Guide new users through an interactive RPI cycle in AgentOps.
npx skills add https://github.com/boshu2/agentops --skill quickstart
Please help me install this Agent Skill. Skill: quickstart Source: https://github.com/boshu2/agentops/tree/main/skills/quickstart Command: npx skills add https://github.com/boshu2/agentops --skill quickstart
Onboarding new users through AgentOps is streamlined with an interactive RPI cycle that demonstrates core capabilities while delivering immediate value.
Run the quickstart workflow to initiate an end-to-end onboarding session. Trigger: "/quickstart" or "get started". The system will detect the project, orient the user, perform a mini research pass, propose a concrete improvement, run a rapid vibe check, reveal available tools, and suggest your next steps.
High-intent search queries and answers about installing and using this skill.
To onboard a new codebase in AgentOps, trigger the /quickstart workflow to detect your project, orient you to the repository, and run a mini Research, Plan, Implement, Validate cycle for rapid value delivery.
Yes, the AgentOps onboarding workflow supports project detection and orientation for Python, Go, TypeScript, and shell repositories, guiding each through a hands-on research, planning, implementation, and validation cycle.
The RPI cycle in AgentOps is an interactive Research, Plan, Implement workflow that guides new users through codebase detection, proposing a concrete improvement, and running a rapid vibe check to deliver immediate value.
No external dependencies are required to run the quickstart onboarding cycle. It operates entirely inside AgentOps, loading operational context and optionally extending via scripts or references without mandatory external installs.
AgentOps validates proposed improvements during onboarding by running a rapid vibe check after the mini research, planning, and implementation phases, revealing available tools and suggesting your next steps.
The best way to plan your first codebase improvement is to trigger /quickstart, which performs a mini research pass on your repository, proposes a concrete improvement, and validates it through a rapid vibe check.
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