novainsilico/jinko-skills

jinko-context

- Explain core Jinkō context, navigation, version management, and domain language for agents and users.

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Jinkō Context

Use this skill to explain Jinkō's shared mental model and vocabulary. Keep answers practical: the goal is to help a user or agent navigate Jinkō artifacts without confusing project organization, scientific content, and immutable versions.

Navigation and Version Management

Jinkō project navigation behaves much like working in a Git repository, but for scientific modeling artifacts instead of source-code files.

  • A Project is like the repository: it is the boundary for a concrete scientific modeling effort, including sources, data, models, protocols, trials, reports, and collaboration history.
  • Folders are like repository directories: they organize related project items by workflow, experiment, disease area, evidence package, or analysis thread. The folder API is an organizational surface; a folder is not a Project Item.
  • Project Items are like tracked files or domain objects in the repository: each item has an SID and represents a Jinkō artifact such as a model, protocol, trial, data table, source, output set, or document.
  • Snapshots are immutable states exposed by versioned Jinkō core-item APIs. The latest state is head. Do not assume every Project Item type exposes snapshots; use the resource's SDK/API surface to determine whether snapshot identity exists.

This Git-like structure makes Jinkō easier for agents to navigate because agents can separate three questions:

  • Where is the work organized? Look at the project and folders.
  • Which artifact is being discussed? Identify the project item.
  • Which exact state of a versioned artifact matters? Use its explicit version or snapshot surface, or use head when the user wants the latest state.

Use Project, Project Item, and Snapshot rather than generic words like workspace, file, upload, save, version, revision, or commit unless you are explicitly making the Git analogy.

Domain Language

Project: A concrete scientific modeling effort with its own sources, data, modeling artifacts, and reports. Avoid: workspace, case, study folder.

Folder: An organizational container inside a project used to group related project items. Folders support navigation and workflow hygiene; they do not replace item identity or snapshot identity. Avoid: Project Item or directory when speaking to end users unless making the Git analogy.

Project Item: A named Jinkō object inside a project, such as a source, model, protocol, trial, data table, output set, or document. Its SID is the preferred user-facing identity. Versioned types can retain that identity across versions. Avoid: file, asset, object when precision matters.

Snapshot: An immutable state of a Jinkō model, protocol, trial, or other resource whose core-item API exposes snapshots. It enables exact version access; it is not a universal property of folders or every Project Item type. Avoid: revision, save, version, commit except when explaining the analogy.

Source: An immutable scientific input, usually a paper PDF, used as evidence for extraction and modeling. Avoid: document, file, upload.

Extract: A structured piece of information derived from a source, such as a parameter value, clinical endpoint, experimental condition, mechanism statement, data series, or assumption. Extracts should stay linked to the source evidence that justifies them. Avoid: note, snippet, untracked fact.

Model: A Jinkō-compatible executable representation of the biological or pharmacological mechanism being modeled. Avoid: model file, script.

Protocol: The Jinkō artifact that defines trial arms, interventions, dosing or treatment conditions, and simulation timing. Avoid: study design, scenario file.

Trial: The Jinkō artifact that combines a protocol, model, output sets, and data tables for simulation or comparison. Avoid: run, simulation setup.

Calibration: The stage that adjusts model parameters so simulations fit selected experimental evidence. Avoid: fitting, tuning.

Calibration Candidate: A model-intrinsic parameter proposed for calibration. It may be marked with a toCalibrate model tag. Avoid: free parameter, tuned knob.

Virtual Population: A generated population of parameterized virtual subjects used for in-silico trial execution. Avoid: cohort, sample.

Reference: The trace from a model, dataset, or report assertion back to the source evidence or assumptions that justify it. Avoid: citation, provenance.

Modeling Context: The full scientific, protocol, trial, source, and artifact background needed to build or refine a Jinkō Model. Avoid: extraction catalog, model input bundle.

Relationships

  • A Project contains one or more Project Items.
  • A Folder organizes Project Items within a Project.
  • Some versioned Project Items expose Snapshots through their core-item APIs.
  • A Snapshot captures one immutable core-item state; consult the resource API rather than assuming one exists.
  • A Source supports one or more Extracts.
  • An Extract should retain a Reference to its Source evidence.
  • A Model represents executable biological or pharmacological mechanisms.
  • A Protocol defines arms, interventions, and timing used by a Trial.
  • A Trial uses a Protocol, a Model, output sets or measures, and optional transformed data overlays.
  • A Calibration uses selected evidence and outputs to adjust Calibration Candidates.
  • A Virtual Population provides parameterized virtual subjects for Trial execution.
  • A Reference links Project Items, Model components, Trial outputs, data tables, and reports back to Sources or explicit assumptions.
  • A Modeling Context gathers the relevant Sources, Extracts, References, Models, Protocols, Trials, and reports needed to make a modeling decision.

Response Guidance

  • Translate user language into Jinkō language when it improves precision.
  • Preserve the Git analogy for orientation, but do not imply Jinkō is literally Git.
  • Distinguish stable SID identity from immutable snapshot identity where that resource exposes snapshots.
  • Use head/latest only when the user wants the current state; name snapshots when exact reproducibility matters.
  • Route implementation tasks to the relevant skill: jinko-model, jinko-protocol, jinko-trial, jinko-vpop, jinko-data-table, or jinko-sdk-setup.
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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.

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GitHub Stars
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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.

Installationen
1
GitHub Stars
1
Aktualisiert
7. Sept.