cli-anything-unimol-tools
Interactive CLI for Uni-Mol molecular property prediction training and inference workflows.
What this skill does
# 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
```bash
# Create a new project
project create --name drug_discovery
# List all projects
project list
# Switch to a project
project switch --name drug_discovery
```
### Training
```bash
# 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
```bash
# List all trained models
models list
# Show model details and performance
models show --model-id <id>
# Rank models by performance
models rank
```
### Storage & Cleanup
```bash
# 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
```bash
# 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:
```csv
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:
```bash
python3 -m cli_anything.unimol_tools --json models list
```
Output format:
```json
{
"status": "success",
"data": [...],
"message": "..."
}
```
## Interactive Mode
Launch without commands for interactive REPL:
```bash
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
```bash
cd unimol_tools/agent-harness
pip install -e .
```
## Documentation
- **SOP**: [UNIMOL_TOOLS.md](../UNIMOL_TOOLS.md)
- **Quick Start**: [docs/guides/02-QUICK-START.md](../docs/guides/02-QUICK-START.md)
- **Full Documentation**: [docs/README.md](../docs/README.md)
## Testing
```bash
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
- **Uni-Mol Tools**: https://github.com/dptech-corp/Uni-Mol/tree/main/unimol_tools
- **Uni-Mol Paper**: https://arxiv.org/abs/2209.11126
- **CLI-Anything**: https://github.com/HKUDS/CLI-Anything
Related in General
modeling-omnistudio-epc-catalog
IncludedSalesforce Industries CME EPC product-modeling skill for Product2-based catalog creation. Use when creating EPC products, configuring product attributes, building offer bundles with Product Child Items, or reviewing EPC DataPack JSON metadata for product catalog changes. TRIGGER when: user creates or updates Product2 EPC records, AttributeAssignment payloads, AttributeMetadata/AttributeDefaultValues, Offer bundles, or ProductChildItem relationships. DO NOT TRIGGER when: designing OmniScripts/FlexCards/Integration Procedures (use building-omnistudio-omniscript, building-omnistudio-flexcard, or building-omnistudio-integration-procedure), implementing Apex business logic (use generating-apex), or troubleshooting deployment pipelines (use deploying-metadata).
relationship-science-coach
IncludedUse this skill for direct, practical adult relationship coaching: couples conflict, repair, trust, marriage, dating, flirting, attachment patterns, emotional connection, sex, desire differences, eroticism, kink negotiation, affection, love languages, breakups, and long-term passion. Draw on Gottman, EFT and Hold Me Tight, attachment science, modern sex research, Perel, Nagoski, Kerner, Schnarch, Love and Stosny, and flexible love-language tools. Be concrete and low-hedge. Redirect only for imminent danger, abuse, coercive control, minors, non-consent, self-harm, stalking, or medical/legal/psychiatric decisions.
building-sf-integrations
IncludedSalesforce integration architecture and runtime plumbing with 120-point scoring. Use this skill to set up Named Credentials, External Credentials, External Services, REST/SOAP callout patterns, Platform Events, and Change Data Capture. TRIGGER when: user sets up Named Credentials, External Services, REST/SOAP callouts, Platform Events, CDC, or touches .namedCredential-meta.xml files. DO NOT TRIGGER when: Connected App/OAuth config (use configuring-connected-apps), Apex-only logic (use generating-apex), or data import/export (use handling-sf-data).
venue-templates
IncludedAccess comprehensive LaTeX templates, formatting requirements, and submission guidelines for major scientific publication venues (Nature, Science, PLOS, IEEE, ACM), academic conferences (NeurIPS, ICML, CVPR, CHI), research posters, and grant proposals (NSF, NIH, DOE, DARPA). This skill should be used when preparing manuscripts for journal submission, conference papers, research posters, or grant proposals and need venue-specific formatting requirements and templates.
let-fate-decide
IncludedDraws the 12 Houses of the Zodiac Tarot spread to inject entropy into planning when prompts are vague, ambiguous, or casually delegated. Interprets the spread to guide next steps. Use when the user says 'let fate decide', 'YOLO', 'whatever', 'idk', or other nonchalant phrases, makes Yu-Gi-Oh references, or when you are about to arbitrarily pick between multiple reasonable approaches. Prefer over ask-questions-if-underspecified when the user's tone is casual or playful rather than precision-seeking.
net-ops
IncludedCross-platform network troubleshooting (Windows, macOS, Linux) via local or remote shell. Use for: DNS broken, can't resolve hostnames, nslookup/dig works but apps fail, NRPT, WFP, scutil, /etc/resolver, systemd-resolved, /etc/resolv.conf, NetworkManager, VPN DNS leak residue (ProtonVPN/Mullvad/WireGuard/AnyConnect), AV/firewall blocking DNS or DoH, Tailscale DNS interaction, intermittent connectivity, remote diagnostics over SSH.