mcpField
R
rlm

rlm

github.com/guia-matthieuAgent workflows150198 installsUpdated 4w ago
Repository

About rlm

rlm is a skill for coordinating codebase analysis through sub-agents rather than loading many files into one context. It suits large cross-file searches and audits, using filtering, parallel summaries, and synthesis.

Key features

  1. 01Find candidate files without loading their contents
  2. 02Delegate focused file analysis to parallel agents
  3. 03Combine agent summaries into structured findings
  4. 04Repeat analysis to investigate gaps
  5. 05Use scripts to track scans across many files

Use cases

  1. 01Find and review API endpoints across a codebase
  2. 02Audit patterns across many source files
  3. 03Analyze large codebases while limiting context use

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/guia-matthieu/clawfu-skills --skill rlm

After installing, type/rlmin Claude Code to confirm it loaded.

Details

From the project README

Recursive Language Model (RLM)

"Context is an external resource, not a local variable."

You are the Root Node. Your job is NOT to read code directly, but to orchestrate sub-agents that read code for you.

The RLM Loop

Phase 1: Index & Filter

Identify relevant files without loading them into context.

# Find candidate files
grep -rl "pattern" src/ --include="*.ts"
find . -name "*.py" -newer last_check
Phase 2: Parallel Map

Split work into atomic units, spawn parallel agents.

  • Launch 3-5+ agents in parallel for broad tasks
  • Give each agent ONE specific file or chunk
  • Each agent returns a structured summary

Example spawn:

Agent 1: "Read src/api/routes.ts. List all endpoints with their auth decorators."
Agent 2: "Read src/api/users.ts. List all endpoints with their auth decorators."
...
Phase 3: Reduce & Synthesize

Collect all agent outputs, find patterns, compile into a coherent answer.

If incomplete, recurse: run a second RLM pass on the specific gaps.

Critical Rules

  1. NEVER read more than 3-5 files into your main context
  2. ALWAYS use parallel agents when file count > 5
  3. Write Python scripts for state tracking across 50+ files — let the script scan and summarize
  4. If parallel agents are unavailable, fall back to iterative Python scripting

Example: "Find all API endpoints, check for Auth"

Wrong (monolithic): Read each file sequentially → context fills up, reasoning degrades.

RLM Way:

  1. grep -l "@Controller" src/**/*.ts → 20 files
  2. Spawn 20 agents, each extracts endpoints + auth status
  3. Collect outputs, compile table, identify missing auth

Output Format

Return a structured summary:

  • Findings table (file, pattern, status)
  • Gaps identified (what needs deeper investigation)
  • Confidence level (how complete the scan was)

Skill Boundaries

Excels for: Codebases >100 files, cross-file pattern search, audit tasks, large file analysis.

Not ideal for: Small projects (<50 files), single file analysis, file modification tasks.

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