Executive Mention Tracker
Catch every mention of a named leader across LinkedIn, X, news, and Reddit and split the wins from the reputation risks
https://apidirect.io/mcp?token=YOUR_API_KEY
Track everything being said about {exec_name} ({exec_linkedin_url}) this week and flag any reputation risks before they spread
executive-mention-tracker.
Any agent can also call get_skill(skill_id="executive-mention-tracker") to pull these steps on demand.
What it does
An executive's reputation is shaped by what others say, not what they post. This watches mentions of a named leader across four networks at once and sorts them into wins to amplify and risks to get ahead of.
Who it's for: Comms teams and chiefs of staff protecting a named executive
Inputs
| Input | Required | Description | Example |
|---|---|---|---|
exec_name |
Yes | Full name of the executive to monitor | Jane Okafor |
exec_linkedin_url |
Yes | LinkedIn profile URL or slug used to match LinkedIn mentions of the person | https://www.linkedin.com/in/janeokafor |
How your agent runs it
-
1
search_linkedin(mentions_member={exec_linkedin_url}, get_sentiment=true, sort_by=most_recent)Pull posts that tag the exec and split positive endorsements (wins) from negative posts (risk).
-
2
search_twitter(query="{exec_name}", get_sentiment=true, sort_by=most_recent, pages=4)Capture public chatter and flag anger/negative items with high engagement for review.
-
3
search_news(query="{exec_name}", time_published=7d, limit=30)Surface earned/press mentions from the past week and note outlet tone and reach.
-
4
search_reddit(query="{exec_name}", get_sentiment=true, sort_by=most_recent)Pull candid community threads and flag negative or anger-dominant posts as emerging risks.
Tools used
The full playbook
This is exactly what the MCP returns to your agent (via the executive-mention-tracker prompt or get_skill tool), with your inputs filled in.
SKILL: Executive Mention Tracker
An executive's reputation is shaped by what others say, not what they post. This watches mentions of a named leader across four networks at once and sorts them into wins to amplify and risks to get ahead of.
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:
- exec_name (required): <exec_name — ASK THE USER>
- exec_linkedin_url (required): <exec_linkedin_url — ASK THE USER>
STEPS:
1. Tool `search_linkedin` — search_linkedin(mentions_member=<exec_linkedin_url>, get_sentiment=true, sort_by=most_recent)
Pull posts that tag the exec and split positive endorsements (wins) from negative posts (risk).
2. Tool `search_twitter` — search_twitter(query="<exec_name>", get_sentiment=true, sort_by=most_recent, pages=4)
Capture public chatter and flag anger/negative items with high engagement for review.
3. Tool `search_news` — search_news(query="<exec_name>", time_published=7d, limit=30)
Surface earned/press mentions from the past week and note outlet tone and reach.
4. Tool `search_reddit` — search_reddit(query="<exec_name>", get_sentiment=true, sort_by=most_recent)
Pull candid community threads and flag negative or anger-dominant posts as emerging risks.
DELIVER: A weekly executive-reputation brief that sorts every mention into wins, neutral, and risks, with the highest-reach negatives flagged for response
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.