# Data Jobs — Skill Co-Occurrence API (AU) (`data_lattice_au/skill-cooccurrence-api`) Actor Which skills should you learn next? Pass any data, analytics, BI, ML or AI skill and get the strongest co-occurring tools and techniques in the live Australian market — ranked by PMI and Jaccard. Powers learning paths, course catalogues and recruiter brief templates. - **URL**: https://apify.com/data\_lattice\_au/skill-cooccurrence-api.md - **Developed by:** [data\_lattice](https://apify.com/data_lattice_au) (community) - **Categories:** Jobs, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $70.00 / 1,000 results This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. 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 ## Data Jobs - Skill Co-Occurrence API (AU) Which skills should you learn next? Pass any data, analytics, BI, ML or AI skill and get the strongest co-occurring tools and techniques in the live Australian market - ranked by PMI and Jaccard. ### What this is for The skill-intelligence Actor produces a single 80 KB JSON snapshot per day. Querying it is fiddly: you have to download it, parse the `top_pairs` array, filter by skill name on either side of each pair, sort, and slice. This Actor is a thin query-and-return layer on top of that snapshot. Pass `["dbt", "Airflow"]`; get back a clean response showing the top 10 co-occurring skills for each, with PMI / Jaccard / joint-count metrics. Powers learning paths, recruiter-brief generation, and skill-adjacency visualisations without you having to build any of that data plumbing. ### How this helps you | You are a... | You use this to... | |---|---| | Bootcamp curriculum designer | "If we teach Snowflake, what skill bundle should accompany it?" Answer: dbt (PMI 2.35), Airflow (1.79), Python (1.00) | | Career coach | Generate "next skill to learn" recommendations grounded in real demand data, not opinion | | Recruiter | Build candidate briefs that name *every* skill clients will likely ask for, including ones the JD didn't explicitly state | | EdTech / LMS | Automatically suggest related courses when a learner finishes a unit | | Job seeker | Answer "I know X - what should I learn to round out my profile?" with market-grounded data | | Data team lead | Justify hiring "Snowflake + dbt" as a unified profile rather than two separate roles | ### Cold-start safe Three fallback layers in order: 1. Apify key-value store entry `latest` (set by the Skill Intelligence Actor when it last ran) 2. Local `/var/data_intel/latest.json` (when running as the cron daemon) 3. Shipped seed snapshot (`seed_intelligence.json` - 3,113 jobs, 136 skills, 120 pairs) So buyers see a useful, accurate response on first run, even before the Skill Intelligence Actor has triggered any refresh. ### Output ```jsonc { "generated_at": "2026-05-09T...", "source": "kvs:latest", "is_seed": false, "per_skill": { "snowflake": { "in_market": 165, "top_related": [ {"skill": "dbt", "joint": 65, "pmi": 2.35, "jaccard": 0.299}, {"skill": "AWS", "joint": 67, "pmi": 1.57, "jaccard": 0.186}, {"skill": "Azure", "joint": 76, "pmi": 1.18, "jaccard": 0.143}, ... ] }, "rag": { ... }, ... } } ```` Full schema in `OUTPUT_SCHEMA.json`. ### Pricing **$0.10 per query** (PAY\_PER\_EVENT, fired as `query_resolved` - one event per Actor run regardless of how many skills you queried). A power user querying 100 times in a day pays ~$10/month. A casual user pays cents. ### Run it ```json { "skills": ["RAG", "Snowflake", "dbt", "GenAI", "embedding"] } ``` Returns the top 10 co-occurring skills for each input, in one response. ### Brand Part of the [Data Lattice](https://datalattice.com.au) portfolio. Consumes the snapshot produced by the [Skill Intelligence Actor](https://apify.com/data_lattice_au/au-data-skill-intelligence). # Actor input Schema ## `skills` (type: `array`): Skills to find co-occurring tools/techniques for. Returns top related skills by PMI and Jaccard. ## Actor input object example ```json { "skills": [ "Python", "SQL", "Power BI" ] } ``` # Actor output Schema ## `generated_at` (type: `string`): ISO 8601 timestamp of the underlying intelligence snapshot. ## `source` (type: `string`): Where the snapshot came from (kvs:latest, local file, or seed). ## `is_seed` (type: `string`): True when this query was answered from the baked cold-start seed. ## `per_skill` (type: `string`): Map of input-skill -> related-skill list. # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = {}; // Run the Actor and wait for it to finish const run = await client.actor("data_lattice_au/skill-cooccurrence-api").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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("data_lattice_au/skill-cooccurrence-api").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 data_lattice_au/skill-cooccurrence-api --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=data_lattice_au/skill-cooccurrence-api", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Data Jobs — Skill Co-Occurrence API (AU)", "description": "Which skills should you learn next? Pass any data, analytics, BI, ML or AI skill and get the strongest co-occurring tools and techniques in the live Australian market — ranked by PMI and Jaccard. Powers learning paths, course catalogues and recruiter brief templates.", "version": "0.48", "x-build-id": "zvYr6ZE5jGkQhrDt0" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/data_lattice_au~skill-cooccurrence-api/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-data_lattice_au-skill-cooccurrence-api", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/data_lattice_au~skill-cooccurrence-api/runs": { "post": { "operationId": "runs-sync-data_lattice_au-skill-cooccurrence-api", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/data_lattice_au~skill-cooccurrence-api/run-sync": { "post": { "operationId": "run-sync-data_lattice_au-skill-cooccurrence-api", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "skills" ], "properties": { "skills": { "title": "Seed skills", "type": "array", "description": "Skills to find co-occurring tools/techniques for. Returns top related skills by PMI and Jaccard.", "default": [ "Python", "SQL", "Power BI" ], "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 } } } } } } } } } } ```