# Aviation Intel (`fiery_dream/aviation-intel`) Actor Enterprise-grade aviation weather briefing and flight operations intelligence agent. Features Real-time METAR Analysis: Current airport conditions with flight category (VFR/MVFR/IFR/LIFR) TAF Forecasts: 24-30 hour terminal aerodrome forecasts decoded to plain English and more! - **URL**: https://apify.com/fiery\_dream/aviation-intel.md - **Developed by:** [Cody Churchwell](https://apify.com/fiery_dream) (community) - **Categories:** Agents, Automation, Travel - **Stats:** 13 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $0.01 / 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. 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 ## Aviation Operations Intelligence Enterprise-grade aviation weather briefing and flight operations intelligence agent. ### Features - **Real-time METAR Analysis**: Current airport conditions with flight category (VFR/MVFR/IFR/LIFR) - **TAF Forecasts**: 24-30 hour terminal aerodrome forecasts decoded to plain English - **SIGMET/AIRMET Monitoring**: Hazardous weather alerts (convection, turbulence, icing) - **PIREP Reports**: Real pilot reports of actual conditions aloft - **Risk Scoring**: Automatic 0-100 risk assessment with go/no-go guidance - **NWS Alerts**: National Weather Service severe weather warnings - **AI-Powered Briefings**: Optional LLM analysis for plain-language recommendations - **Flight Tracking**: Recent arrivals/departures via OpenSky Network - **Radar Imagery**: Current precipitation radar links ### Data Sources (100% FREE, NO API KEY REQUIRED) | Source | Data Provided | |--------|---------------| | AviationWeather.gov | METARs, TAFs, PIREPs, SIGMETs, AIRMETs | | National Weather Service | Forecasts, severe weather alerts | | OpenSky Network | Live aircraft tracking, arrivals/departures | | RainViewer | Radar precipitation imagery | ### Input Parameters | Parameter | Type | Description | |-----------|------|-------------| | airports | array | ICAO airport codes (e.g., ["KJFK", "KLAX"]) | | modules | array | Data modules to fetch | | include_traffic | boolean | Include flight tracking data | | include_ai_analysis | boolean | Use AI for plain-language briefing | | model_name | string | LLM model for AI analysis | | route_bbox | array | Bounding box for route weather | ### Output ```json { "airports": ["KJFK", "KLAX"], "timestamp": "2024-01-15T14:30:00Z", "risk_assessment": { "score": 35, "level": "MODERATE", "recommendation": "Exercise caution. Review all weather products carefully.", "factors": ["MVFR conditions at KJFK", "Moderate winds at KLAX"] }, "briefing_text": "# AVIATION WEATHER BRIEFING...", "metars": [...], "tafs": [...], "sigmets": [...], "airmets": [...], "pireps": [...], "alerts": [...], "ai_analysis": "Based on current conditions..." } ```` ### Risk Levels | Level | Score | Description | |-------|-------|-------------| | MINIMAL | 0-19 | Good flying conditions expected | | LOW | 20-39 | Generally favorable, monitor conditions | | MODERATE | 40-69 | Exercise caution, review all products | | HIGH | 70-100 | Flight not recommended, significant hazards | ### Use Cases - **Pre-flight Briefing**: Complete weather briefing before every flight - **FBO Operations**: Monitor conditions at your airport - **Flight Schools**: Training weather decision-making - **Charter Operators**: Route planning and risk management - **Dispatch Centers**: Multi-airport weather monitoring ### Example Usage ```bash apify call aviation-intel -i '{ "airports": ["KJFK", "KBOS", "KDCA"], "modules": ["metar", "taf", "sigmets", "pireps", "alerts"], "include_ai_analysis": true }' ``` ### Pricing Pay-per-event pricing based on airports briefed: - $0.01 per airport briefed - AI analysis included when API key provided ### Aviation Disclaimer This tool provides weather information for planning purposes only. Always obtain an official weather briefing from 1800wxbrief.com or your local flight service station before flight. Pilot in command is responsible for all go/no-go decisions. # Actor input Schema ## `airports` (type: `array`): List of ICAO airport codes (e.g., KJFK, KLAX, KORD). Can be comma-separated string or array. ## `modules` (type: `array`): Select which aviation data to fetch ## `include_traffic` (type: `boolean`): Fetch recent arrivals/departures from OpenSky Network ## `include_ai_analysis` (type: `boolean`): Use AI to generate plain-language briefing and recommendations (requires API key) ## `model_name` (type: `string`): LLM model for analysis ## `query` (type: `string`): Optional: Specific question about the weather/conditions ## `route_bbox` (type: `array`): Optional: \[min\_lat, max\_lat, min\_lon, max\_lon] for en-route weather ## `api_key` (type: `string`): API key for the selected AI model (Google AI Studio key for Gemini, OpenAI key for GPT, Anthropic key for Claude) ## Actor input object example ```json { "airports": [ "KJFK", "KLAX" ], "modules": [ "metar", "taf", "pireps", "sigmets", "airmets", "alerts", "forecast" ], "include_traffic": false, "include_ai_analysis": true, "model_name": "gemini/gemini-2.0-flash" } ``` # 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 = { "airports": [ "KJFK", "KLAX" ] }; // Run the Actor and wait for it to finish const run = await client.actor("fiery_dream/aviation-intel").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 = { "airports": [ "KJFK", "KLAX", ] } # Run the Actor and wait for it to finish run = client.actor("fiery_dream/aviation-intel").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 '{ "airports": [ "KJFK", "KLAX" ] }' | apify call fiery_dream/aviation-intel --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=fiery_dream/aviation-intel", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Aviation Intel", "description": "Enterprise-grade aviation weather briefing and flight operations intelligence agent.\n\nFeatures\nReal-time METAR Analysis: Current airport conditions with flight category (VFR/MVFR/IFR/LIFR)\nTAF Forecasts: 24-30 hour terminal aerodrome forecasts decoded to plain English\nand more!", "version": "1.0", "x-build-id": "kuP9u5ScjH1D141A4" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/fiery_dream~aviation-intel/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-fiery_dream-aviation-intel", "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/fiery_dream~aviation-intel/runs": { "post": { "operationId": "runs-sync-fiery_dream-aviation-intel", "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/fiery_dream~aviation-intel/run-sync": { "post": { "operationId": "run-sync-fiery_dream-aviation-intel", "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": [ "airports" ], "properties": { "airports": { "title": "Airport Codes (ICAO)", "type": "array", "description": "List of ICAO airport codes (e.g., KJFK, KLAX, KORD). Can be comma-separated string or array.", "items": { "type": "string" }, "default": [ "KJFK" ] }, "modules": { "title": "Data Modules", "type": "array", "description": "Select which aviation data to fetch", "items": { "type": "string", "enum": [ "metar", "taf", "pireps", "sigmets", "airmets", "alerts", "airport_info", "forecast", "flights", "radar" ] }, "default": [ "metar", "taf", "pireps", "sigmets", "airmets", "alerts", "forecast" ] }, "include_traffic": { "title": "Include Traffic Data", "type": "boolean", "description": "Fetch recent arrivals/departures from OpenSky Network", "default": false }, "include_ai_analysis": { "title": "Include AI Analysis", "type": "boolean", "description": "Use AI to generate plain-language briefing and recommendations (requires API key)", "default": true }, "model_name": { "title": "AI Model", "enum": [ "gemini/gemini-2.0-flash", "gpt-4o-mini", "gpt-4o", "claude-3-5-sonnet-20241022" ], "type": "string", "description": "LLM model for analysis", "default": "gemini/gemini-2.0-flash" }, "query": { "title": "Custom Query", "type": "string", "description": "Optional: Specific question about the weather/conditions" }, "route_bbox": { "title": "Route Bounding Box", "type": "array", "description": "Optional: [min_lat, max_lat, min_lon, max_lon] for en-route weather", "items": { "type": "number" } }, "api_key": { "title": "AI API Key", "type": "string", "description": "API key for the selected AI model (Google AI Studio key for Gemini, OpenAI key for GPT, Anthropic key for Claude)" } } }, "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 } } } } } } } } } } ```