jinko-output-set

Jinkō Output Set SDK Workflows

Use this skill for output-set mechanics through the SDK: creating, validating, inspecting, and incrementally editing simple and advanced output sets. Keep trial attachment in jinko-trial, calibration attachment in jinko-calibration-cmaes, and data tables in jinko-data-table.

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.

Core Concepts

Jinkō uses different names for the same objects in the UI, the API, and the SDK.

UI wording	API project-item type	SDK entry points
Simple output set	MeasureDesign	client.create_simple_output_set(...), SimpleOutputSet
Advanced output set	ScoringDesign	client.create_advanced_output_set(...), AdvancedOutputSet

An advanced output set groups three component kinds: constraints (boolean per-patient eligibility expressions), scalars (numeric formulas derived from simulation output), and objectives (range-based scoring rules with a weight). Scalars serialize under the raw JSON key components.measures — do not confuse with simple-output-set "measures".

Discovering Output Ids

Never invent output ids. Call model.time_dependent_ids() to discover the model's valid output ids before creating a simple output set, same as jinko-trial does. Advanced-output-set formulas reference output ids or other scalar ids by name. Scoring-formula time values are always seconds, regardless of the model time unit. Formulas do not support time-unit annotations: convert scientific times explicitly. Consequently, int(X) and auc(X) have a seconds time component; declare their scalar units with *s. If unsure whether a piece of formula syntax is still supported, validate it with client.validate_scoring_formula(...) rather than assuming old examples still apply. Read references/formula-language.md before writing any non-trivial constraint/scalar/objective formula or component filter. It gives the actual grammar (time reduction functions, time indexing, arm references) and documents validation messages.

Simple Output Set Workflow

model = client.get_model("cm-...")
output_set = client.create_simple_output_set(model, model.time_dependent_ids())
# Equivalently: model.create_simple_output_set([...])

Measures accept output-id strings (shorthand for {"timeseriesId": id}) or dicts for custom names, origins, and point-in-time/across-time functions. Edit with .content(), .measures, .replace_all_measures(), .add_measures(), .remove_measures(), .update_measure(). See references/simple-output-set.md for the full schema.

Advanced Output Set Workflow

scoring_design = client.create_advanced_output_set(
    constraints=[{"id": "adults", "constraint": "age >= 18"}],
    scalars=[{"id": "auc", "formula": "auc(Drug)", "unit": "mg/L*s"}],
    objectives=[
        {
            "id": "obj_auc",
            "formula": {
                "target": "auc(Drug)",
                "range": {
                    "narrowRangeLowBound": 8.0,
                    "narrowRangeHighBound": 12.0,
                    "wideRangeLowBound": 5.0,
                    "wideRangeHighBound": 15.0,
                },
            },
            "weight": 1.0,
        }
    ],
    name="PK scoring",
)

Add components incrementally with scoring_design.components.add_constraint(...), .add_scalar(...), .add_objective(...) — each call creates a new versioned snapshot. For a batch, validate every component and check all ids for conflicts before the first call; if a later request fails, report the ids already applied. See references/advanced-output-set.md for full schemas, the components service, and the raw JSON escape hatch.

Validation & Diagnostics

Constraint and formula expressions are validated automatically inside create() and components.add_*(). They always raise ValidationError on failure — show_validation only controls whether a report is printed, it does not suppress the raise.

if scoring_design.diagnostics.errors():
    print(scoring_design.diagnostics.errors().explain())

sd.diagnostics is chainable: .for_kind(...), .with_severity(...), .with_code(...), .for_component(...), .errors(), .warnings(), .has_errors(), .by_component(), .by_kind(), .by_severity(), .explain(). Use sd.diagnostics_at(revision) for a historical snapshot.

What this validates, and what it does not. client.validate_scoring_formula(...)/validate_scoring_condition(...) and sd.diagnostics only check the scoring design in isolation — formula syntax, referenced ids resolving to something, constraint/objective shape. They do not know about any concrete trial. A formula can pass every check here and still fail once the advanced output set is bound to a trial (e.g. it references a measure that exists on this model but not on the trial's simple output set, or a unit/time-window mismatch with the trial's protocol). Passing standalone validation is necessary, not sufficient, for trial compatibility — never report an advanced output set as "trial-ready" based on this skill's checks alone. Use jinko-trial's trial-sanity step (trial.sanity()) once the output set is attached to a concrete trial.

Attaching to Trials/Calibrations

This skill only creates, inspects, and edits output sets — it does not attach them or run anything, and it cannot confirm trial compatibility (see above). For trials, use jinko-trial: model.create_trial(simple_output_set=..., advanced_output_set=..., ...), then run trial.sanity() before launch — see jinko-trial's pre-launch check. For calibrations, use jinko-calibration-cmaes for the SDK call (model.create_calibration(simple_output_set=..., advanced_output_set=..., ...)). A calibration needs at least one fitness-function source: a data table with validForFitnessFunction: True (see jinko-data-table) and/or an advanced output set with objectives.

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_simple_output_set: dry-run by default, creates a simple output set with --apply.
• jinko.cli.create_advanced_output_set: dry-run by default, creates an advanced output set with --apply.
• jinko.cli.inspect_output_set: inspects an existing simple or advanced output set.
• jinko.cli.edit_advanced_output_set: adds constraints/scalars/objectives to an existing advanced output set.

python -m jinko.cli.create_simple_output_set --model-sid cm-... --output-id Drug
python -m jinko.cli.create_simple_output_set --model-sid cm-... --output-id Drug --folder 2026-07-07-output-sets --create-folder --apply
python -m jinko.cli.create_advanced_output_set --constraint "adults:age >= 18" --scalar "auc:auc(Drug)" --name "PK scoring"
python -m jinko.cli.create_advanced_output_set --from-json skills/jinko-output-set/assets/advanced_output_set_example.json --apply
python -m jinko.cli.inspect_output_set --kind advanced --sid sc-... --diagnostics
python -m jinko.cli.edit_advanced_output_set --sid sc-... --add-objective "obj_auc:auc(Drug):8:12:5:15:1.0" --show-diagnostics --apply

Reference Routing

• Read references/simple-output-set.md for measure dict shapes and editing methods.
• Read references/advanced-output-set.md for constraint/scalar/objective shapes, validation, diagnostics, and tags.
• Read references/formula-language.md for the constraint/scalar/objective formula grammar (functions, time indexing, arm references) and its pitfalls.