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Apify job search api

Skill johnisanerd/claude-skill-job-search-api/apify-job-search-api

Claude/agent skill: turn Google Jobs into a job search API feed for apps and AI agents. Installs via npx skills add.

Install
npx -y skills add johnisanerd/claude-skill-job-search-api --skill apify-job-search-api

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What its author says it does

Copied from the file, not written here

Turn Google Jobs into a job search API for your app, AI agent, or alerts pipeline with the Apify Google Jobs Scraper Actor (johnvc/Google-Jobs-Scraper). Query by role and location with country, language, and radius filters, and get fresh structured listings back, including title, company_name, source platform, posted_at, schedule_type, and direct apply_options links, ready to dedupe by job_id and serve as a feed. Use when the user wants a job search api, a job postings API or job listings feed for an application or AI agent, wants to refresh a job board or job alerts programmatically, or asks which job search sites have an API. Pay-per-page billing, MCP-ready for Claude and other AI agents.

The file declares its own license as MIT. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

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Job Search API: A Live Job Feed From Google Jobs

Use Google Jobs as a job search API. One call takes a role and location and returns fresh, structured listings with direct apply links, ready to serve to an app, an AI agent, a job board, or an alerts pipeline.

When to use this skill

  • The user wants a job search API or a job postings feed for an application, bot, or AI agent.
  • They want to refresh a job board, careers digest, or alerts pipeline on a schedule.
  • They want fresh openings for a role filtered to a location, language, or radius.
  • They ask "which job search sites have an API" or "how do I search job listings by API".

Not for: one-off bulk exports of a single search (use the scrape-google-jobs skill), salary analytics (no numeric salary field), or LinkedIn-only listings (use the LinkedIn Jobs API).

What the feed returns (one row per listing)

title, company_name, location, via (source platform), description, job_highlights, extensions, detected_extensions (posted_at, schedule_type, plus benefit flags such as health_insurance only when a listing advertises them), apply_options (per-platform direct apply links), job_id, share_link. job_id is the dedupe key; posted_at ("3 days ago") is the freshness signal.

Prerequisites

The Actor

Run it with the Apify CLI

One feed call, bounded to about three pages:

apify actors call "johnvc/Google-Jobs-Scraper" -i '{"query":"registered nurse","location":"Dallas, TX","num_results":30,"max_pagination":3}' \
  --json \
  --user-agent apify-awesome-skills/apify-job-search-api \
  2>/dev/null

Read the newest run's items later, for example from a scheduled run:

apify datasets get-items <DATASET_ID> --format json --user-agent apify-awesome-skills/apify-job-search-api 2>/dev/null

Every call carries the three flags this repo expects: --json (or --format json), --user-agent apify-awesome-skills/apify-job-search-api, and 2>/dev/null.

Run it from Claude or another AI agent (MCP)

The Actor is MCP-ready. Add the hosted server URL:

https://mcp.apify.com/?tools=actors,docs,johnvc/Google-Jobs-Scraper

Then ask, for example: "Search jobs for senior accountant roles in Chicago posted this week and give me the apply links." MCP setup docs: https://docs.apify.com/platform/integrations/mcp

Workflow

  1. Define the feed. One Actor input per feed: query (role or skill), location, and localization (google_domain, language) for non-US markets. A radius feed adds include_lrad and lrad_value.
  2. Bound each poll. For a feed you refresh often, keep num_results at 20 to 50 and set max_pagination (2 to 5) as the hard cost cap per call.
  3. Estimate cost per poll and per month, then confirm with the user. See references/gotchas.md.
  4. Call the Actor, read the dataset, and post-process: dedupe against your store on job_id, parse posted_at relative ages ("3 days ago") to timestamps, and keep only rows newer than your last poll.
  5. Serve the result. Hand rows to the app or agent, write them to your database, or deliver a digest. For recurring feeds, wrap the same input in an Apify Schedule or a cron job.

Inputs

  • query (string, required): role, skill, or company
  • location (string): city, state, or country
  • country, language, google_domain (enums): localization
  • num_results (integer, default 100): cap per call, keep small for feeds
  • max_pagination (integer): hard page cap per call, the cost bound
  • include_lrad (boolean) plus lrad_value (string, km): radius feeds

Cost

Billing is per page processed, roughly 10 listings per page, so a bounded feed poll of 30 results is about three pages. A daily 3-page poll is roughly 90 pages per month; estimate with the live per-page price in references/gotchas.md before scheduling.

Honest limits

  • This is a polled feed, not a push API: freshness is your poll interval.
  • posted_at is a relative string; the workflow converts it client-side. There is no server-side date filter.
  • No numeric salary field and no experience-level field.
  • Inventory varies by region and query; some polls legitimately return nothing new.

Troubleshooting

  • Duplicate listings across polls: expected; dedupe on job_id before serving.
  • Nothing new in a poll: normal for narrow queries; widen the query or slow the poll.
  • Budget warning at startup: raise the run budget or lower num_results / max_pagination.

See references/gotchas.md for cost guardrails and error recovery, and references/actor-index.md for the Actor routing table.

Related job-data Actors

What ships with it: 2 files

3.8 KB alongside SKILL.md

references/

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Said here and by no other author read

  • Set query, location, and localization per feed
  • Keep num_results between 20 and 50
  • Set max_pagination as the hard cost cap
  • Estimate and confirm polling costs with the user
  • Dedupe results on job_id
  • Parse posted_at relative ages to timestamps

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