How do I install the Linkedin Influencer Discovery skill?

Run npx skillmds@latest add robomotionio/linkedin-influencer-discovery in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

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Is Linkedin Influencer Discovery free to use?

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Who published Linkedin Influencer Discovery?

robomotionio (@robomotionio) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Linkedin Influencer Discovery by robomotionio

Name: Linkedin Influencer Discovery
Author: robomotionio

Linkedin Influencer Discovery

Discover top LinkedIn influencers and voices by topic, industry, country, and follower count — to build the top-N voices in a space, assemble influencer lists for outreach, or identify thought leaders to engage. Apify influencer-database actor primary (accurate follower counts + email flags); keyless web-search degrade.

robomotionio Updated Sep 17, 2026

File contents

LinkedIn Influencer Discovery

Find the top voices for a topic. Primary path is an Apify influencer-database actor (indexed profiles, accurate follower counts and email flags). Degrade is a keyless web search for candidate /in/ profiles — no follower precision, so the agent ranks by serp prominence + relevance instead.

When to use

"Find the top 100 voices in [space]" / "who are the LinkedIn thought leaders for [topic]?"
"Build an influencer list" filtered by follower range / country / email-available.
Pairs with linkedin-profile-post-scraper (enrich recent posts) and kol-discovery (post-engagement-based ranking).

How to run

Python 3 stdlib only — no install:

python3 ${SKILL_DIR}/scripts/influencer_discovery.py \
  --topic "artificial intelligence" \
  --min-followers 50000 --country US --has-email \
  --max-results 100 --output json

Flag	Default	Meaning
`--topic`	(required)	The space, e.g. "saas", "marketing".
`--category`	`""`	Coarse category (technology, business, ...).
`--country` / `--language`	`""`	Geo / language filters.
`--min-followers` / `--max-followers`	`0`	Follower-range filter (0 = unbounded).
`--has-email`	off	Only profiles with an email.
`--max-results`	`100`	Cap (up to 1000).
`--actor`	apimaestro influencers-database	Override Apify actor id.
`--output`	`json`	`json` / `summary`.

Outputs

Ranked by follower count desc: {full_name, username, biography, follower_count, following_count, main_topic, topics, category, linkedin_url, has_email, external_url, country, city, is_verified, source}. source is apify-index or serp-degrade.

Credentials / env

env.required: none — the serp degrade always runs (Robomotion Proxy is platform-provided).
env.optional: APIFY_API_TOKEN — if set → indexed influencer-database actor (accurate follower counts + email flags); else → keyless serp candidate discovery (the default; no follower metrics, so min/max-followers and has-email filters effectively no-op).

Notes & edge cases

The Apify index is pre-built (not live LinkedIn search); follower counts can be cross-platform, not LinkedIn-specific — flagged via source.
min/max-followers and has-email filter client-side after results return.
On the serp degrade there are no follower counts — rank by serp prominence + relevance and treat the list as lower-confidence candidates.

---name: linkedin-influencer-discoverydescription: Discover top LinkedIn influencers and voices by topic, industry, country, and follower count — to build the top-N voices in a space, assemble influencer lists for outreach, or identify thought leaders to engage. Apify influencer-database actor primary (accurate follower counts + email flags); keyless web-search degrade.---# LinkedIn Influencer DiscoveryFind the top voices for a topic. Primary path is an **Apify influencer-database actor**(indexed profiles, accurate follower counts and email flags). Degrade is a **keyless websearch** for candidate `/in/` profiles — no follower precision, so the agent ranks by serpprominence + relevance instead.## When to use- "Find the top 100 voices in [space]" / "who are the LinkedIn thought leaders for [topic]?"- "Build an influencer list" filtered by follower range / country / email-available.- Pairs with `linkedin-profile-post-scraper` (enrich recent posts) and `kol-discovery` (post-engagement-based ranking).## How to runPython 3 stdlib only — no install:```bashpython3 ${SKILL_DIR}/scripts/influencer_discovery.py \ --topic "artificial intelligence" \ --min-followers 50000 --country US --has-email \ --max-results 100 --output json```| Flag | Default | Meaning ||---|---|---|| `--topic` | (required) | The space, e.g. "saas", "marketing". || `--category` | `""` | Coarse category (technology, business, ...). || `--country` / `--language` | `""` | Geo / language filters. || `--min-followers` / `--max-followers` | `0` | Follower-range filter (0 = unbounded). || `--has-email` | off | Only profiles with an email. || `--max-results` | `100` | Cap (up to 1000). || `--actor` | apimaestro influencers-database | Override Apify actor id. || `--output` | `json` | `json` / `summary`. |## OutputsRanked by follower count desc: `{full_name, username, biography, follower_count,following_count, main_topic, topics, category, linkedin_url, has_email, external_url,country, city, is_verified, source}`. `source` is `apify-index` or `serp-degrade`.## Credentials / env- `env.required`: **none** — the serp degrade always runs (Robomotion Proxy is platform-provided).- `env.optional`: `APIFY_API_TOKEN` — **if set → indexed influencer-database actor (accurate follower counts + email flags); else → keyless serp candidate discovery** (the default; no follower metrics, so `min/max-followers` and `has-email` filters effectively no-op).## Notes & edge cases- The Apify index is pre-built (not live LinkedIn search); follower counts can be cross-platform, not LinkedIn-specific — flagged via `source`.- `min/max-followers` and `has-email` filter client-side after results return.- On the serp degrade there are no follower counts — rank by serp prominence + relevance and treat the list as lower-confidence candidates.

Frequently asked questions