Agent skill · Data & Analytics

rlm

Use RLM (Recursive Language Models) for verified code execution, calculations, data analysis, and task decomposition. Executes Python code iteratively until producing verified results - no LLM guessing.

majiayu000github.com/majiayu000GitHub ↗
claude-codeMIT
Install
npx skills add majiayu000/claude-skill-registry --skill rlm-moltbot-skills --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 2
SKILL.md size: 7 KB
Bundled scripts: none
Path: skills/ai-llm/rlm-moltbot-skills/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 534
Language: HTML

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# RLM - Recursive Language Models Execute tasks with **verified code execution** via mcporter MCP bridge. RLM writes and executes Python code iteratively until it produces a verified answer. Unlike direct LLM responses, RLM computations are **100% accurate** for calculations. ## Prerequisites ### 1. Install mcporter (MCP bridge) ```bash npm install -g mcporter ``` ### 2. Install RLM MCP Server **Option A: Clone and setup (recommended)** ```bash # Clone RLM project git clone https://github.com/alexzhang13/rlm.git $HOME/rlm cd $HOME/rlm pip install -e . # Create MCP server directory mkdir -p $HOME/.claude/mcp-servers/rlm/src # Download MCP server files curl -o $HOME/.claude/mcp-servers/rlm/src/server.py \ https://raw.githubusercontent.com/eesb99/rlm-mcp/main/src/server.py curl -o $HOME/.claude/mcp-servers/rlm/run_server.sh \ https://raw.githubusercontent.com/eesb99/rlm-mcp/main/run_server.sh curl -o $HOME/.claude/mcp-servers/rlm/setup.sh \ https://raw.githubusercontent.com/eesb99/rlm-mcp/main/setup.sh curl -o $HOME/.claude/mcp-servers/rlm/requirements.txt \ https://raw.githubusercontent.com/eesb99/rlm-mcp/main/requirements.txt # Setup venv and install dependencies chmod +x $HOME/.cla

What's inside
Steps it walks through
  1. Prerequisites
  2. 1. Install mcporter (MCP bridge)
  3. 2. Install RLM MCP Server
  4. 3. Configure MCP (for Claude Code)
  5. 4. Set API Key
  6. 5. Verify Installation
  7. Available Tools
  8. Quick Commands
  9. When to Use RLM
  10. How It Works
  11. Configuration
  12. Troubleshooting
  13. References
Ships with 1 file
  • metadata.json
Commands it runs
npm install -g mcporter
Clone RLM project
git clone https://github.com/alexzhang13/rlm.git $HOME/rlm
cd $HOME/rlm
pip install -e .
Create MCP server directory
mkdir -p $HOME/.claude/mcp-servers/rlm/src
Download MCP server files
curl -o $HOME/.claude/mcp-servers/rlm/src/server.py \
curl -o $HOME/.claude/mcp-servers/rlm/run_server.sh \
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About this skill
What does the rlm skill do?

Use RLM (Recursive Language Models) for verified code execution, calculations, data analysis, and task decomposition. Executes Python code iteratively until producing verified results - no LLM guessing.

How do I install it?

Run `npx skills add majiayu000/claude-skill-registry --skill rlm-moltbot-skills --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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