novainsilico/jinko-skills

jinko

- Discover and route Jinkō QSP and mechanistic-modeling requests to the public Jinkō skill that owns the work.

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Jinkō Skill Router

Identify the user's immediate intent and load the narrowest published owner. Do not reproduce its mechanics. Task skills own task-level decisions and orchestration; resource skills own reusable Jinkō SDK/API mechanics.

Routing Map

  • Connection and credentials: jinko-sdk-setup.
  • Terminology and navigation: jinko-context.
  • Product capabilities and model-library discovery: jinko-solution-and-product-guide.
  • Publication discovery: jinko-task-literature-search.
  • ClinicalTrials.gov discovery: jinko-task-trial-data-scoping.
  • Reference PDFs and source extracts: jinko-reference.
  • Evidence-to-table extraction: jinko-task-extract-data-table.
  • MATLAB/SimBiology export and solver parity: jinko-task-export-to-matlab.
  • NONMEM model and trial conversion: jinko-task-from-nonmem.
  • Data-table mechanics: jinko-data-table.
  • Model mechanics: jinko-model.
  • Calibration-input classification: jinko-task-define-param-to-calibrate.
  • Confirmed CMA-ES execution: jinko-task-cmaes; SDK mechanics: jinko-calibration-cmaes.
  • Protocol mechanics: jinko-protocol.
  • Virtual-population mechanics: jinko-vpop.
  • Trial mechanics: jinko-trial.
  • Trial visualization mechanics: jinko-trial-viz.
  • Document mechanics: jinko-document.

If a request spans several areas, state which owner applies to the immediate request and let that skill determine its own prerequisites or handoffs. Do not invent a project plan, artifact checklist, stage order, or completion gate here.

Missing Capabilities

The public skills are an interdependent plugin bundle. If a named owner is not available, direct the user to install or update the complete Jinkō plugin from novainsilico/jinko-skills, then start a fresh session or reload plugins. Do not recommend installing one skill in isolation.

Resource Links

When an owner returns a resource, surface its SDK-provided .url or returned resource URL. Never construct a link from a SID and a hard-coded hostname; configured JINKO_URL and on-premises application URLs must be preserved.

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Mais Skills

Todos os Skills
novainsilico
Comunidade

jinko-context

- Explain core Jinkō context, navigation, version management, and domain language for agents and users. Use this skill whenever the user needs a mental model of Jinkō projects, folders, project items, snapshots, sources, extracts, protocols, trials, calibration, virtual populations, references, or modeling context; when translating between generic terms and Jinkō terminology; or when an agent needs orientation before navigating or modifying Jinkō artifacts. This skill is conceptual and terminology-focused; use dedicated jinko- workflow skills for creating or editing specific artifacts.

instalações
1
GitHub Stars
1
Atualizado
7 de set.
novainsilico
Comunidade

jinko-data-table

- Create or inspect Jinkō data tables via the jinko-sdk. Use this skill whenever the user wants to upload observed data for trial overlays or calibration objectives from CSV, SQLite, or pandas DataFrame; check data-table schema columns; inspect existing data tables; or verify metadata.public.validForFitnessFunction. Do not use this skill for output sets; use jinko-output-set for that.

instalações
1
GitHub Stars
1
Atualizado
7 de set.
novainsilico
Comunidade

jinko-sdk-setup

Authenticate and configure access to a Jinkō project via the jinko-sdk. Use this skill whenever the user wants to connect to Jinkō, install the SDK, set up credentials or a .env file, verify API access, fail-fast check that a JINKOAPIKEY and JINKOPROJECTID work, or debug ConfigurationError, AuthenticationError, or AuthorizationError from the SDK. Do not use this skill for creating models, vpops, protocols, output sets, or trials.

instalações
1
GitHub Stars
1
Atualizado
7 de set.
novainsilico
Comunidade

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.

instalações
1
GitHub Stars
1
Atualizado
7 de set.