linkedin-toolkit

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.

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.

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.

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.

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.

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.