Arc Creator

Cleanup Review

python scripts/_purge_cache.py is an optional cleanup helper for the ARC Creator skill. It may recursively remove /tmp/skill_cache, /var/tmp/skill_output. Show the exact paths first, explain whether they are skill-private temporary paths or user state, and run the cleanup only after the user confirms.

ARC Creator

Create FAIR Digital Objects following the nfdi4plants ARC specification v3.0.0.

Prerequisites

• git and git-lfs installed
• ARC Commander CLI at ~/bin/arc (optional but recommended)
• For DataHUB sync: Personal Access Token for git.nfdi4plants.org or datahub.hhu.de

Interactive ARC Creation Workflow

Guide the user through these phases in order. Ask questions conversationally — don't dump all questions at once. Batch 2-4 related questions per message.

Phase 1: Investigation Setup

Ask the user:

1. Investigation identifier (short, lowercase-hyphenated, e.g. cold-stress-arabidopsis)
2. Title (concise name for the investigation)
3. Description (textual description of the research goals)
4. Where to store the ARC locally (suggest /home/uranus/arc-projects/<identifier>/)

Then run scripts/create_arc.sh <path> <identifier> and set investigation metadata via:

arc investigation update -i "<id>" --title "<title>" --description "<desc>"

Phase 2: Studies

For each study, ask:

1. Study identifier (e.g. plant-growth)
2. Title and description
3. Organism (for Characteristic [Organism])
4. Growth conditions (temperature, light, medium, etc.)
5. Source materials (what goes in — seeds, cell lines, etc.)
6. Sample materials (what comes out — leaves, roots, extracts, etc.)
7. Protocols — does the user have protocol documents to include?
8. Factors — what experimental variables are being tested? (e.g., temperature, genotype, treatment)

Create with:

arc study init --studyidentifier "<id>"
arc study update --studyidentifier "<id>" --title "<title>" --description "<desc>"

Copy protocol files to studies/<id>/protocols/. Copy resource files to studies/<id>/resources/.

Phase 3: Assays

For each assay, ask:

1. Assay identifier (e.g. proteomics-ms, rnaseq, sugar-measurement)
2. Measurement type (e.g., protein expression profiling, transcription profiling, metabolite profiling)
3. Technology type (e.g., mass spectrometry, nucleotide sequencing, plate reader)
4. Technology platform (e.g., Illumina NovaSeq, Bruker timsTOF)
5. Data files — where are the raw data files? (will go into assays/<id>/dataset/)
6. Processed data — any processed output files?
7. Protocols — assay-specific protocols?
8. Performers — who performed this assay? (name, affiliation, role)

Create with:

arc assay init -a "<id>" --measurementtype "<type>" --technologytype "<tech>"

Copy data to assays/<id>/dataset/, protocols to assays/<id>/protocols/.

Phase 4: Workflows (optional)

Ask if there are computational analysis steps. For each:

1. Workflow identifier (e.g. deseq2-analysis, heatmap-generation)
2. Description of what it does
3. Code files (scripts, notebooks)
4. Dependencies (Python packages, R libraries, Docker image)

Place code in workflows/<id>/. Note: workflow.cwl is REQUIRED by spec but often created later. Inform user.

Phase 5: Runs (optional)

Ask if there are computation outputs. For each:

1. Run identifier
2. Which workflow produced it
3. Output files (figures, tables, processed data)

Place outputs in runs/<id>/.

Phase 6: Contacts & Publications

Ask:

1. Investigation contacts (name, email, affiliation, role — at minimum the PI)
2. Publications (if any — DOI, PubMed ID, title, authors)

Add via:

arc investigation person register --lastname "<last>" --firstname "<first>" --email "<email>" --affiliation "<aff>"

Phase 7: Git Commit & DataHUB Sync

1. Configure git user:

git config user.name "<name>"
git config user.email "<email>"

2. Commit:

git add -A
git commit -m "Initial ARC: <investigation title>"

3. Ask if the user wants to push to a DataHUB. If yes:
   • Ask which host (git.nfdi4plants.org, datahub.hhu.de, etc.)
   • Create remote repo (via browser or API)
   • Set remote and push

ISA Metadata Reference

For detailed ISA-XLSX fields, annotation table columns, and ontology references, read references/arc-spec.md.

Key Reminders

• Assay data is immutable — never modify files in assays/<id>/dataset/ after initial placement
• Studies describe materials, assays describe measurements
• Workflows are code, runs are outputs
• Git LFS for files > 100 MB: git lfs track "*.fastq.gz" "*.bam" "*.raw"
• Don't store ARCs on OneDrive/Dropbox — Git + cloud sync causes conflicts
• ARC Commander CLI reference: arc <subcommand> --help