# Sdrf:setup

> Use when the user wants to set up SDRF skills dependencies, install parse_sdrf and techsdrf, or configure the environment for the first time.

- **Type:** Skill
- **Install:** `agentstack add skill-bigbio-sdrf-skills-sdrf-setup`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [bigbio](https://agentstack.voostack.com/s/bigbio)
- **Installs:** 0
- **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [bigbio](https://github.com/bigbio)
- **Source:** https://github.com/bigbio/sdrf-skills/tree/main/skills/sdrf-setup
- **Website:** https://sdrf.quantms.org

## Install

```sh
agentstack add skill-bigbio-sdrf-skills-sdrf-setup
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# SDRF Setup Workflow

You are guiding the user through installing SDRF skills dependencies. Follow these steps.

**In Cursor**: The user invokes this by asking "install SDRF dependencies" or similar (no `/sdrf:setup` slash command). Ensure `environment.yml` and `requirements.txt` exist at the workspace root; if not, suggest cloning the full sdrf-skills repo or copying those files.

## Step 1: Detect Available Package Managers

Check which package managers are available (run these in the terminal or ask the user):

```bash
command -v conda && conda --version
command -v mamba && mamba --version
command -v uv && uv --version
command -v pip && pip --version
```

- **Conda or mamba**: Recommended — best for thermorawfileparser (Thermo .raw files) via bioconda
- **Pip**: Works for sdrf-pipelines and techsdrf; thermorawfileparser requires conda
- **uv**: Can install Python tools; same limitation as pip for thermorawfileparser

## Step 2: Provide Installation Commands

Based on what's available, output the exact commands the user should run.

### Option A — Conda (recommended)

```bash
# From the sdrf-skills project directory:
conda env create -f environment.yml
conda activate sdrf-skills
```

If using **mamba** (faster):
```bash
mamba env create -f environment.yml
conda activate sdrf-skills
```

### Option B — Pip (venv)

```bash
# From the sdrf-skills project directory:
python -m venv .venv
source .venv/bin/activate   # On Windows: .venv\Scripts\activate
pip install -r requirements.txt
```

**Note**: With pip, thermorawfileparser is not available (not on PyPI). For Thermo .raw files, use conda.

### Option C — uv

```bash
uv venv .venv
source .venv/bin/activate
uv pip install -r requirements.txt
```

## Step 3: Verify Installation

After the user runs the commands, ask them to verify:

```bash
parse_sdrf --version
techsdrf --version
```

If both succeed, setup is complete.

## Step 4: Optional — Spec Submodule

If the user cloned without submodules or wants the latest spec:

```bash
git submodule update --init --recursive
# To pull latest:
git submodule update --remote --recursive
```

## Step 5: Optional — MCP Servers

For full SDRF annotation (PRIDE, OLS, PubMed), the user needs MCP servers configured. Tell them to check their host's MCP configuration:

- **PRIDE MCP** — project metadata, OLS, EuropePMC
- **PubMed** — literature, PMC full text
- **bioRxiv** — preprint search (optional)
- **Consensus** — evidence search (optional)

For Europe PMC full text, prefer the local normalizer over raw XML inspection:
`python scripts/europepmc_fulltext.py PMC_ID --format text`
This keeps methods/results/discussion easier for LLMs to interpret and preserves
canonical links plus detected accessions in JSON mode.

## Summary Output

Provide a clear summary:

1. **Package manager detected**: conda / pip / uv
2. **Commands to run**: (copy-paste block)
3. **Verify**: parse_sdrf --version, techsdrf --version
4. **Next**: Run /sdrf:annotate PXD###### or /sdrf:validate yourfile.sdrf.tsv

## If User Passes "check"

When the user invokes `/sdrf:setup check`, run the verification step and report status:
- parse_sdrf: ✓ or ✗
- techsdrf: ✓ or ✗
- spec/ submodule: present and init'd or not
- Suggest fixes for any missing items

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [bigbio](https://github.com/bigbio)
- **Source:** [bigbio/sdrf-skills](https://github.com/bigbio/sdrf-skills)
- **License:** MIT
- **Homepage:** https://sdrf.quantms.org

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-bigbio-sdrf-skills-sdrf-setup
- Seller: https://agentstack.voostack.com/s/bigbio
- Browse the marketplace: https://agentstack.voostack.com/browse

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Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.