# FIRST CVSS Severity Index - Free Exploit Probability API (`ntriqpro/first-cvss-severity-index`) Actor Free API for FIRST EPSS (Exploit Prediction Scoring System). No subscription. Get exploit probability scores and prioritize vulnerability patching. Government data, pay-per-use. - **URL**: https://apify.com/ntriqpro/first-cvss-severity-index.md - **Developed by:** [daehwan kim](https://apify.com/ntriqpro) (community) - **Categories:** AI, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing $100.00 / 1,000 charged when a dividend safety analysis is successes 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.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 ## Dividend Safety Analyzer Analyze dividend safety for any US-listed stock using SEC EDGAR financial filings and FRED macroeconomic data. Returns a **0-100 safety score**, **A+ to F grade**, and actionable verdict — ideal for income investors, financial advisors, and portfolio risk tools. ### What It Does For each ticker symbol, this actor fetches real SEC EDGAR company facts (annual 10-K filings) and computes four weighted dimensions: | Dimension | Weight | What It Measures | |---|---|---| | **Payout Ratio** | 25 pts | Dividends paid vs. earnings per share | | **Dividend Growth** | 25 pts | Consecutive years of growth + CAGR | | **Earnings Stability** | 25 pts | EPS volatility coefficient + negative years | | **Debt Coverage** | 25 pts | Long-term debt relative to earnings | **Total score: 0-100. Grade: A+ to F.** ### Use Cases - **Income investors** — Screen dividend stocks before buying - **Financial advisors** — Quickly assess dividend sustainability for client portfolios - **Portfolio risk tools** — Batch-analyze dividend payers for risk flags - **Fintech apps** — Embed dividend quality scores in investment platforms - **Robo-advisors** — Filter high-yield dividend traps from safe dividend growers ### Input ```json { "ticker": "JNJ", "years": 5 } ```` | Field | Type | Required | Default | Description | |---|---|---|---|---| | `ticker` | string | Yes | `JNJ` | Stock ticker symbol. Supports comma-separated batch (e.g., `"JNJ,KO,PG"`) | | `years` | integer | No | `5` | Years of historical data to analyze (1-10) | ### Output ```json { "ticker": "JNJ", "safetyScore": 82, "grade": "B+", "verdict": "Safe dividend — solid fundamentals with manageable payout ratio", "payoutRatio": { "ratio": 0.44, "score": 20, "maxScore": 25 }, "dividendGrowth": { "cagr": 5.2, "consecutiveGrowthYears": 5, "score": 25, "maxScore": 25 }, "earningsStability": { "volatilityCoefficient": 0.12, "negativeEarningsYears": 0, "score": 22, "maxScore": 25 }, "debtCoverage": { "debtToEarningsRatio": 2.1, "score": 15, "maxScore": 25 }, "flags": [], "scoreBreakdown": { "payoutRatioScore": 20, "dividendGrowthScore": 25, "earningsStabilityScore": 22, "debtCoverageScore": 15 }, "sources": ["SEC EDGAR Company Facts", "FRED Federal Reserve"], "timestamp": "2025-03-17T00:00:00.000Z" } ``` #### Score Guide | Score | Grade | Meaning | |---|---|---| | 90-100 | A / A+ | Highly safe — consistent growth, strong coverage | | 75-89 | B / B+ | Safe — solid fundamentals | | 50-74 | C | Moderate risk — some sustainability concerns | | 25-49 | D | Elevated risk — cut possible in downturn | | 0-24 | F | High cut risk — unsustainable payout | #### Risk Flags - `HIGH_PAYOUT_RATIO` — Payout ratio > 90% - `PAYOUT_EXCEEDS_EARNINGS` — Dividends exceed earnings (ratio > 100%) - `NO_DIVIDEND_GROWTH` — No consecutive growth years detected - `NEGATIVE_EARNINGS_YEARS` — Company had loss years in the analysis window - `HIGH_DEBT_LOAD` — Debt-to-earnings ratio > 6x ### Data Sources - **SEC EDGAR** — `data.sec.gov/api/xbrl/companyfacts` — Annual 10-K filings for EPS, dividends, long-term debt - **FRED (Federal Reserve)** — `api.stlouisfed.org` — Fed funds rate for macro context All data is fetched in real time from official public APIs. No third-party data providers or subscriptions required. ### Pricing **Pay-per-event:** $0.05 per ticker analyzed. Analyzing 10 tickers = $0.50. Batch input supported — pass comma-separated tickers. ### Example: Batch Analysis ```json { "ticker": "JNJ,KO,PG,MMM,T", "years": 5 } ``` Returns one result per ticker, each with full score breakdown and flags. ### Limitations - Requires SEC EDGAR CIK lookup — works for US-listed companies with 10-K filings - Funded ratio data comes from annual 10-K filings; REITs and MLPs may show distorted payout ratios by design - FRED macro context uses national-level rates, not company-specific ### About Built by **NtriqPRO** — financial data intelligence tools for Apify Store. # Actor input Schema ## `ticker` (type: `string`): Stock ticker symbol to analyze (e.g., 'AAPL', 'JNJ', 'KO'). Supports comma-separated values for batch analysis. ## `years` (type: `integer`): Number of years of historical data to analyze for dividend growth and earnings stability trends. ## Actor input object example ```json { "ticker": "JNJ", "years": 5 } ``` # Actor output Schema ## `results` (type: `string`): No description # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = {}; // Run the Actor and wait for it to finish const run = await client.actor("ntriqpro/first-cvss-severity-index").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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("ntriqpro/first-cvss-severity-index").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 ntriqpro/first-cvss-severity-index --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=ntriqpro/first-cvss-severity-index", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "FIRST CVSS Severity Index - Free Exploit Probability API", "description": "Free API for FIRST EPSS (Exploit Prediction Scoring System). No subscription. Get exploit probability scores and prioritize vulnerability patching. Government data, pay-per-use.", "version": "1.0", "x-build-id": "FjOrQ4u1loIip3QQ9" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/ntriqpro~first-cvss-severity-index/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-ntriqpro-first-cvss-severity-index", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/ntriqpro~first-cvss-severity-index/runs": { "post": { "operationId": "runs-sync-ntriqpro-first-cvss-severity-index", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/ntriqpro~first-cvss-severity-index/run-sync": { "post": { "operationId": "run-sync-ntriqpro-first-cvss-severity-index", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "ticker" ], "properties": { "ticker": { "title": "Stock Ticker Symbol", "type": "string", "description": "Stock ticker symbol to analyze (e.g., 'AAPL', 'JNJ', 'KO'). Supports comma-separated values for batch analysis.", "default": "JNJ" }, "years": { "title": "Analysis Years", "minimum": 1, "maximum": 10, "type": "integer", "description": "Number of years of historical data to analyze for dividend growth and earnings stability trends.", "default": 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 } } } } } } } } } } ```