{ "openapi": "3.0.1", "info": { "title": "Job Market Intelligence", "description": "Aggregate job listings from four free data sources, deduplicate them, and generate a structured intelligence report with skill demand rankings, salary benchmarks, top hiring companies, and remote-work statistics — all without any API keys.", "version": "1.0", "x-build-id": "eHEXGpHQKUQYlFiIg" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/ryanclinton~job-market-intelligence/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-ryanclinton-job-market-intelligence", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/ryanclinton~job-market-intelligence/runs": { "post": { "operationId": "runs-sync-ryanclinton-job-market-intelligence", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/ryanclinton~job-market-intelligence/run-sync": { "post": { "operationId": "run-sync-ryanclinton-job-market-intelligence", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "query" ], "properties": { "query": { "title": "Search Query", "type": "string", "description": "Job search query (e.g., 'software engineer', 'data scientist', 'product manager')", "default": "software engineer" }, "location": { "title": "Location", "type": "string", "description": "Location filter (e.g., 'San Francisco', 'Remote', 'New York')" }, "companyName": { "title": "Company Name", "type": "string", "description": "Filter by specific company name" }, "remoteOnly": { "title": "Remote Only", "type": "boolean", "description": "Only show remote jobs", "default": false }, "datePosted": { "title": "Date Posted", "enum": [ "day", "week", "month", "any" ], "type": "string", "description": "How recent should the job postings be", "default": "month" }, "sources": { "title": "Sources", "type": "array", "description": "Which sources to search (leave empty for all). Available: remotive, arbeitnow, jobicy, hn-whoishiring", "items": { "type": "string" } }, "analyzeSkills": { "title": "Analyze Skills", "type": "boolean", "description": "Extract and rank mentioned skills from job descriptions", "default": true }, "analyzeSalaries": { "title": "Analyze Salaries", "type": "boolean", "description": "Extract and summarize salary data from job postings", "default": true }, "maxResults": { "title": "Max Results", "minimum": 1, "maximum": 500, "type": "integer", "description": "Maximum number of job listings to return. Analytics quality plateaus past ~200 — going higher mostly increases runtime, not insight.", "default": 100 }, "sourceWeights": { "title": "Source Weights", "type": "object", "description": "Per-source sub-sampling fraction (0..1). Use this to down-weight noisier sources — e.g. {\"hn-whoishiring\": 0.5} keeps roughly half of HN listings while taking the other sources whole. Sources not listed here pass through at full weight. Sub-sampling is deterministic via per-listing hash, so re-runs are reproducible. ⚠️ Use only when you intentionally want a representative sample, not complete coverage — sub-sampling drops listings, so cohort size shrinks." }, "customSkills": { "title": "Custom Skills", "type": "array", "description": "Add domain-specific skills to detect alongside the built-in 80+ technologies. Each entry: { name, regex, category? }. Example: [{\"name\": \"Snowpark\", \"regex\": \"\\\\bsnowpark\\\\b\", \"category\": \"Data\"}]. Categories: Languages, Frameworks, Cloud, Data, AI/ML, Other (default). Invalid regex entries are logged and skipped." }, "groupBy": { "title": "Segment By", "type": "array", "description": "Split analytics into per-segment reports — fixes the cohort-mixing distortion when one query spans regions / seniorities / job types. Pick one or more dimensions; output adds a `segments[]` array with per-segment salary, top skills, seniority breakdown, and remote percentage. Empty = single cohort.", "items": { "type": "string" } }, "enableHistoricalTracking": { "title": "Enable Historical Tracking", "type": "boolean", "description": "Persist a cohort snapshot to a named KV store (job-market-intelligence-history) and compute trend insights against the previous run. Adds: salaryMedianChange, listingGrowthRate, topRising/FallingSkills, newCompanies/departedCompanies, direction (expanding/stable/tightening). First run with this on returns trendInsights=null and writes the baseline snapshot.", "default": false }, "historyStateKey": { "title": "History State Key", "type": "string", "description": "Override the auto-derived snapshot key (default: hash of query + location). Use a stable string when monitoring the same cohort across runs with slightly different filters." }, "incremental": { "title": "Incremental Mode", "type": "boolean", "description": "When enabled together with historical tracking, drops listings whose URLs were already returned in the previous run. Reduces downstream processing/noise — only fresh listings reach your dataset/pipelines (all sources are still fetched in full so analytics like trend insights remain accurate). First run for a key returns everything (no prior URLs to filter).", "default": false }, "lookbackDays": { "title": "Lookback Days", "minimum": 1, "maximum": 365, "type": "integer", "description": "Maximum age of the prior snapshot for trend computation. Snapshots older than this are treated as a first run. Default 30.", "default": 30 }, "mode": { "title": "Mode (Persona Preset)", "enum": [ "default", "job_seeker", "recruiter", "analyst" ], "type": "string", "description": "Reorders the recommendedActions[] block by audience priority. default = balanced; job_seeker = bubbles learn-skill / apply-now / curriculum actions; recruiter = bubbles salary-band / hiring-velocity / role-spec actions; analyst = bubbles monitoring / strategy / regime actions. The full action list is always emitted — mode only affects ordering.", "default": "default" }, "eventThresholds": { "title": "Event Thresholds", "type": "object", "description": "Override the default thresholds that trigger entries in the events[] array. Defaults: salarySpikePercent 5, salaryDropPercent -5, listingGrowthSpikePercent 25, listingDropPercent -25, remoteShiftPoints 5, skillEmergenceDeltaPercent 100. Example: {\"salarySpikePercent\": 3, \"listingGrowthSpikePercent\": 10} for noisier alerting." }, "whatIfScenarios": { "title": "What-If Scenarios", "type": "array", "description": "Counterfactual scenarios to evaluate against the cohort. Each: { type: 'salary_change' | 'skill_emphasis', percent? (for salary_change), skill? (for skill_emphasis), constraints?: { maxPercent?, minPercent? } }. When omitted, the actor auto-generates 2–4 representative scenarios. Outputs are derivable-only (percentile shift, tier change, scarcity match) — never forecasts. 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