sdrf-skills

Turn Claude Code, Cursor, OpenAI Codex, Gemini CLI, or OpenCode into an expert proteomics SDRF annotator.

Pick a dataset → the agent fetches PRIDE + paper → you review a validated SDRF.

Structured skills that give AI assistants expert-level capabilities for annotating, validating, improving, and reviewing proteomics metadata in the SDRF format. Instead of guessing at ontology terms or SDRF rules, the agent follows the methodology of experienced annotators using real tools (OLS, PRIDE, PubMed). The specification data (column definitions, templates) lives in a git submodule and is read at runtime, so the skills stay current as the spec evolves.

Available skills

Four skills. Three are slash commands under /sdrf-skills:; sdrf-adversarial-review is dispatched by sdrf-annotate into a fresh context and is never typed as a command (a pipeline can also run it directly as a separate task):

Skill	What it does
/sdrf-skills:sdrf-annotate	Everything about one SDRF. A PXD: record + paper → samples.tsv + technical.tsv → sdrf-tools build → validate → independent review, always. An existing .sdrf.tsv: review it (validate, structure, check, reconcile, cross-reference, score, then the independent review). A question: explain the format, look up a term, or plan what to capture. A short core plus references/ it reads only when a step needs them
/sdrf-skills:sdrf-contribute	Contribute a reviewed SDRF back to sdrf-annotated-datasets via PR, behind the review gate
/sdrf-skills:sdrf-campaign	Screen a class of studies against your criteria into a resumable TSV, then loop sdrf-annotate over the included ones, keeping only evidence-backed improvements
sdrf-adversarial-review (dispatched, not typed)	Independent review of an SDRF in a fresh context, checked against the evidence, with a verdict bound to the file's hash

Installation

Then pip install -e . from the checkout gives you the sdrf-tools command (pip install -e '.[mcp]' adds the bundled MCP server's dependencies), and sdrf-tools doctor checks parse_sdrf, sdrf-tools, the spec submodule and techsdrf, and says what to install for anything missing.

Claude Code, in two lines — no clone needed; the marketplace install fetches the spec/ submodules with it:

/plugin marketplace add bigbio/sdrf-skills
/plugin install sdrf-skills@sdrf-skills

Then run sdrf-tools doctor; if it reports something missing, skills/sdrf-annotate/references/setup.md has the install steps for parse_sdrf and techsdrf. The skills resolve spec/, tools/ and data/ against $CLAUDE_PLUGIN_ROOT, so you can work from any directory.

From a checkout (for development, or for the other platforms below):

# 1. Clone WITH submodules (the spec data is a submodule):
git clone --recurse-submodules https://github.com/bigbio/sdrf-skills
# already cloned without them?  git submodule update --init --recursive

# 2. Install the deterministic helper tools (conda recommended — includes thermorawfileparser):
conda env create -f environment.yml && conda activate sdrf-skills
# pip alternative (thermorawfileparser not on PyPI):
#   pip install -r requirements.txt && pip install git+https://github.com/bigbio/techsdrf.git

# 3. Point the skills at this checkout (they resolve spec/, tools/ and data/ against it).
#    Run from the checkout; this writes its absolute path to your shell profile (in an image, use ENV):
echo "export SDRF_SKILLS_ROOT=\"$PWD\"" >> ~/.bashrc

Update the bundled spec any time with git submodule update --remote --recursive.

Container image. ghcr.io/bigbio/sdrf-skills holds the skills, the spec/ submodule, tools/ and data/ at /opt/sdrf-skills, with sdrf-tools, parse_sdrf, git, curl and jq on PATH. SDRF_SKILLS_ROOT and CLAUDE_PLUGIN_ROOT point at /opt/sdrf-skills. The parse_sdrf ontology cache is included, so validation works offline and as any user. The image installs the core dependencies from pyproject.toml without the [mcp] extra, and has no techsdrf. Tags: X.Y.Z, X.Y and latest for each release, main for the tip of main, and sha-<commit> for every build. To release, push a tag vX.Y.Z that matches the version in .claude-plugin/plugin.json; the workflow refuses any other tag.

docker run --rm -v "$PWD":/data ghcr.io/bigbio/sdrf-skills:main sdrf-tools doctor
docker run --rm -v "$PWD":/data ghcr.io/bigbio/sdrf-skills:main \
  parse_sdrf validate-sdrf --sdrf_file file.sdrf.tsv --template ms-proteomics --template human

以上为 README 的开头部分。其余内容请阅读原文。 查看完整 README ↗