuilding the same comparison logic in every automation. ### Use cases Use it for competitive monitoring, market research, prioritization, change alerts, analyst triage, recurring portfolio reviews, and agent workflows where raw records need a consistent action layer. It is especially useful when the source has many rows but human attention should be spent only on the highest-signal changes. ### Pricing The primary event price is **$0.0035 USD per decision-ready result row**. There is no separate analytical start fee in the product design. Users can control spend with `maxItems` and by filtering upstream data before analysis. ### Limitations This Actor analyzes the records supplied to it; source completeness depends on the upstream scraper or dataset. Scores are decision support, not predictions of financial outcomes. Field conventions can differ between upstream Actors, so the sample schema should be normalized when necessary. Only public or appropriately licensed data should be processed. Snapshot intelligence is meaningful only when entity identifiers are stable across periods. ### Workflow design For production use, keep extraction and intelligence as separate steps. This makes source replacement easier, allows historical snapshots to be retained independently, and lets the same intelligence Actor consume data from different collectors. The output schema stays stable even when the upstream extractor changes. # Actor input Schema ## `currentItems` (type: `array`): Optional current source rows. If supplied, these are analyzed directly and no source fetch is required. ## `currentDatasetId` (type: `string`): Current Apify Dataset from a compatible upstream extraction workflow. ## `previousItems` (type: `array`): Optional previous rows for deterministic change intelligence. ## `previousDatasetId` (type: `string`): Previous Apify Dataset for recurring snapshot comparison. ## `maxItems` (type: `integer`): Maximum number of records analyzed and written to the output Dataset. ## `payload` (type: `object`): Automatically supplied by Apify Actor-to-Actor integrations. If currentDatasetId is empty, payload.resource.defaultDatasetId is used. ## Actor input object example ```json { "currentItems": [ { "id": "creator1", "name": "Creator Alpha", "followers": 420000, "engagementRate": 5.8, "growthPct": 22, "verified": true, "brandFitScore": 88 }, { "id": "creator2", "name": "Creator Beta", "followers": 68000, "engagementRate": 1.9, "growthPct": 3, "verified": false, "brandFitScore": 62 } ], "previousItems": [ { "id": "creator1", "name": "Creator Alpha", "followers": 360000, "engagementRate": 4.9, "growthPct": 14, "verified": true, "brandFitScore": 82 } ], "maxItems": 20000 } ``` # Actor output Schema ## `results` (type: `string`): Normalized and scored result rows. ## `report` (type: `string`): Aggregate report. # 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 = { "currentItems": [ { "id": "creator1", "name": "Creator Alpha", "followers": 420000, "engagementRate": 5.8, "growthPct": 22, "verified": true, "brandFitScore": 88 }, { "id": "creator2", "name": "Creator Beta", "followers": 68000, "engagementRate": 1.9, "growthPct": 3, "verified": false, "brandFitScore": 62 } ], "previousItems": [ { "id": "creator1", "name": "Creator Alpha", "followers": 360000, "engagementRate": 4.9, "growthPct": 14, "verified": true, "brandFitScore": 82 } ] }; // Run the Actor and wait for it to finish const run = await client.actor("quanmatrix/x-creator-influence-authenticity-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 = { "currentItems": [ { "id": "creator1", "name": "Creator Alpha", "followers": 420000, "engagementRate": 5.8, "growthPct": 22, "verified": True, "brandFitScore": 88, }, { "id": "creator2", "name": "Creator Beta", "followers": 68000, "engagementRate": 1.9, "growthPct": 3, "verified": False, "brandFitScore": 62, }, ], "previousItems": [{ "id": "creator1", "name": "Creator Alpha", "followers": 360000, "engagementRate": 4.9, "growthPct": 14, "verified": True, "brandFitScore": 82, }], } # Run the Actor and wait for it to finish run = client.actor("quanmatrix/x-creator-influence-authenticity-intelligence").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}") for item in client.dataset(run.default_dataset_id).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "currentItems": [ { "id": "creator1", "name": "Creator Alpha", "followers": 420000, "engagementRate": 5.8, "growthPct": 22, "verified": true, "brandFitScore": 88 }, { "id": "creator2", "name": "Creator Beta", "followers": 68000, "engagementRate": 1.9, "growthPct": 3, "verified": false, "brandFitScore": 62 } ], "previousItems": [ { "id": "creator1", "name": "Creator Alpha", "followers": 360000, "engagementRate": 4.9, "growthPct": 14, "verified": true, "brandFitScore": 82 } ] }' | apify call quanmatrix/x-creator-influence-authenticity-intelligence --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "type": "http", "url": "https://mcp.apify.com/?tools=fetch-actor-details,quanmatrix/x-creator-influence-authenticity-intelligence" } } } ``` The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer ` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations). ## OpenAPI specification Download the OpenAPI definition: https://api.apify.com/v2/actors/qTb5FdnRNhrnacp6E/builds/9P8caKoS5Go9slpYf/openapi.json