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novainsilico/jinko-skills97 installs

jinko-task-cmaes

Execute a CMA-ES calibration from confirmed Jinkō inputs: assemble the model, protocol, output sets, fitness data tables, parameter priors, and optimizer options; create and run the Calibration; and return the supported results. Use when the user wants to perform a CMA-ES calibration, not when they need to choose a calibration strategy, infer priors, design objectives, or decide whether results are acceptable.

How do I install this agent skill?

npx skills add https://github.com/novainsilico/jinko-skills --skill jinko-task-cmaes
view source ↗

Is this agent skill safe to install?

  • Gen Agent Trust Hubpass

    The skill facilitates scientific calibration tasks on the Jinkō platform using vendor-provided SDKs and scripts. It manages the assembly of models and execution of CMA-ES optimization workflows within the Jinkō environment.

  • Socketpass

    No alerts

  • Snykpass

    Risk: LOW · No issues

What does this agent skill do?

CMA-ES Calibration Task

Execute a confirmed calibration specification. Do not invent objectives, constraints, parameter priors, optimizer options, or acceptance criteria.

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.

Inputs

Require:

  • a model SID;
  • parameter priors with physical bounds;
  • seed, thresholdWeightedScore, numberOfIterations, and populationSize;
  • at least one fitness source: calibration-ready data tables and/or an advanced output set containing objectives;
  • any protocol, simple output set, advanced output set, folder, and name needed by the specification.

If quantitative evidence has not yet been converted into a calibration-ready table, use jinko-task-extract-data-table. Use jinko-data-table, jinko-output-set, jinko-model, and jinko-protocol only for their respective Jinkō object mechanics.

Workflow

  1. Resolve every input to its intended SID and snapshot. Present missing or ambiguous inputs instead of guessing.
  2. Use jinko-calibration-cmaes and its bundled creation script. Review its dry-run output before applying it. The script owns parameter encoding, fitness-table eligibility, bound scaling, creation, and post-creation sanity; stop on an error and surface warnings.
  3. Return the created calibration SID, revision, snapshot, URL, and effective options for confirmation.
  4. Use the lower-level run script to perform pre-launch sanity, launch, and wait for a terminal state. Do not relaunch a terminal snapshot; create or update a configuration so the intended change has a new snapshot.
  5. Use the lower-level inspection interfaces to collect the final status, stopping reason, performance, results summary, objective weights, and the patient sorted first by optimizationWeightedScore when available. Fetch per-patient scalars, timeseries, errors, or augmented data tables only when their required selectors are present in the result metadata.

Return

Return:

  • calibration SID, revision, snapshot, and URL;
  • effective input references, priors, and optimizer options;
  • sanity warnings, terminal status, stopping reason, and performance;
  • supported result payloads and best-patient identity, with the iteration and scenario arm needed for subsequent result calls;
  • a concise account of unavailable requested outputs.

Do not claim a separate run ID, convergence analysis, score-evolution curve, best-patient parameter values, parameter posterior, or simulation-vs-data plot unless the returned API payloads actually provide the required data.

Add the canonical catalog link to the repository README so users can inspect current installs and available audits. The publishing guide covers the complete discovery path.

<a href="https://skillzs.dev/skills/novainsilico/jinko-skills/jinko-task-cmaes">View jinko-task-cmaes on skillZs</a>