keyapi-linkedin-user-analytics

Discover, profile, and deeply analyze LinkedIn professionals — from identity resolution and career history to content activity, social graph metrics, and interest mapping.

This skill provides comprehensive LinkedIn user intelligence using the KeyAPI MCP service. It enables retrieval of full professional profiles, contact details, follower/connection counts, published posts, comments, videos, images, work experience, education, skills, certifications, publications, honors, recommendations, and interest groups — all through a unified, cache-first workflow.

Use this skill when you need to:

• Retrieve a complete professional profile for a LinkedIn user by username
• Audit a user's career history, education background, and skill endorsements
• Analyze a professional's published content — posts, comments, videos, and images
• Research contact information, follower counts, and connection network size
• Discover a user's certifications, publications, honors, and recommendations
• Map professional interests via followed groups and companies
• Search and discover LinkedIn professionals by name, title, company, or industry

author: KeyAPI license: MIT repository: https://github.com/EchoSell/keyapi-skills

Prerequisites

author: KeyAPI license: MIT repository: https://github.com/EchoSell/keyapi-skills

MCP Server Configuration

All tool calls in this skill target the KeyAPI LinkedIn MCP server:

Server URL : https://mcp.keyapi.ai/linkedin/mcp
Auth Header: Authorization: Bearer $KEYAPI_TOKEN

Setup (one-time):

# 1. Install dependencies
npm install

# 2. Set your API token (get one free at https://keyapi.ai/)
export KEYAPI_TOKEN=your_token_here

# 3. List all available tools to verify the connection
node scripts/run.js --platform linkedin --list-tools

author: KeyAPI license: MIT repository: https://github.com/EchoSell/keyapi-skills

Analysis Scenarios

author: KeyAPI license: MIT repository: https://github.com/EchoSell/keyapi-skills

Workflow

Step 1 — Identify Analysis Targets and Select Nodes

Clarify the research objective and map it to one or more nodes. Typical entry points:

• Profile lookup: Use get_user_profile with username. Enable include_* flags to fetch additional sections in a single call.
• Content audit: Combine get_user_posts + get_user_comments + get_user_videos + get_user_images.
• Career deep-dive: Use get_user_experience + get_user_educations + get_user_skills + get_user_certifications.
• Reputation research: Use get_user_recommendations + get_user_honors + get_user_publications.
• Interest mapping: Use get_user_interests_groups + get_user_interests_companies.
• People discovery: Use search_people with name, title, company, or industry filters.

Critical: Two Identifier Types

LinkedIn endpoints use two distinct identifiers:

• username — the handle from the profile URL (e.g., https://www.linkedin.com/in/jack → jack). Used by get_user_profile, get_user_contact_information, get_user_follower_and_connection.
• urn — an internal opaque identifier (e.g., ACoAABCtiL8B26nfi3Nbpo_AM8ngg4LeClT1Wh8). Required by all other user nodes.

Always call get_user_profile first with the username to obtain the urn before calling any urn-based endpoint.

get_user_profile Efficiency Tip

get_user_profile supports optional include_* boolean flags: include_follower_and_connection, include_experiences, include_skills, include_certifications, include_publications, include_educations, include_volunteers, include_honors, include_interests, include_bio. Enable these to retrieve multiple sections in a single API call and reduce total request count.

Step 2 — Retrieve API Schema

Before calling any node, inspect its input schema to confirm required parameters and available options:

node scripts/run.js --platform linkedin --schema <tool_name>

# Examples
node scripts/run.js --platform linkedin --schema get_user_profile
node scripts/run.js --platform linkedin --schema search_people

Step 3 — Call APIs and Cache Results Locally

Execute tool calls and persist responses to the local cache to avoid redundant API calls.

Calling a tool:

# Single call with pretty output
node scripts/run.js --platform linkedin --tool <tool_name> \
  --params '<json_args>' --pretty

# Force fresh data, skip cache
node scripts/run.js --platform linkedin --tool <tool_name> \
  --params '<json_args>' --no-cache --pretty

Example — get full profile with experience and skills:

node scripts/run.js --platform linkedin --tool get_user_profile \
  --params '{"username":"jack","include_experiences":true,"include_skills":true}' --pretty

Example — get user posts (first page):

node scripts/run.js --platform linkedin --tool get_user_posts \
  --params '{"urn":"ACoAABCtiL8B26nfi3Nbpo_AM8ngg4LeClT1Wh8","page":1}' --pretty

Example — get next page using pagination token:

node scripts/run.js --platform linkedin --tool get_user_posts \
  --params '{"urn":"ACoAABCtiL8B26nfi3Nbpo_AM8ngg4LeClT1Wh8","page":2,"pagination_token":"<token_from_previous_response>"}' --pretty

Example — search people by title and company:

node scripts/run.js --platform linkedin --tool search_people \
  --params '{"title":"CEO","company":"OpenAI","page":1}' --pretty

Example — get received recommendations:

node scripts/run.js --platform linkedin --tool get_user_recommendations \
  --params '{"urn":"ACoAAC3iNKcB3qbWJrP7K5Z3i89AF5c1snr8bhc","type":"received","page":1}' --pretty

Pagination:

Most user content endpoints use a hybrid pagination model:

Cache directory structure:

.keyapi-cache/
└── YYYY-MM-DD/
    ├── get_user_profile/
    │   └── {params_hash}.json
    ├── get_user_about/
    │   └── {params_hash}.json
    ├── get_user_contact_information/
    │   └── {params_hash}.json
    ├── get_user_follower_and_connection/
    │   └── {params_hash}.json
    ├── get_user_posts/
    │   └── {params_hash}.json
    ├── get_user_comments/
    │   └── {params_hash}.json
    ├── get_user_videos/
    │   └── {params_hash}.json
    ├── get_user_images/
    │   └── {params_hash}.json
    ├── get_user_experience/
    │   └── {params_hash}.json
    ├── get_user_skills/
    │   └── {params_hash}.json
    ├── get_user_educations/
    │   └── {params_hash}.json
    ├── get_user_publications/
    │   └── {params_hash}.json
    ├── get_user_certifications/
    │   └── {params_hash}.json
    ├── get_user_honors/
    │   └── {params_hash}.json
    ├── get_user_recommendations/
    │   └── {params_hash}.json
    ├── get_user_interests_groups/
    │   └── {params_hash}.json
    ├── get_user_interests_companies/
    │   └── {params_hash}.json
    └── search_people/
        └── {params_hash}.json

Cache-first policy:

Before every API call, check whether a cached result already exists for the given parameters. If a valid cache file exists, load from disk and skip the API call.

Step 4 — Synthesize and Report Findings

After collecting all API responses, produce a structured professional intelligence report:

For individual profile analysis:

1. Profile Overview — Name, username, URN, headline, location, follower count, connection count, verification status.
2. Career Summary — Work experience timeline, current and past employers, tenure patterns, career progression.
3. Education & Credentials — Academic institutions, degrees, certifications, publications, honors.
4. Skills & Endorsements — Top endorsed skills, skill categories, competency breadth.
5. Content Activity — Post frequency, engagement patterns, video and image publishing cadence.
6. Reputation Signals — Received recommendations, honors, community recognition.
7. Interest Profile — Followed groups and companies, industry focus areas.

For discovery / shortlist building:

1. Search Results — Matched professionals with headline, company, location.
2. Comparative Overview — Side-by-side profile metrics for shortlisted candidates.
3. Actionable Recommendations — Best-fit profiles for outreach, partnership, or hiring.

author: KeyAPI license: MIT repository: https://github.com/EchoSell/keyapi-skills

Common Rules

author: KeyAPI license: MIT repository: https://github.com/EchoSell/keyapi-skills

Error Handling