Hiring-as-Demand Leading Indicator
Turn fresh job-posting velocity for an emerging skill into a forward demand signal and a map of who is investing
https://apidirect.io/mcp?token=YOUR_API_KEY
Track weekly hiring velocity for {emerging_skill} in {location_id} and tell me which companies are investing ahead of the market
hiring-demand-leading-indicator.
Any agent can also call get_skill(skill_id="hiring-demand-leading-indicator") to pull these steps on demand.
What it does
Companies hire ahead of building. Counting fresh, last-7-day job posts for an emerging skill, then profiling the companies posting them, reveals demand momentum and which firms are betting before the market notices.
Who it's for: VCs, market analysts, and competitive-intel teams
Inputs
| Input | Required | Description | Example |
|---|---|---|---|
emerging_skill |
Yes | The skill, role, or technology to track as a demand proxy | RAG engineer |
location_id |
No | Numeric LinkedIn location id to scope the market (resolve via the linkedin-job-locations doc) | 103644278 (United States) |
How your agent runs it
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1
search_linkedin_jobs(query={emerging_skill}, posted_ago=7d, sort_by=most_recent, location_id={location_id})Count fresh postings this week and capture the hiring companies as the leading-edge demand signal.
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2
search_linkedin_jobs(query={emerging_skill}, posted_ago=30d, sort_by=most_recent, location_id={location_id})Pull the 30-day window as a baseline so the 7-day count can be expressed as a velocity or acceleration ratio.
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3
linkedin_job_details(url=<job url>)Open a sample of the freshest postings to extract seniority, comp, and responsibilities that reveal what is actually being built.
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4
search_linkedin_companies(query=<top hiring company name>)Resolve each leading employer's name to its numeric company id and company url.
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5
linkedin_company_details(url=<company url>)Profile the biggest investors in the skill (size, founded year, specialities, similar_companies) to map who is betting and which adjacent firms to watch.
Tools used
The full playbook
This is exactly what the MCP returns to your agent (via the hiring-demand-leading-indicator prompt or get_skill tool), with your inputs filled in.
SKILL: Hiring-as-Demand Leading Indicator
Companies hire ahead of building. Counting fresh, last-7-day job posts for an emerging skill, then profiling the companies posting them, reveals demand momentum and which firms are betting before the market notices.
You are running this skill on API Direct via its MCP tools. Execute the steps below yourself by calling the named tools in order — values in <angle brackets> come from a previous step. Then deliver the result described at the end.
INPUTS:
- emerging_skill (required): <emerging_skill — ASK THE USER>
- location_id (optional): (optional — e.g. 103644278 (United States))
STEPS:
1. Tool `search_linkedin_jobs` — search_linkedin_jobs(query=<emerging_skill>, posted_ago=7d, sort_by=most_recent, location_id=<location_id>)
Count fresh postings this week and capture the hiring companies as the leading-edge demand signal.
2. Tool `search_linkedin_jobs` — search_linkedin_jobs(query=<emerging_skill>, posted_ago=30d, sort_by=most_recent, location_id=<location_id>)
Pull the 30-day window as a baseline so the 7-day count can be expressed as a velocity or acceleration ratio.
3. Tool `linkedin_job_details` — linkedin_job_details(url=<job url>)
Open a sample of the freshest postings to extract seniority, comp, and responsibilities that reveal what is actually being built.
4. Tool `search_linkedin_companies` — search_linkedin_companies(query=<top hiring company name>)
Resolve each leading employer's name to its numeric company id and company url.
5. Tool `linkedin_company_details` — linkedin_company_details(url=<company url>)
Profile the biggest investors in the skill (size, founded year, specialities, similar_companies) to map who is betting and which adjacent firms to watch.
DELIVER: A weekly demand-velocity readout for the skill plus a ranked list of the companies hiring hardest against it and the adjacent firms likely to follow.
Note: each underlying tool call is billed at its normal endpoint price; get_sentiment adds a small per-page surcharge. Page through results as needed but stop once you have enough to deliver the outcome.