Rlm-Workflow

Rlm-Workflow, a new agent skill for coding assistants like Codex, implements a structured kanban-style workflow to improve code quality, traceability, and context management by keeping specifications out of chat and using locked markdown documents for each phase. The workflow, inspired by the MIT Recursive Language Models paper, reduces context rot and token usage while enhancing accuracy and speed.

role-model Model routing protocol Router runtime Pi package Protocol and runtime for hybrid local/API model inference. Models are assigned roles based on capabilities, performance, speed and cost; task requests are routed to the best suited models to optimiza the triangle of constraints, cost-speed-accuracy. Github: https://github.com/try-works/role-model https://github.com/try-works/role-model Docs: https://role-model.dev/ https://role-model.dev/ rlm-workflow Agent Skill for Codex, etc Improved context length, code quality, and traceability Installation npx skills add https://github.com/doubleuuser/rlm-workflow https://github.com/doubleuuser/rlm-workflow --skill rlm-workflow skills.sh http://skills.sh https://skills.sh/doubleuuser/rlm-workflow/rlm-workflow https://skills.sh/doubleuuser/rlm-workflow/rlm-workflow GitHub https://github.com/doubleuuser/rlm-workflow https://github.com/doubleuuser/rlm-workflow After the MIT paper Recursive Language Models paper demonstrated a method of increasing effective context length to 10M tokens by using sub-agents to move information from the context window to an information store outside the chat, there's been a number of different takes on how to put this into practice in development workflows. Some even go in the direction of storing entire session contexts in a database for later retrieval to preserve reasoning for changes that were made. rlm-workflow is yet another take, this time with a slightly different angle: Important information like requirements, codebase analysis and implementation plans should not be passed in the chat in the first place. Chat is effectively CLI and should be used for invocations and commands, not for passing information. rlm-workflow is modelled after a regular kanban workflow from requirement to implementation plan to testing and manual QA. The workflow is sequential and phase; each phase outputs one markdown doc and takes the previous phases' docs as input. Each phase is gated on fulfilling criteria defined in the previous phase, and at the end of a phase, its output docs are locked. The user first creates the 00-requirements.md http://00-requirements.md doc in an RLM folder, then invokes the workflow in chat. It then runs until the manual QA stage, where it waits for user approval before continuing. After finishing an RLM run, the agent updates DECISIONS.md http://DECISIONS.md which is a ledger of requirements implemented previously, their whys and whats, and links to respective RLM docs. It also updates STATE.md, an overview of the app's current state. To be practical, this is what your repo will look like: rlm/00-my-first-requirements/ - 00-requirements.md user-created http://00-requirements.md - 01-as-is.md http://01-as-is.md - 02-to-be.md http://02-to-be.md - 03-implementation-summary.md http://03-implementation-summary.md - 04-manual-qa.md test cases are pre-defined; the user enters pass/fail and notes in the doc http://04-manual-qa.md + /addenda/ if needed To summarize: 1. Specs are never passed through the chat so they do not suffer from context rot 2. Work is always done based on docs that are locked, so it cannot suffer from degradation 3. The workflow is self-documenting; it is also easily human readable; can also be used to generate information for non-technical stakeholders 4. There is no need to index the codebase a database. The rlm docs provide progressive disclosure and point the model in the right direction. Should significantly reduce token usage. 4. In my simple test, the workflow improves both quality and time to success for complex requirements The benefits of using rlm-workflow for assisted engineering includes improved traceability through workflow and global docs, reduced token usage, reduced context rot, improved accuracy and code quality, and improved speed, when considering reduced need for re-work. What about the chat sessions? Forget about them. 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