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Use when the user wants to plan what metadata to capture for a new experiment, or discuss experimental design and SDRF strategy before creating the file.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
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.
| name | sdrf:brainstorm |
| description | Use when the user wants to plan what metadata to capture for a new experiment, or discuss experimental design and SDRF strategy before creating the file. |
| user-invocable | true |
| argument-hint | [experiment description] |
You are helping the user plan their SDRF annotation BEFORE creating the file. This is a collaborative thinking session, not a file generation task.
Ask the user about (if not already provided):
Use the sdrf:templates decision tree to select the right combination. Reference the 5 template layers:
ms-proteomics or affinity-proteomicshuman, vertebrates, invertebrates, or plantsdia-acquisition, cell-lines, single-cell, immunopeptidomics, crosslinkingclinical-metadata, oncology-metadatametaproteomics + child (human-gut, soil, water)Read spec/sdrf-proteomics/sdrf-templates/templates.yaml to confirm template names and current versions.
Present the recommendation:
Your experiment: [description]
Recommended templates:
1. ms-proteomics (required — mass spectrometry experiment)
2. human (organism is Homo sapiens)
3. clinical-metadata (patient samples with treatment data)
4. oncology-metadata (cancer study — adds tumor staging)
This combination requires these columns: [read from TERMS.tsv, filter by template names in usage]
And recommends these additional columns: [read from template YAMLs for recommended columns]
Read spec/sdrf-proteomics/TERMS.tsv and filter by the selected template names to list the columns.
Find reference datasets to learn from:
Search PRIDE for similar experiments:
mcp PRIDE → search_extensive(query="<keywords>")
Search publications for standard experimental designs:
mcp PubMed → search_articles(query="<keywords> AND proteomics")
Search bioRxiv for recent preprints:
mcp bioRxiv → search_preprints(category="biochemistry" or "cell biology", recent_days=180)
Present findings:
Similar datasets found:
- PXD012345: TMT phosphoproteomics of breast cancer (24 samples, 12 fractions)
- PXD023456: Label-free DIA of liver cancer tissue (30 patients)
Common design patterns in this field:
- Typical sample size: 10-30 per group
- Common labels: TMT, label-free DIA, SILAC
- Standard fractionation: 12-24 high-pH RP fractions
- Most include: age, sex, disease staging
Present a complete column plan organized by importance:
Columns required by the selected template combination. For each: explain what it is, what ontology to use, and give examples.
Columns that 70%+ of similar experiments include. For each: explain why it adds value.
Columns that would increase reusability and findability. For each: explain the benefit.
Discuss what the experimental comparison is:
Raise potential issues proactively:
Help the user understand the scale:
Your SDRF will have:
Rows: [samples] × [fractions] × [label channels] × [technical replicates]
Example: 20 patients × 12 fractions × 1 (label-free) × 1 replicate = 240 rows
Example: 20 patients × 12 fractions × 10 (TMT10plex) × 1 replicate = 2,400 rows
Columns: ~15 required + ~8 recommended + factor values = ~25 columns
Create a clear annotation plan the user can follow:
## SDRF Annotation Plan for [experiment]
Templates: ms-proteomics + human + oncology-metadata
Rows: ~240 (20 patients × 12 fractions)
Columns: 26
Required metadata to collect:
- Patient demographics: age, sex (from clinical records)
- Diagnosis: specific cancer subtype (from pathology)
- Tumor staging: TNM stage, grade (from clinical records)
- Tissue type: primary tumor vs adjacent normal
Technical metadata (from instrument):
- Instrument model, fragmentation method
- Mass tolerances, collision energy
- Label type and channel assignments
Factor values: disease (tumor vs normal)
Next step: Run /sdrf:annotate to create the file