Jinko Data Table

Jinkō Data Table SDK Workflows

Use this skill for data-table mechanics through the SDK. Data tables can support trial overlays and calibration objectives; the row schema is the same, and fitness-function compatibility is reported by metadata when available.

PREREQUISITE: This skill needs an initialized jinko-sdk connection and an SDK satisfying its metadata.requires_sdk range. Run the jinko-sdk-setup skill (../jinko-sdk-setup/SKILL.md) and proceed only once its check passes. If that skill is not found, install it from novainsilico/jinko-skills.

Scope

• Use client.create_data_table_from_csv() for CSV files or bytes.
• Use client.create_data_table_from_sqlite() for SQLite files or bytes.
• Use client.create_data_table_from_dataframe() for pandas DataFrames.
• Inspect existing data tables with get_data_table(), content(), summary(), validate(), and export().
• Check metadata.public.validForFitnessFunction after creation or inspection when available if the data table needs to be attached through trial/calibration dataTableDesigns.
• For trial workflows that attach data tables through jinko-trial, use a data table with validForFitnessFunction: True; point-value overlay tables may upload successfully but fail trial launch sanity.

Project Folder Hygiene

• Prefer creating data tables inside a dedicated Jinkō folder instead of the project root. At the start of a workflow, ask for or propose a folder name, for example YYYY-MM-DD-<experiment-name>.
• Reuse an existing exact-match folder when possible: client.get_folder_by_name(name, exact_match_only=True).
• If the folder does not exist, create it only after user confirmation or when a script is run with --apply.
• Resolve one folder object or folder id, then pass folder=folder to SDK creation calls that support it.

Row Schema

Read assets/data-table.json before changing CSV structure.

Supported row shapes:

• Point-value row: obsId, time, value, plus optional unit, armScope, ranges, weight, and reference.
• Range row: obsId, time, narrowRangeLowBound, narrowRangeHighBound, plus optional unit, armScope, wide ranges, weight, and reference.

Use ISO-8601 duration strings for time, for example PT0S, PT6H, or P1D.

Bundled Assets

• assets/toy_data_table_values.csv: point-value observations for trial overlays.
• assets/toy_data_table_ranges.csv: range observations suitable for calibration objective workflows.
• assets/data-table.json: schema subset for supported data-table rows.

SDK Scripts

These are on PATH as console scripts once the SDK is installed, and also runnable via python -m as shown below.

• jinko.cli.create_data_table: dry-run-validates every CSV row and creates a data table with --apply; use --allowed-obs-id, --require-unit, --require-experiment-ref, and --require-fitness for calibration inputs.
• jinko.cli.inspect_data_table: inspects existing data tables and can enforce fitness compatibility with --require-fitness.

Examples:

python -m jinko.cli.create_data_table --source skills/jinko-data-table/assets/toy_data_table_ranges.csv --method csv
python -m jinko.cli.create_data_table --source extracted.csv --allowed-obs-id Drug --require-unit --require-experiment-ref --require-fitness --apply
python -m jinko.cli.create_data_table --source skills/jinko-data-table/assets/toy_data_table_ranges.csv --method csv --apply
python -m jinko.cli.create_data_table --source skills/jinko-data-table/assets/toy_data_table_ranges.csv --method csv --folder 2026-06-15-fit-data --create-folder --apply
python -m jinko.cli.create_data_table --source skills/jinko-data-table/assets/toy_data_table_values.csv --method dataframe --apply
python -m jinko.cli.inspect_data_table --data-table-sid dt-... --fitness --validate

Reference Routing

• Read references/data-table-schema.md for row shape and fitness-function notes.
• Read assets/data-table.json when checking required columns.