linkedin-toolkit

Triages LinkedIn inbox and network data into prioritized opportunities and reports.

Updated Apr 26, 2026

Search Tags:#reports #linkedin #toolkit #pipeline-management #inbox-triage #company-research #recruiter-pitches

Ready to run

npx skills add https://github.com/vicnaum/linkedin-toolkit --skill linkedin-toolkit

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: linkedin-toolkit
Source: https://github.com/vicnaum/linkedin-toolkit/tree/main/skills/linkedin-toolkit
Command: npx skills add https://github.com/vicnaum/linkedin-toolkit --skill linkedin-toolkit

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Automates LinkedIn inbox and network triage to turn scattered messages, invitations, and connections into a prioritized pipeline of opportunities, reducing manual overhead and decision fatigue.

Core Features & Use Cases

End-to-end triage of inbox, network, and recruiter pitches, including research, scoring, and reporting.
Phase-based pipeline that delegates sub-agents to classify pitches (phase 2), classify connections (phase 3), verify careers pages (phase 4), and generate reports (phase 5).
Quick lookups and on-demand actions: fetch profiles, read conversations, and star/unstar conversations.
Output artifacts and dashboards: SHORTLIST.md, OPPORTUNITIES_TABLE.md, and salary/companies reports.

Quick Start

Trigger the full workflow by asking to refresh your LinkedIn data and then perform a quick lookup such as 'who's pitching me right now?'.

Frequently Asked Questions about linkedin-toolkit

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I automate LinkedIn inbox triage and prioritize recruiter pitches?▼

LinkedIn inbox triage is automated by classifying messages, invitations, and recruiter pitches into a prioritized pipeline. The system delegates sub-agents to research, score, and surface actionable opportunities, reducing manual overhead and decision fatigue.

What is the best way to extract structured company research from LinkedIn connections?▼

Structured company research is extracted by running a phase-based pipeline that verifies careers pages and classifies connections. Sub-agents classify network data in phase 3, verify company data in phase 4, and generate structured reports like OPPORTUNITIES_TABLE.md in phase 5.

Do I need Python tooling and lnx CLI to manage LinkedIn pipeline management?▼

Python tooling and the lnx CLI are required to manage LinkedIn pipeline management. These dependencies drive deterministic phases, sub-agent orchestration, and optional browser-cache integration to process network data and generate output artifacts.

Can I fetch specific LinkedIn profiles and read conversations on demand?▼

You can fetch LinkedIn profiles and read conversations on demand using quick lookup actions. The toolkit supports targeted actions to fetch profiles, read conversations, and star or unstar conversations outside of the full pipeline workflow.

What report formats are generated from LinkedIn network data?▼

Report formats generated from LinkedIn network data include SHORTLIST.md, OPPORTUNITIES_TABLE.md, and salary or company reports. These dashboards and output artifacts are produced during the final reporting phase of the pipeline.

How does sub-agent orchestration work across the LinkedIn pipeline phases?▼

Sub-agent orchestration works by delegating specific tasks across five pipeline phases: classifying pitches in phase 2, classifying connections in phase 3, verifying careers pages in phase 4, and generating reports in phase 5, driven deterministically by the lnx CLI.