All skills
Competitive Intelligence
Distress & Layoff Early-Warning
Detect competitor instability from layoff posts and a surge of 'open to work' employees.
Run this skill with your agent
1. Connect the MCP server
https://apidirect.io/mcp?token=YOUR_API_KEY
2. Then just say to your agent
Is there any sign {competitor} is in trouble or doing layoffs right now?
In clients that support MCP prompts (Claude Desktop, Claude Code, Cursor) this skill appears as a prompt named
distress-layoff-early-warning.
Any agent can also call get_skill(skill_id="distress-layoff-early-warning") to pull these steps on demand.
What it does
Cross-references a rival's employee posts, X chatter and the news wire to catch instability early — useful for displacement, poaching, or de-risking a deal.
Who it's for: Competitive intel, recruiters, and investors tracking a watchlist.
Inputs
| Input | Required | Description | Example |
|---|---|---|---|
competitor |
Yes | The company to watch for distress. | Acme Corp |
How your agent runs it
-
1
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2
search_linkedin(author_company=<company_id>, query="\"open to work\" OR \"impacted\" OR \"laid off\"", get_sentiment=true, sort_by=most_recent)Count employees posting departure/availability signals; a spike + sadness/anger sentiment is the tell.
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3
search_twitter(query="{competitor} layoffs", get_sentiment=true, sort_by=most_recent)Corroborate with public chatter and gauge mood.
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4
search_news(query="{competitor} layoffs", time_published="7d")Confirm against reporting from the last week.
Delivers: A short risk readout: signal count, trend vs baseline, representative posts/articles, and a confidence call.
Tools used
The full playbook
This is exactly what the MCP returns to your agent (via the distress-layoff-early-warning prompt or get_skill tool), with your inputs filled in.
SKILL: Distress & Layoff Early-Warning
Cross-references a rival's employee posts, X chatter and the news wire to catch instability early — useful for displacement, poaching, or de-risking a deal.
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:
- competitor (required): <competitor — ASK THE USER>
STEPS:
1. Tool `search_linkedin_companies` — search_linkedin_companies(query="<competitor>")
Resolve the company_id.
2. Tool `search_linkedin` — search_linkedin(author_company=<company_id>, query="\"open to work\" OR \"impacted\" OR \"laid off\"", get_sentiment=true, sort_by=most_recent)
Count employees posting departure/availability signals; a spike + sadness/anger sentiment is the tell.
3. Tool `search_twitter` — search_twitter(query="<competitor> layoffs", get_sentiment=true, sort_by=most_recent)
Corroborate with public chatter and gauge mood.
4. Tool `search_news` — search_news(query="<competitor> layoffs", time_published="7d")
Confirm against reporting from the last week.
DELIVER: A short risk readout: signal count, trend vs baseline, representative posts/articles, and a confidence call.
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.