The Opportunity Discovery and Pursuit Engine is a WORK IN PROGRESS ... to see where we're at, look at Development Journal ... OR you might be interested in my microblogging on X
The core purpose of an Opportunity Discovery and Pursuit Engine is the RELATIONSHIPS themselves ... we want to discover high-value opportunities efficiently, pursue them with focus, bring them to successful closure, and sustain them over time. Technology is merely the means to that end. AI, multi-agent systems, and agentic workflows are not the point — they exist only to get the job done and then step aside.
OpptyOps is conceived as a pragmatic, no-frills multi-toolkit. It will not prioritize polish, at least not initially and possibly not ever. Instead, it is an open-source evolution and deliberate study of both the development process behind Santifer’s Career-Ops system and the original repository (https://github.com/santifer/career-ops), along with its forks and related projects.
The goal is to build a lightweight, extensible open source toolkit for a pragamatic Partner Relationship Management (PRM) / CRM platform that be modified/extended to fully automate different opportunity lifecycles. Opportunities extend well beyond traditional job search to include startup founding, venture philanthropy, special-projects staffing, volunteer coordination, and any creative, scientific, or artistic collaboration that requires meaningful commitment of talent and resources.
The system is designed to run continuously on low-resource local hardware such as a Raspberry Pi or Mac Mini M4. It employs lightweight multi-agent orchestration, file- and Markdown-first memory, and small specialized large language models. Core capabilities include autonomous prospecting and data ingestion, multi-dimensional opportunity qualification, persistent CRM-style tracking, and on-demand generation of the documents essential to discussion and accelerated negotiation — resumes, cover letters, statements of work, proposals, and pitch decks.
As a development monorepo, OpptyOps must deliberate work hard at avoiding reinventing existing solutions. For any given task it surfaces 3–4 proven, practical approaches (CLI tools, Python, Rust, Go, Chroma, dashboards, etc.) so developers and users can select the simplest and most effective path for their needs.