Jinkō Skills

Agent skills for using Jinkō through AI assistants, coding agents, and agentic workflows, following the agentskills.io specification.

Jinkō is a platform for biology-grounded modeling, virtual populations, and In-Silico trial simulation. These skills make Jinkō easier for agents to use safely and consistently through the typed python jinko-sdk.

Vision

The scientific software of tomorrow is agent-ready.

That does not mean humans disappear from the workflow. It means agents can perform scoped, repeatable work while scientists keep control of assumptions, interpretation, and decisions.

These skills give AI coding agents specialized, task-focused workflows for modelers working with biology-grounded models on Jinkō.

The Jinkō UI remains the shared workspace where humans and agents can review, correct, validate, and continue work on mechanistic modeling and in silico trial simulation together.

Headless does not mean UI-less. It means agent-ready.

What is Jinkō?

Jinkō is Nova In Silico's platform for biology-grounded modeling and in silico trial simulation: it combines computational models with AI/ML to build digital twins and virtual cohorts, then runs simulations that help predict outcomes.

A complete project lifecycle can run through these skills and the SDK: scope evidence, curate data and knowledge, build or adapt a model, design multi-arm protocols, generate virtual populations, run trials at scale, and analyze results.

Jinkō comes in two flavors: multi-tenant SaaS (managed by Nova) or on-premise, deployed as a dedicated instance within your own private cloud environment, such as instances on AWS, Azure or Google Cloud Platform.

Installation

The skills in this repository are inter-dependant, and not meant to be picked individually; we strongly recommend you install all the Jinkō skills at once, and use an update method that can reliably get new ones.

Claude Web

In the settings, go to Plugins > Add > Add marketplace > type "novainsilico/jinko-skills" > Sync > hit the "+" button on the Jinkō plugin.

Claude Science

In the settings, go to Skills > Add skill > Import from GitHub > type "novainsilico/jinko-skills" > Preview > Import skills.

Claude Code CLI or IDE extension

In Claude Code, to add the Jinkō marketplace (this repo) and install its plugin:

/plugin marketplace add novainsilico/jinko-skills
/plugin install jinko@jinko
/reload-plugins # Or restart claude code

ChatGPT Web

Download this repository as a zip file. Go to chatGPT's plugins page from the sidebar (or Settings > Plugins > Browse plugins), go to the Skills tab, hit the "+" button, and "Upload from your computer". Drop the zip file there.

Codex

Add the Jinkō marketplace using either method:

Codex interface: Open Codex, run /plugins, select Add Marketplace, and enter novainsilico/jinko-skills.
Terminal:

codex plugin marketplace add novainsilico/jinko-skills

Then open /plugins, select the Jinkō marketplace, search for Jinko, and install the plugin. Start a new Codex session after installation.

Other agents and custom installations

Jinkō skills work with most agents that support the Agent Skills specification. Use the skills CLI to install every skill in this repository; the installer will guide you through the agent-specific setup:

npx skills add novainsilico/jinko-skills --skill '*'

You can also copy the skills directory to your agent's skills location and manage updates manually.

Why Jinkō is powerful for agents

Jinkō is engineered around guarantees that make autonomous and semi-autonomous agents safe and effective:

Versioned & Deterministic: Resources are versioned and immutable; simulations are reproducible from their referenced inputs. An agent can re-run, diff, and revert to any prior version in one step, with no hidden state.
Collaboration: Models, trials, data tables, and results are shared, human-readable assets. Agents, modelers, scientists, and project teams all operate on the same linked objects, breaking silos between R&D roles.
Auditability: Every action maps to a documented REST API operation. Each change spawns a labeled version snapshot recording who, what payload, and when, so an agent's work is fully traceable and reviewable.
Verifiable loops: The SDK exposes an actionable harness for agent workflows: agents can create or edit assets, call sanity checks, get feedback, apply a focused fix, and check again. Automated diagnostics cover issues such as unit checking failures, missing variables, invalid references, circular references, and trial setup problems before expensive runs start.
Validation & diagnostics: Built-in calibration, goodness-of-fit, and quantitative validation methods let agents compare model and trial outputs against data and surface actionable errors instead of silent failures.
Efficiency & scalability: Out-of-the-box parallelization runs large virtual populations and trial sweeps across many cores or a cluster, so agents can explore broad scenario spaces quickly.

Available skills

Together these skills cover a broad modeler workflow: from context, setup, and evidence scoping, through model and population design, to running in silico trials and analyzing results.

Skill	Purpose
`jinko`	Route a Jinkō request to the narrowest published skill that owns it.
`jinko-calibration-cmaes`	Create, run, poll, and inspect CMA-ES calibrations through the SDK.
`jinko-calibration-subsampling`	Create, run, and inspect subsampling virtual-population generation.
`jinko-context`	Core Jinkō concepts, navigation, and domain terminology.
`jinko-data-table`	Create or inspect data tables for overlays and calibration.
`jinko-document`	Create or update Jinkō documents from markdown through the SDK.
`jinko-model`	Build, edit, sanity-check, and debug Jinkō computational models.
`jinko-output-set`	Create, inspect, validate, and edit simple and advanced output sets through the SDK.
`jinko-protocol`	Design or edit multi-arm protocol designs.
`jinko-reference`	Create, inspect, download, and extract evidence from reference PDFs.
`jinko-sdk-setup`	Authenticate and configure access to a Jinkō project.
`jinko-solution-and-product-guide`	Guidance on Jinkō solutions and product features.
`jinko-task-cmaes`	Execute a confirmed CMA-ES calibration from prepared Jinkō inputs.
`jinko-task-define-param-to-calibrate`	Classify model inputs and assign them to calibration steps.
`jinko-task-extract-data-table`	Extract traceable evidence into readable or Jinkō-ready tables.
`jinko-task-export-to-matlab`	Export a Jinkō model to SimBiology and compare MATLAB simulations with Jinkō results.
`jinko-task-from-nonmem`	Convert a NONMEM run into a validated Jinkō model and trial setup.
`jinko-task-ichm15-planning`	Get recommendations on preparing the documentation related to ICHM15 planning phase.
`jinko-task-literature-search`	Discover and prioritize biomedical publications from PubMed.
`jinko-task-paper2model`	Reconstruct a published mechanistic model in Jinkō and report its fidelity and confidence.
`jinko-task-trial-data-scoping`	Discover and prioritize ClinicalTrials.gov registry records.
`jinko-trial`	Set up, sanity-check, run, poll, and download in silico trial results.
`jinko-trial-viz`	Create, inspect, sanity-check, and retrieve Jinkō trial visualizations.
`jinko-vpop`	Create, generate, and inspect virtual populations.

Need a Jinkō account? Request a demo to get started.

SDK compatibility

Skills that use the Jinkō SDK declare a compatible SDK version range in their SKILL.md metadata (requires_sdk, a PEP 440 specifier). Install or upgrade the official SDK, then use jinko-sdk-setup to verify the installed version against the skills bundle:

pip install --upgrade jinko-sdk

Start with jinko-sdk-setup to verify your credentials and SDK installation before using the workflow skills. The SDK reference is documented at doc.jinko.ai/sdk.

License

MIT: see LICENSE.

Maintained by Nova In Silico. This repository is published from an internal monorepo. You can open issues, discussions, and skill requests directly in this public GitHub repository.