القائمة
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.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
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