rlm-workflow

you can counter the context rot and requirement drift that is experienced here by many users by using a recursive, self-documenting workflow: https://github.com/doubleuuser/rlm-workflow

I use a self-documenting recursive workflow: https://github.com/doubleuuser/rlm-workflow

most codebases dont have traces to train on. if you use rlm-workflow you will build up rich traceability in the form of requirements, plans, implementation artifacts, along with worktree diffs. with these, you can then use self-distillation on models or use autoagent to improve your harness. https://github.com/doubleuuser/rlm-workflow

You need to lock the specs and implementation plan and verify the implementation about the previous phase docs. https://github.com/doubleuuser/rlm-workflow

>I do think there are opportunities in this space, but what I'd like to see is: >* write text specifications >* model transforms text into a formal specification >* then the formal spec is translated into code which can be verified against the spec This skill does just that: https://github.com/doubleuuser/rlm-workflow Each stage produces its own output artifact (analysis, implementation plan, implementation summary, etc) and takes the previous phases' outputs as input. The artifact is locked after the stage is done, so there is no drift.

Recursive language models: https://github.com/doubleuuser/rlm-workflow

I built an RLM workflow as a coding agent skill: https://github.com/doubleuuser/rlm-workflow It has two differences: 1. It does not store chat history, reasoning traces etc, only workflow artifacts (requirements, codebase analysis, implementation plan, etc). I frankly do not believe those things are relevant. 2. It is significantly simpler and more lightweight, using only markdown files

that's similar to the workflow i built, inspired by Recursive Language Models: https://github.com/doubleuuser/rlm-workflow

I built a skill for this: https://github.com/doubleuuser/rlm-workflow The readme is a bit more to the point.