Market Research & Trends

Hiring-as-Demand Leading Indicator

Turn fresh job-posting velocity for an emerging skill into a forward demand signal — triangulated with news and practitioner chatter — and a map of who is investing

What it does

Companies hire ahead of building. Counting fresh, last-7-day LinkedIn job posts for an emerging skill against a 30-day baseline reveals hiring velocity, and profiling the posting companies maps who is betting before the market notices. Optional news and Reddit passes confirm the same demand from the corporate-investment and practitioner angles, so you act on accelerating trends, not single-platform noise.

Who it's for: VCs, market analysts, and competitive-intel teams

Inputs

How your agent runs it

1. 1
   search_linkedin_jobs

   search_linkedin_jobs(query={emerging_skill}, posted_ago=7d, sort_by=most_recent, location_id={location_id})

   Core: count fresh postings this week and capture the hiring companies as the leading-edge demand signal.

2. 2
   search_linkedin_jobs

   search_linkedin_jobs(query={emerging_skill}, posted_ago=30d, sort_by=most_recent, location_id={location_id})

   Core: pull the 30-day window as a baseline so the 7-day count becomes a velocity / acceleration ratio.

3. 3
   search_linkedin_companies

   search_linkedin_companies(query=<top hiring company name>)

   Core: resolve each leading employer's name from the job results to its numeric company id and company url.

4. 4
   linkedin_company_details

   linkedin_company_details(url=<company url>)

   Core: profile the biggest investors in the skill (size, founded year, specialities, similar_companies) to map who is betting and which adjacent firms to watch.

5. 5
   search_news

   search_news(query={emerging_skill}, time_published=7d)

   Only run if {platforms} includes news: corroborate the corporate-investment axis — surface this week's funding rounds, hiring announcements, and product launches around the skill to confirm which companies are betting and whether momentum is building.

6. 6
   search_reddit

   search_reddit(query={emerging_skill}, sort_by=most_recent, get_sentiment=true)

   Only run if {platforms} includes reddit: capture the grassroots practitioner-demand axis — 'who's hiring', adoption, and learning threads with sentiment — as an independent confirmation that demand is accelerating beyond corporate hiring.

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 LinkedIn job posts for an emerging skill against a 30-day baseline reveals hiring velocity, and profiling the posting companies maps who is betting before the market notices. Optional news and Reddit passes confirm the same demand from the corporate-investment and practitioner angles, so you act on accelerating trends, not single-platform noise.

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>
      The skill, role, or technology to track as a demand proxy
  - location_id (optional): (optional — e.g. 103644278 (United States))
      Numeric LinkedIn location id to scope the hiring market (resolve via the linkedin-job-locations doc)
  - platforms (optional): (optional — e.g. news, reddit)
      Comma-separated list of platforms to run — options: linkedin, news, reddit. Omit to run all of them; name specific platforms to limit the run.

PLATFORM SELECTION: some steps are gated with "Only run if {platforms} includes X". If the user supplied a `platforms` value, run only the steps whose platform is listed. If the user did NOT supply `platforms`, run ALL steps — every platform the skill supports.

STEPS:
  1. Tool `search_linkedin_jobs` — search_linkedin_jobs(query={emerging_skill}, posted_ago=7d, sort_by=most_recent, location_id={location_id})
     Core: 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})
     Core: pull the 30-day window as a baseline so the 7-day count becomes a velocity / acceleration ratio.
  3. Tool `search_linkedin_companies` — search_linkedin_companies(query=<top hiring company name>)
     Core: resolve each leading employer's name from the job results to its numeric company id and company url.
  4. Tool `linkedin_company_details` — linkedin_company_details(url=<company url>)
     Core: profile the biggest investors in the skill (size, founded year, specialities, similar_companies) to map who is betting and which adjacent firms to watch.
  5. Tool `search_news` — search_news(query={emerging_skill}, time_published=7d)
     Only run if {platforms} includes news: corroborate the corporate-investment axis — surface this week's funding rounds, hiring announcements, and product launches around the skill to confirm which companies are betting and whether momentum is building.
  6. Tool `search_reddit` — search_reddit(query={emerging_skill}, sort_by=most_recent, get_sentiment=true)
     Only run if {platforms} includes reddit: capture the grassroots practitioner-demand axis — 'who's hiring', adoption, and learning threads with sentiment — as an independent confirmation that demand is accelerating beyond corporate hiring.

DELIVER: A weekly demand-velocity readout for the skill, a ranked list of the companies hiring hardest against it, and the adjacent firms likely to follow — plus, when enabled, corroborating corporate-investment (news) and practitioner-demand (Reddit) signals confirming whether the trend is real and accelerating.

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.