MCPInstall

cli-anything-unimol-tools

by hkudsAI & Agents

Interactive CLI for Uni-Mol molecular property prediction training and inference workflows.

Installs
460
Last 24h
—
Trending rank
—
Security
Not audited

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/hkuds/cli-anything --skill cli-anything-unimol-tools
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads cli-anything-unimol-tools automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

Uni-Mol Tools - Molecular Property Prediction CLI

Package: cli-anything-unimol-tools Command: python3 -m cli_anything.unimol_tools

Description

Interactive CLI for training and inference of molecular property prediction models using Uni-Mol Tools. Supports 5 task types: binary classification, regression, multiclass, multilabel classification, and multilabel regression.

Key Features

  • Project Management: Organize experiments with named projects
  • 5 Task Types: Classification, regression, multiclass, multilabel variants
  • Model Tracking: Automatic performance history and rankings
  • Smart Storage: Analyze usage and clean up underperformers
  • JSON API: Full automation support with --json flag

Common Commands

Project Management

# Create a new project
project create --name drug_discovery

# List all projects
project list

# Switch to a project
project switch --name drug_discovery

Training

# Train a classification model
train --data-path train.csv --target-col active --task-type classification --epochs 10

# Train a regression model
train --data-path train.csv --target-col affinity --task-type regression --epochs 10

Model Management

# List all trained models
models list

# Show model details and performance
models show --model-id <id>

# Rank models by performance
models rank

Storage & Cleanup

# Analyze storage usage
storage analyze

# Automatic cleanup of poor performers
cleanup auto

# Manual cleanup with criteria
cleanup manual --max-models 10 --min-score 0.7

Prediction

# Make predictions with a trained model
predict --model-id <id> --data-path test.csv

Data Format

CSV files must contain:

  • SMILES column: Molecular structures in SMILES format
  • Target column(s): Values to predict (name specified via --target-col)

Example:

SMILES,target
CCO,1
CCCO,0
CC(C)O,1

Task Types

  1. classification: Binary classification (0/1)
  2. regression: Continuous value prediction
  3. multiclass: Multiple class classification
  4. multilabel_classification: Multiple binary labels
  5. multilabel_regression: Multiple continuous values

JSON Mode

Add --json flag to any command for machine-readable output:

python3 -m cli_anything.unimol_tools --json models list

Output format:

{
  "status": "success",
  "data": [...],
  "message": "..."
}

Interactive Mode

Launch without commands for interactive REPL:

python3 -m cli_anything.unimol_tools

Features:

  • Tab completion
  • Command history
  • Contextual help
  • Project state persistence

Test Data

Example datasets available at: https://github.com/545487677/CLI-Anything-unimol-tools/tree/main/unimol_tools/examples

Includes data for all 5 task types.

Requirements

  • Python 3.8+
  • PyTorch 1.12+
  • Uni-Mol Tools backend
  • 4GB+ RAM (8GB+ recommended for training)

Installation

cd unimol_tools/agent-harness
pip install -e .

Documentation

  • SOP: UNIMOL_TOOLS.md
  • Quick Start: docs/guides/02-QUICK-START.md
  • Full Documentation: docs/README.md

Testing

cd docs/test
bash run_tests.sh --unit -v    # Unit tests (67 tests)
bash run_tests.sh --full -v    # Full test suite

Performance Tips

  • Start with 10 epochs for initial experiments
  • Use smaller batch sizes if memory is limited
  • Monitor storage with storage analyze
  • Use models rank to identify best performers
  • Clean up regularly with cleanup auto

Troubleshooting

  • CUDA errors: Reduce batch size or use CPU mode
  • CSV not recognized: Verify SMILES column exists
  • Low accuracy: Try more epochs or adjust learning rate
  • Storage full: Run cleanup auto to free space

Related

FAQ

What does the cli-anything-unimol-tools skill do?

Interactive CLI for Uni-Mol molecular property prediction training and inference workflows.

How do I install cli-anything-unimol-tools?

Run npx skills add https://github.com/hkuds/cli-anything --skill cli-anything-unimol-tools in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/cli-anything-unimol-tools/ (personal) or .claude/skills/cli-anything-unimol-tools/ (project).

Which AI agents can use cli-anything-unimol-tools?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is cli-anything-unimol-tools safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains cli-anything-unimol-tools?

It is published by hkuds in hkuds/cli-anything. It has 460 recorded installs.

More from hkuds/cli-anything

View all →

cli-hub-meta-skill

hkuds/cli-anything

Discover agent-native CLIs for professional software. Access the live catalog to find tools for creative workflows, productivity, AI, and more.

Media & Creative5.4K installs

cli-anything

hkuds/cli-anything

Use when the user wants Codex to build, refine, test, validate, or list CLI-Anything harnesses for GUI applications or source repositories. Adapts the full CLI-Anything methodology to Codex without changing the generated Python harness format.

Testing & QA2.1K installs

cli-anything-blender

hkuds/cli-anything

Command-line interface for Blender - A stateful command-line interface for 3D scene editing, following the same patterns as the GIMP CLI ...

Media & Creative959 installs

cli-anything-obsidian

hkuds/cli-anything

Command-line interface for Obsidian — Knowledge management and note-taking via Obsidian Local REST API. Designed for AI agents and power users who need to manage notes, search the vault, and execute commands without the GUI.

Productivity & Automation932 installs

cli-anything-notebooklm

hkuds/cli-anything

Experimental NotebookLM harness for listing notebooks, managing sources, asking questions, generating artifacts, and downloading outputs through an installed notebooklm CLI.

815 installs

cli-anything-libreoffice

hkuds/cli-anything

Command-line interface for Libreoffice - A stateful command-line interface for document editing, producing real ODF files (ZIP archives with ...

678 installs

Popular ai & agents skills

View category →

google-agents-cli-publish

google/agents-cli

This skill should be used when the user wants to "publish an agent", "publish my ADK agent", "register an agent with Gemini", "publish to Gemini" (formerly Gemini Enterprise), or needs guidance on the agents-cli publish gemini command. Also use when the user wants to "manage agents in Agent Registry", "list/update/delete registered agents", or "register an MCP server". Covers ADK vs A2A registration modes, programmatic and interactive usage, flag reference, auto-detection from deployment metadata, Agent Registry fleet management, and troubleshooting. Part of the agents-cli skills suite. Do NOT use for deployment (use google-agents-cli-deploy).

AI & Agents513.9K installs

google-agents-cli-eval

google/agents-cli

This skill should be used when the user wants to "run an evaluation", "evaluate my agent", "evaluate my ADK agent", "write an eval dataset", "analyze eval failures", "compare eval results", "optimize agent", or needs guidance on the Gemini platform eval methodology and the Quality Flywheel. Covers eval metrics, dataset schema, LLM-as-judge scoring, and common failure causes. Applies to any agents-cli project, whatever framework the agent is written in. Do NOT use for agent API code patterns (ADK: use google-agents-cli-adk-code), deployment (use google-agents-cli-deploy), or project scaffolding (use google-agents-cli-scaffold).

AI & Agents513.5K installs

skill-creator

anthropics/skills

Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

OfficialAI & Agents416.4K installs

codex-pet

prime-skills/runcomfy-agent-skills

Codex Pet generator on RunComfy. Build a Codex-compatible Codex Pet spritesheet.webp + pet.json from a single reference image, drop it into ${CODEXHOME:-$HOME/.codex}/pets/ / and Codex picks it up as a custom Codex Pet next to the 8 built-ins. This skill produces the exact Codex Pet atlas Codex expects (1536x1872 PNG/WebP, 8 cols x 9 rows, 192x208 cells, 9 animation states — idle, running-right, running-left, waving, jumping, failed, waiting, running, review). Calls OpenAI GPT Image 2 edit ONCE via the local RunComfy CLI as runcomfy run openai/gpt-image-2/edit to produce a canonical Codex Pet pose, then assembles all 9 animation rows programmatically with ImageMagick micro-transforms — no Codex Pro, no $imagegen, no OPENAIAPIKEY required, only RUNCOMFYTOKEN. Triggers on "codex pet", "create codex pet", "make codex pet", "hatch codex pet", "/hatch image", "desktop pet codex", "codex pets", "spritesheet.webp", or any explicit ask to build a custom pet for OpenAI Codex.

AI & Agents402.1K installs

full-output-enforcement

leonxlnx/taste-skill

Overrides default LLM truncation behavior. Enforces complete code generation, bans placeholder patterns, and handles token-limit splits cleanly. Apply to any task requiring exhaustive, unabridged output.

AI & Agents401K installs

executing-plans

obra/superpowers

Use when executing an implementation plan in the current session as the implementer yourself — your human partner chose inline execution, or no subagent tool is available

AI & Agents245.7K installs