Skillget

ml-ralph

agent
Guvenlik Denetimi
Uyari
Health Uyari
  • No license — Repository has no license file
  • Description — Repository has a description
  • Active repo — Last push 0 days ago
  • Community trust — 32 GitHub stars
Code Gecti
  • Code scan — Scanned 12 files during light audit, no dangerous patterns found
Permissions Gecti
  • Permissions — No dangerous permissions requested
Purpose
This is an autonomous AI agent with a terminal user interface that automates machine learning experiments. It handles the entire experimentation loop—from planning and data analysis to execution and reflection—using Claude to drive the process.

Security Assessment
Overall Risk: Medium. As an autonomous coding agent, the tool inherently executes shell commands and interacts with your local file system to run ML experiments and track metrics. It requires the Claude Code CLI to be installed and authenticated, meaning it relies on external network requests to Anthropic's API to function. The automated code scan (12 files) found no dangerous patterns, hardcoded secrets, or explicitly dangerous permission requests. However, because it autonomously runs commands via tmux, users should monitor its actions, especially in directories containing sensitive data.

Quality Assessment
The project is actively maintained, with its most recent push occurring today. It has garnered 32 GitHub stars, indicating a fair level of early community trust and visibility. The light audit flagged a minor discrepancy: the repository lacks a formal license file in its root directory, even though the README and NPM badge explicitly declare it as MIT licensed. This missing file is a minor administrative oversight rather than a blocker, but corporate users should be aware of it.

Verdict
Use with caution—a well-maintained and safe tool, but its autonomous nature requires standard oversight when letting an AI execute local commands.
SUMMARY

Autonomous ML agent for running experiments using Claude.

README.md

ml-ralph

npm version
License: MIT

An autonomous ML engineering agent with a terminal user interface. ml-ralph automates the experiment loop — planning, execution, analysis, and learning extraction — so you can iterate on ML projects faster.

You define your goals through a PRD. The agent works through stories autonomously, runs experiments, tracks metrics, and accumulates structured learnings across iterations.

Getting started

bunx @pentoai/ml-ralph

That's it. Run it inside any ML project directory and the TUI will launch in tmux.

Requirements

The cognitive framework

ml-ralph operates as a paranoid scientist. Its core assumption: results are probably misleading, data is probably corrupted, and conclusions should be broken before they're trusted. It allocates roughly 70% of effort to understanding and verification, 20% to strategy, and 10% to execution.

The agent works through a 4-phase cognitive cycle:

UNDERSTAND → STRATEGIZE → EXECUTE → REFLECT
     ↑                                  │
     └──────────────────────────────────┘

Understand — Verify data integrity (row counts, label distributions, sample inspection). Run exploratory analysis. Research prior art. Build a mental model and explicitly list all assumptions. Nothing happens until this is done.

Strategize — Generate 3–5 competing hypotheses. For each: what's expected, why, and what will be learned. Think 5–6 steps ahead. Pick the path with the best learning-to-effort ratio. Run the smallest experiment that tests the hypothesis.

Execute — Run the experiment. Log metrics and observations as work happens, not after. Surprises are more valuable than confirmations.

Reflect — Verify results are real, not artifacts of bugs, leakage, or evaluation errors. Try to break your own result before trusting it. Then decide:

  • Too good? → Verify harder
  • Verified and promising? → Strategize next step
  • Surprised or confused? → Go back to Understand
  • Stuck after 2–3 experiments? → Strategic retreat to Understand
  • All success criteria met and verified? → Complete

Strategic retreat — going back to understand when stuck — is a first-class concept, not a failure. Understanding is progress.

License

MIT

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