rlm

rlm is a Context window & token budget skill for AI agents, published by mifunedev in the openharness repository on GitHub. Skillspace rates rlm D (62/100) and it passed the safety scan; the rating is based on static analysis only.

Context-as-environment decomposition (Layer B of the RLM integration). Treat a large artifact (file / dir / log) as a REPL/filesystem the root agent greps and slices instead of ingesting — partition it into addressable chunks via query-context.mjs --map, RECURSE sub-agent calls over the relevant chunks under a bounded depth/children/step budget, AGGREGATE (piping competing per-chunk answers through /weigh), then PERSIST the recursion trace. The anti-context-rot move: ADDRESS context, don't dump the whole artifact into the window. Reuses .oh/scripts/ralph.sh (the recursion loop) and .oh/worktrees/ (isolated branches) BY REFERENCE — never edits either. Manual-invoke (spawns agents, burns tokens). TRIGGER when: /rlm invoked, or asked to "answer a question over a huge file/log", "decompose a large artifact", "recurse over chunks", "address context instead of ingesting it", "beat context rot on a long input", "RLM <file> <query>".

Grade
D
Quality
62 / 100
Safety
Passed
GitHub stars
24

Unproven: rated by static analysis only, not yet compared head-to-head.

This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.

Our review

Architecture
84 / 100
Maturity
Structured: ships references or scripts
Security scan
Low-risk patterns
License
MIT
Adoption
Adopted by 0 other repositories, in 0 collections

What the scanner flagged

  • mentions secrets in text

Not checked yet

  • Category is a classifier prediction; no model has read this skill yet
  • Not judged on the rubric yet (not a contender in its category)
  • No blind task eval yet
  • Scope (portable / vendor / repo-internal) not assessed; only the top of each category is read for scope
  • Sibling files (scripts, references) not fetched; security scan covers SKILL.md only

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