jinko-task-export-to-matlab

Jinkō to MATLAB / SimBiology

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.

Guide the workflow, but use scripts/jinko_matlab_parity.py for deterministic file handling and comparison maths. The helper is an implementation detail: do not present it as a CLI users must learn or ask them to reproduce its commands. This avoids repeated ad-hoc code and preserves context for the scientific result.

MATLAB MCP capability gate

Before launching MATLAB, running MATLAB code, or saying that a MATLAB/SimBiology MCP server is unavailable, explicitly inspect the complete callable tool registry for MATLAB or SimBiology capabilities. Include lazily discoverable tools; do not infer MCP absence from the initial tool summary alone. In environments that expose ALL_TOOLS, query it for matlab or simbiology; otherwise use the platform's tool-discovery mechanism.

A callable MATLAB evaluation or script-execution MCP tool means the server is available. State that finding and use the MCP for MATLAB inspection, execution, and result retrieval.
Use a local MATLAB CLI only after the full capability check finds no suitable MATLAB MCP operation. Record the fallback succinctly so the user can distinguish it from an MCP-backed run.
Do not continue after an ambiguous discovery result: resolve it before choosing the execution path. This gate prevents an invisible or lazy-loaded MCP server from being bypassed.

Start with the model, not the outputs

Complete the MATLAB MCP capability gate. Use the server when available; do not install or reconfigure one without the user's direction.
Ask only for the CM short ID (a Jinkō CM link is sufficient; extract its ID). Export before asking which outputs to compare.
Create a dedicated run directory such as /tmp/jinko-to-matlab/<cm-sid>/ and invoke the helper's export command there. It safely extracts the bundle, requires exactly one workbook and its matching SimulationSettings_<model_name>.yaml, validates the settings, and writes parity-manifest.json with content hashes.

Do not ask for repeated confirmation after successful access, export, or manifest validation. State the resulting workbook and settings paths succinctly. The helper preserves existing manifests and result files by default. Review the existing artifacts before using --overwrite for an intentional rerun.

Reuse the imported project

Read reuse.workbook_unchanged in the manifest before requesting any Model Builder action.

If a discoverable saved .sbproj exists and the workbook is unchanged, reuse it. Apply the current exported settings for every comparison; a settings-only update does not require a reimport.
If no saved project exists, the workbook changed, or several candidate projects are found, ask one concise question. For a changed/missing project, direct the user to create a blank Model Builder model, delete its default unnamed compartment, then use Home → Model → Import Model Components from Excel. Have them save the project in the run directory. Removing that placeholder prevents an extra compartment from being carried into the imported model. This is intentionally the only mandatory manual step: SimBiology has documented SBML/project imports, not an Excel-component import API.
Never overwrite the supplied .sbproj. Load it into MATLAB and make all configuration/dose changes in memory for the current comparison only.

Offer a single comparison choice

Use the helper's list-series command to obtain statically time-dependent IDs. Display them as the eligible outputs and ask the user to choose one to ten. Do not include Time in that list or count it against the limit. Do not silently select, rename, or substitute components.

Once the user selects outputs, call the helper's solve command. It sends the exact Jinkō payload ['Time', *selected_ids]; Time is case-sensitive and is not returned unless requested. Pass the export's parity-manifest.json to the command and require every requested series and Time in the response. The helper converts Jinkō's returned seconds into the exported TimeUnits and writes matlab_output_times; use those values as MATLAB OutputTimes and never derive a uniform grid or pass raw Jinkō seconds to a differently configured MATLAB model.

Run MATLAB with the exported configuration

Load the saved project, resolve the requested model (ask only if the project has multiple models), and use its active configuration set. Apply the manifest's:

When the capability gate found a MATLAB MCP server, execute these operations with that MCP in the existing MATLAB session. Do not replace it with a local CLI run.

configset.CompileOptions.DefaultSpeciesDimension = settings.CompileOptions.DefaultSpeciesDimension;
configset.CompileOptions.DimensionalAnalysis = settings.CompileOptions.DimensionalAnalysis;
configset.CompileOptions.UnitConversion = settings.CompileOptions.UnitConversion;
configset.SolverOptions.AbsoluteTolerance = settings.SolverOptions.AbsoluteTolerance;
configset.SolverOptions.RelativeTolerance = settings.SolverOptions.RelativeTolerance;
configset.SolverType = settings.SolverType;
configset.TimeUnits = settings.TimeUnits;
configset.StopTime = settings.StopTime;
configset.SolverOptions.OutputTimes = matlabOutputTimes;

Before simulation, inspect model doses. If any are inactive, tell the user their names and ask whether to activate them for this in-memory parity run, keep their saved state, or stop. Do not persist that choice. Run sbiosimulate, collect only the selected series and their units, and write a compact MATLAB-results JSON:

{
  "dose_decision": "activate inactive doses for this run",
  "time": { "unit": "second", "values": [0, 3600] },
  "series": [{ "id": "Tumor", "unit": "mole", "values": [1, 2] }]
}

time.unit must reflect the MATLAB output time unit; each series ID must match the selected Jinkō ID exactly. A missing component or unsupported unit conversion is a mapping failure, not a reason to guess.

Compare and report

Invoke the helper's compare command with the Jinkō and MATLAB JSON files. It:

converts compatible mole/substance_count values using Avogadro's constant;
converts common time units before alignment;
retains the final value at each duplicate Jinkō timestamp (the post-event state) for pointwise matching, then integrates on unique increasing times;
reports pointwise maximum absolute/relative error and mismatch count using abs <= 1e-8 + 1e-6 * abs(jinko) as diagnostics;
computes trapezoidal Jinkō and MATLAB AUCs, and marks relative AUC error above 1% as WARN. If the reference AUC is zero, report absolute AUC error and AUC status N/A; if any selected series has zero reference AUC, the overall status is also N/A, never an unqualified PASS.

Present comparison.md as the compact result table. Make the AUC status the headline result; explain that pointwise mismatches can reflect solver/event-time differences. Keep comparison.json and comparison.csv as run artifacts, including units, conversions, selected outputs, and duplicate-time policy.

Failure handling

Stop with the specific precondition when settings, output IDs, or time alignment are missing. Do not continue with inferred values.
Require explicit, compatible units and finite numeric time-series values. A missing unit, NaN, or infinity is a failed comparison input, not agreement.
Keep selection at one to ten requested outputs. Time is mandatory internally.
Do not edit the Jinkō model, its solving options, or the source project.
Without MATLAB MCP, give the minimum commands for the current project and ask only for the MATLAB-results JSON needed by the helper; do not create a new persistent user workflow.