# CSV Quality Scorecard (`stellar_ballet_0bu/csv-quality-scorecard`) Actor Compute a data-quality scorecard for CSV rows: missing required fields, bad emails, bad URLs, duplicate rows, completeness %. - **URL**: https://apify.com/stellar\_ballet\_0bu/csv-quality-scorecard.md - **Developed by:** [Nikita S](https://apify.com/stellar_ballet_0bu) (community) - **Categories:** Automation, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## 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 ## CSV Quality Scorecard Compute a data-quality scorecard for CSV rows before they hit Sheets, Airtable, Supabase, or your CRM. ### What it does - Detects missing required fields. - Validates email columns. - Validates URL columns. - Flags duplicate rows. - Computes completeness % and a 0–100 quality score. - Emits one `summary` row per source plus one `issue` row per problem. ### Use cases - Inbound lead CSV QA before CRM import. - Vendor catalog health checks for marketplaces. - E-commerce data audit (missing price/availability/SKU). - CRM cleanup sprints — score, then route bad rows to a human queue. ### Output - One `summary` row per source with `qualityScore`, `completeness`, `duplicates`, `summary` (`high|medium|low|critical`), and per-row issue list. - One `issue` row per problem with `source`, `_row`, `issues[]`, `filled` (non-empty cell count). - `SUMMARY` with overall score across sources. ### Pricing / cost expectations Default 256 MB / 1 GB. Streaming CSV parse (no full-file load); runs of 10–100k rows finish in seconds. **Pay-per-event** scheduled to activate after public launch: 1 charge per `csv_scored` event (per source) + 1 per `issue_found` event (per problem row). ### Related Actors - [`csv-schema-profiler`](../csv-schema-profiler) — per-column type inference + format detection. - [`csv-dedupe-normalizer`](../csv-dedupe-normalizer) — dedupe + header normalization. - [`csv-header-normalizer`](../csv-header-normalizer) — normalize header names to a target convention. - [`csv-delimiter-autodetect`](../csv-delimiter-autodetect) — figure out the delimiter + quote char + encoding. - [`csv-diff`](../csv-diff) — diff two CSVs on a key column. - [`jsonl-stream-validator`](../jsonl-stream-validator) — same idea for JSONL/NDJSON. ### FAQ **Does it fetch URLs?** Yes, via `csvUrls[]`. Public URLs only, no auth. **Does it work on Excel?** Pre-convert to CSV. For programmatic XLSX/XLS extraction use [`xlsx-sheet-extractor`](../xlsx-sheet-extractor). **Does it call out to email/URL validators?** No. Bad email/URL detection is regex-based. For deep SMTP/MX checks, run this Actor first, then a custom step. ### Safety Public data only. No login, no external lookups, no personal data enrichment. # Actor input Schema ## `csvText` (type: `string`): Paste CSV. Use this or csvUrls. ## `csvUrls` (type: `array`): Public CSV files to score. ## `delimiter` (type: `string`): CSV delimiter. ## `requiredFields` (type: `array`): Field names that must not be empty. ## `emailColumns` (type: `array`): Field names that should contain valid email addresses. ## `urlColumns` (type: `array`): Field names that should contain valid http(s) URLs. ## Actor input object example ```json { "csvText": "", "csvUrls": [], "delimiter": ",", "requiredFields": [], "emailColumns": [], "urlColumns": [] } ```` # Actor output Schema ## `summaryRows` (type: `string`): Default dataset items with \_kind='summary' (or 'issue'), source, headers, rowCount, completeness, qualityScore, duplicateCount, perColumnIssues, perColumnCompleteness, emailStats, urlStats, etc. ## `summary` (type: `string`): Run summary with sources, overallScore (0–100), totalIssues, and per-source summaries array. # 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("stellar_ballet_0bu/csv-quality-scorecard").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("stellar_ballet_0bu/csv-quality-scorecard").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 stellar_ballet_0bu/csv-quality-scorecard --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=stellar_ballet_0bu/csv-quality-scorecard", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "CSV Quality Scorecard", "description": "Compute a data-quality scorecard for CSV rows: missing required fields, bad emails, bad URLs, duplicate rows, completeness %.", "version": "0.1", "x-build-id": "na0rCCsFGNeBkM6qf" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/stellar_ballet_0bu~csv-quality-scorecard/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-stellar_ballet_0bu-csv-quality-scorecard", "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/stellar_ballet_0bu~csv-quality-scorecard/runs": { "post": { "operationId": "runs-sync-stellar_ballet_0bu-csv-quality-scorecard", "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/stellar_ballet_0bu~csv-quality-scorecard/run-sync": { "post": { "operationId": "run-sync-stellar_ballet_0bu-csv-quality-scorecard", "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": { "csvText": { "title": "CSV text", "type": "string", "description": "Paste CSV. Use this or csvUrls.", "default": "" }, "csvUrls": { "title": "Public CSV URLs", "type": "array", "description": "Public CSV files to score.", "default": [], "items": { "type": "string" } }, "delimiter": { "title": "Delimiter", "type": "string", "description": "CSV delimiter.", "default": "," }, "requiredFields": { "title": "Required fields", "type": "array", "description": "Field names that must not be empty.", "default": [], "items": { "type": "string" } }, "emailColumns": { "title": "Email columns", "type": "array", "description": "Field names that should contain valid email addresses.", "default": [], "items": { "type": "string" } }, "urlColumns": { "title": "URL columns", "type": "array", "description": "Field names that should contain valid http(s) URLs.", "default": [], "items": { "type": "string" } } } }, "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 } } } } } } } } } } ```