The rlm-workflow has been introduced as an agent skill for managing hybrid local/API model inference. It employs a structured kanban workflow to enhance context length and maintain traceability, optimizing how code and requirements are processed.
Rlm-workflow is a new agent skill designed to improve context management and model inference performance. This tool routes requests to suitable models based on their capabilities, creating efficiencies in cost, speed, and accuracy.
The workflow utilizes a sequential kanban model that guides users from requirements gathering to manual QA. Each phase is documented in markdown, ensuring systematic progress and accountability.
The important distinction in rlm-workflow is to minimize the transfer of information within chat, treating it as a command line interface (CLI) for invocations instead.
This release follows insights from a recent MIT paper highlighting the use of sub-agents to extend effective context length for language models up to 10 million tokens. Previous attempts at implementing such strategies have varied, with some advocating for entire session contexts to be stored in databases for retrieval.
Rlm-workflow stands out by focusing on structured documentation and gated processes, requiring completed phases before progression.
By integrating rlm-workflow, teams can expect a more reliable model inference process with enhanced context management. The structured documentation and phase-gating can help ensure higher quality outputs and better team alignment during development.
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The rlm-workflow has been introduced as an agent skill for managing hybrid local/API model inference. It employs a structured kanban workflow to enhance context length and maintain traceability, optimizing how code and requirements are processed.