jinko-task-define-param-to-calibrate

Define Parameters To Calibrate

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. A session code can be exchanged only once, so call get_session_code again before this skill's own SDK process. If that skill is not found, install it from novainsilico/jinko-skills.

Classify model inputs from supplied evidence; do not infer unsupported provenance or invent calibration steps.

Inputs

Require:

• a model SID;
• documentation or other evidence for the current input values;
• an explicit ordered list of calibration step identifiers and each step's biological scope;
• any user overrides and whether existing labels may be replaced.

Labels

Assign at most one source tag per eligible input:

• s::knowledge: supported by literature, expert knowledge, or a reference model;
• s::arbitrary: deliberately fixed without an evidence-derived value;
• s::to-calibrate: insufficiently informed and intended for calibration;
• s::calibrated: preserve when present; never assign in this task.

Assign CalibIter::<step> only with s::to-calibrate, using an explicitly provided step whose scope covers the input. Absence means the input is not assigned to calibration. Leave uncertain inputs unchanged and report them.

Workflow

1. Use jinko-model to inspect parameters, categorical parameters, and species initial conditions. Exclude derived formulas, technical infrastructure, and non-input components.
2. Preserve existing s::* and CalibIter::* tags unless relabeling was requested. For each remaining input, classify its source from the evidence.
3. Map every s::to-calibrate input to the first supplied step whose scope fully covers its biological role. If no unique step qualifies, leave it unchanged and add it to todo.
4. Write the proposed mutations as JSON and run scripts/apply_calibration_labels.py in dry-run mode, then with --apply after review. The script validates source/step consistency, duplicate assignments, component kinds, existing-label conflicts, and allowed model mutations before applying one component batch.
5. Re-fetch the model and return the new revision and snapshot with a compact report: assigned and preserved labels, counts by source and step, and todo entries with reasons.

The mutation plan has this shape:

{
  "in_scope_steps": ["2", "3"],
  "assignments": [
    {"component_id": "k_elim", "source": "to-calibrate", "calibration_step": "2"},
    {"component_id": "body_weight", "source": "knowledge"}
  ]
}

Do not change values, units, descriptions, equations, structure, or unrelated tags. todo items are reported, not encoded as placeholder tags.