Layoff Wave Interceptor

SKILL: Layoff Wave Interceptor
News breaks layoffs faster than social does; by resolving each affected company to its numeric LinkedIn company_id and scanning fresh 'open to work' / 'impacted' posts -- and optionally extending the sweep to X and Reddit, where many people announce availability before updating LinkedIn -- you reach displaced candidates while they are still on the market and before the inbound rush.

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:
  - sector (required): <sector — ASK THE USER>
      Industry or sector to monitor for layoff announcements
  - role (required): <role — ASK THE USER>
      Target job title to prioritize among the impacted employees
  - platforms (optional): (optional — e.g. linkedin, twitter, reddit)
      Comma-separated list of platforms to run — options: news, linkedin, twitter, reddit. Omit to run all of them; name specific platforms to limit the run.
  - freshness (optional): 7d (default — override if you like)
      How far back to scan layoff news, mapped to search_news time_published. One of 1h, 1d, 7d, 1y, anytime. Defaults to 7d.
  - country (optional): (optional — e.g. us)
      Optional ISO country code to geo-narrow the layoff news scan (search_news country param) for region-specific recruiting. Omit for global.

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_news` — search_news(query="{sector} layoffs", time_published=7d, limit=50)
     Identify companies that announced workforce reductions within 7d and extract each affected company name. Pass {country} only when a region is specified; default time_published to 7d if 7d is omitted.
  2. Tool `search_linkedin_companies` — search_linkedin_companies(query=<company name>)
     Resolve each affected company from the news into its numeric LinkedIn company_id.
  3. Tool `search_linkedin` — search_linkedin(query="open to work", author_company=<company_id>, author_title={role}, get_sentiment=true, sort_by=most_recent)
     Surface impacted employees who just announced availability; keep the most recent posts matching the target role.
  4. Tool `linkedin_person_posts` — linkedin_person_posts(url=<person_url>, get_sentiment=true)
     Confirm the person is genuinely displaced and currently active before adding them to the outreach list.
  5. Tool `search_twitter` — search_twitter(query="{sector} laid off open to work {role}", sort_by=most_recent, get_sentiment=true)
     Only run if {platforms} includes twitter: catch displaced {role} talent announcing layoffs or availability on X, where many post days before updating LinkedIn. Keep the most recent, negatively-tilted-but-active posts.
  6. Tool `search_reddit` — search_reddit(query="laid off {sector} {role} open to work", sort_by=most_recent, get_sentiment=true)
     Only run if {platforms} includes reddit: surface freshly laid-off {role} candidates posting in layoff and career communities for an extra sourcing channel beyond LinkedIn.

DELIVER: A time-ranked shortlist of freshly displaced {role} candidates from companies that just announced {sector} layoffs, drawn from LinkedIn and (optionally) X and Reddit, with profile/post links and recency signals.

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.