npx agentspec-cli install 5ddeac79-5b04-4906-b5ef-3cb814d04cee --framework ClaudeCode→ .claude/skills/nmrxiv/SKILL.md
# nmrxiv-downloader: NMR Dataset Search & Download
CLI tool for searching and downloading NMR spectroscopy datasets from nmrxiv.org.
## Setup
```bash
# Install from PyPI
pip install nmrxiv-downloader
# Verify installation
nmrxiv --help
```
## Quick Reference
| Task | Command | JSON Path |
|------|---------|-----------|
| List projects | `nmrxiv list --type project` | `.items[].identifier` |
| List datasets | `nmrxiv list --type dataset` | `.items[].identifier` |
| Search molecule | `nmrxiv search --query "name"` | `.results[].canonical_smiles` |
| Search SMILES | `nmrxiv search --smiles "CCO"` | `.results[].iupac_name` |
| Filter by experiment | `nmrxiv search --type hsqc` | `.results[].identifier` |
| Show details | `nmrxiv show P5` | `.item.download_url` |
| Download | `nmrxiv download P5 -o /tmp` | `.file`, `.size` |
| Download + extract | `nmrxiv download P5 -o /tmp --extract` | `.extracted_to` |
## Common Experiment Types
```bash
nmrxiv search --type hsqc # 1H-13C HSQC
nmrxiv search --type hmbc # HMBC
nmrxiv search --type cosy # COSY
nmrxiv search --type dept # DEPT
nmrxiv search --type "1d-13c" # 1D 13C
nmrxiv search --type noesy # NOESY
nmrxiv search --type tocsy # TOCSY
```
## Key Facts
- **All output is JSON by default** (add `--no-json` for human-readable)
- **Projects have download URLs, datasets don't** - download via parent project
- **Data hierarchy**: Project → Study → Dataset → Files (Bruker, JCAMP-DX)
- **Pagination**: Use `--page N` for large result sets
## Workflow: Find and Download NMR Data
```bash
# 1. Search for experiment type
nmrxiv search --type hsqc
# 2. Get dataset details (shows parent project)
nmrxiv show D410
# 3. Download parent project
nmrxiv download P11 --output ./data --extract
# 4. Data is now in ./data/P11/
```
## Workflow: Search by Compound
```bash
# 1. Search by name
nmrxiv search --query "quercetin"
# 2. Or by SMILES substructure
nmrxiv search --smiles "c1ccc(O)cc1"
# 3. Find related projects
nmrxiv list --type project | jq '.items[] | select(.name | test("quercetin"; "i"))'
```
## Error Handling
If download fails with "No download URL":
```json
{"error": true, "message": "No download URL for D410. Try downloading parent project: P11", "code": 1}
```
→ Use the suggested parent project ID to download.
## Parsing Examples
```bash
# Get first project identifier
nmrxiv list --type project | jq -r '.items[0].identifier'
# Get all HSQC dataset IDs
nmrxiv search --type hsqc | jq -r '.results[].identifier'
# Get download URL for a project
nmrxiv show P5 | jq -r '.item.download_url'
```
Built on the Agent Skills standard
Research MCP Config
5 MCP servers — bgpt: Search scientific papers with full-text experimental data via BGPT. 50 free searches, no API key needed.; fetch: Fetch web pages, documentation, and research resources as markdown.; memory: Persistent knowledge graph for tracking research findings, citations, and insights across sessions.; filesystem: Read and write research notes, papers, and data files. Replace the placeholder with your project root.; brave-search: Web search for supplementary sources, preprints, and grey
by AgentHub Crawler
JAX-ENT: JAX-based Ensemble Modeling and Optimization Toolkit
By default the Python environment is found in `.venv`. JAX-ENT is a JAX-based library for ensemble modeling and optimization in structural biology/biophysics, with a strong focus on HDX-MS workflows. This document is a codebase-oriented guide for a mixed audience: contributors who need architectural detail and users who need practical workflow grounding.
by AgentHub Crawler
HyperNOs: Automated and Parallel Neural Operators Research
HyperNOs is a Python framework for automated, distributed, and parallel hyperparameter optimization (HPO) of Neural Operators. It leverages **PyTorch** for model development and **Ray Tune** for scalable hyperparameter search across various physics-informed benchmarks.
Sign in to leave a comment.
by AgentHub Crawler