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jinko-solution-and-product-guide

jinko-solution-and-product-guide

github.com/novainsilicoOther4159 installsUpdated 6d ago
Repository

About jinko-solution-and-product-guide

A guidance skill for matching Jinko models and platform capabilities to scientific and modeling objectives. It uses model-library metadata, highlights assumptions and limitations, and recommends an actionable next step.

Key features

  1. 01Scientific objective clarification
  2. 02Filtered model-library search
  3. 03Mapping features to workflow needs
  4. 04Tradeoff and limitation analysis
  5. 05Prioritized next-step recommendations

Use cases

  1. 01Find candidate models for a scientific question
  2. 02Compare modeling approaches under constraints
  3. 03Relate model choices to digital twin use cases

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/novainsilico/jinko-skills --skill jinko-solution-and-product-guide

After installing, type/jinko-solution-and-product-guidein Claude Code to confirm it loaded.

Bundled filesagents/openai.yamlscripts/search_model_library.py

Details

From the project README

Jinko solution and product guide

Overview

Use this skill to explain how Jinko solutions and product features can support a user's scientific or modeling objective.

Focus on practical guidance that helps users choose models, workflows, and platform capabilities with clear rationale and explicit limitations.

When to use

Trigger this skill when the user asks to:

  • Find candidate models to start from for a scientific question
  • Understand Jinko platform features relevant to a modeling strategy
  • Compare solution paths based on scientific objective, constraints, and timeline
  • Identify where AI capabilities can accelerate routine steps
  • Connect model choices to digital twin use cases

Workflow

  1. Clarify objective and context
  • Restate the scientific objective in first-principles terms.
  • Capture scope, modality, disease area, time horizon, and constraints.
  1. Identify model options
  • Run scripts/search_model_library.py with a narrow --query and, when known, exact --type, --group, or --access filters. The read-only script fetches only the fixed trusted endpoint, bounds the response and output, validates every required field, and sorts results deterministically. Do not replace it with an arbitrary URL fetch.
  • Use the returned projectName, type, groupName, description, contextOfUse, inputs, outputs, and Access to assess fit.
  • Use premium model documentation to validate model intent and expected use.
  • Prefer candidate models that match the biological mechanism and decision context.
  1. Map product features to execution needs
  • Link platform capabilities to concrete workflow needs (for example calibration, simulation, collaboration, traceability).
  • Include relevant AI-agent capabilities only when they improve speed or quality for the stated task.
  1. Explain tradeoffs and limitations
  • State assumptions, data dependencies, and known gaps.
  • Distinguish established capability from inferred fit.
  • Avoid over-claiming and flag where evidence is incomplete.
  1. Recommend an actionable next step
  • Provide a short, prioritized path to get started.
  • Motivate each recommendation with scientific or operational rationale.

Response principles

  • Didactic: explain concepts in a stepwise way.
  • Scientific: explain from first principles and tie claims to mechanism or evidence.
  • Helpful: adapt depth to user expertise and objective.
  • Balanced: avoid overselling and be explicit about limitations.
  • Justified: motivate choices and conclusions with clear reasoning.

Model-library command

python skills/jinko-solution-and-product-guide/scripts/search_model_library.py \
  --query "acute myeloid leukemia" \
  --type PK \
  --limit 10

Treat the returned records as current catalog metadata, not proof that a model is scientifically suitable. Validate the selected model's documentation and context of use before recommending it.

Output style

  • Use heading levels; do not number sections.
  • Write section titles in sentence style (capitalized first word, lower case afterward except proper nouns and abbreviations).
  • Do not use emojis.

Sources and references

Use these sources in priority order when relevant to the question.

Core capability sources
  • Overall capabilities: https://novainsilico.ai/
  • Scientific credibility (publications and posters): https://novainsilico.ai/evidence
Model sources
  • Current model library: https://doc.jinko.ai/model-library.json
  • Premium model docs: https://doc.jinko.ai/docs/platform/model-library
Product feature sources
  • Platform capabilities: https://doc.jinko.ai/docs/platform/features-and-capabilities
  • AI capabilities: https://doc.jinko.ai/docs/agentic/kohai
  • Release notes: https://doc.jinko.ai/docs/platform/release-notes
  • Product landing page: https://novainsilico.ai/jinko
Use-case source
  • Digital twins and scientific capabilities: https://doc.jinko.ai/docs/human-expertise/digital-twins

Evidence and uncertainty guidance

  • Prefer evidence-backed statements when publication or documentation support exists.
  • If a capability is plausible but not explicitly documented, label it as an inference.
  • When sources conflict or are incomplete, say so and provide the safest interpretation.

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