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Use when the user wants to set up SDRF skills dependencies, install parse_sdrf and techsdrf, or configure the environment for the first time.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
| name | sdrf:setup |
| description | Use when the user wants to set up SDRF skills dependencies, install parse_sdrf and techsdrf, or configure the environment for the first time. |
| user-invocable | true |
| argument-hint | [optional: conda | pip | check] |
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.
Check which package managers are available (run these in the terminal or ask the user):
command -v conda && conda --version
command -v mamba && mamba --version
command -v uv && uv --version
command -v pip && pip --version
Based on what's available, output the exact commands the user should run.
# From the sdrf-skills project directory:
conda env create -f environment.yml
conda activate sdrf-skills
If using mamba (faster):
mamba env create -f environment.yml
conda activate sdrf-skills
# 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.
uv venv .venv
source .venv/bin/activate
uv pip install -r requirements.txt
After the user runs the commands, ask them to verify:
parse_sdrf --version
techsdrf --version
If both succeed, setup is complete.
If the user cloned without submodules or wants the latest spec:
git submodule update --init --recursive
# To pull latest:
git submodule update --remote --recursive
For full SDRF annotation (PRIDE, OLS, PubMed), the user needs MCP servers configured. Tell them to check their host's MCP configuration:
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.
Provide a clear summary:
When the user invokes /sdrf:setup check, run the verification step and report status:
Independently falsify and review a created, modified, or proposed SDRF against its specification, ontology terms, source evidence, repository files, and experimental design. Use after SDRF annotation or repair, before contribution or completion, when a review gate reports a pending artifact, or whenever an isolated reviewer must distrust the producer's self-assessment and issue a hash-bound verdict.
Create or improve an SDRF through a producer-reviewer workflow with deterministic validation, an evidence manifest, fresh-context adversarial review, repair, and mandatory re-review. Use when an SDRF must be independently verified before completion or contribution, when the user requests adversarial review, or when automated review hooks require a passing hash-bound receipt.
Use when the user wants SDRF annotation to run as an autonomous retained-improvement loop over one dataset, a manifest, or a dataset class such as all PRIDE cell line or crosslinking datasets.
Use when the user has a completed SDRF annotation for a ProteomeXchange dataset and wants to contribute it back to the community via a PR to sdrf-annotated-datasets.
Use when the user wants a comprehensive quality review of an SDRF file, a PR review of an SDRF submission, or a quality score assessment.
Use before sdrf:autoresearch when the user needs to screen or shortlist proteomics studies from PRIDE, MassIVE, ProteomeXchange accessions, or a manifest using detailed user-defined inclusion/exclusion criteria; extract study-level metadata from repository records and publications; and write an evidence-backed TSV for downstream annotation, review, or meta-analysis.