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novainsilico

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• 22 skills
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• 1 day ago last updated

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   Jinko · novainsilico bundle

   Discover and route Jinkō QSP and mechanistic-modeling requests to the public Jinkō skill that owns the work. Use when the user is starting a Jinkō session, asks what capability or skill to use, describes a multi-area modeling request, or has not yet identified the relevant jinko-* or jinko-task-* skill. This skill does not make scientific decisions, plan workflows, execute SDK calls, or decide that a task step is complete.
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   Jinko Vpop · novainsilico bundle

   Create, generate, inspect, or work with Jinkō virtual populations (vpops) and vpop designs via the jinko-sdk. Use this skill whenever the user wants to upload a vpop from CSV or pandas DataFrame, create a vpop generator from marginal distributions, generate a vpop from a vpop design, inspect vpop content/statistics, or edit an existing vpop design. Vpops generated or uploaded as Vpop project items are not editable; edit the vpop design instead and regenerate.
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   Jinko Model · novainsilico bundle

   Build or edit a Jinkō computational model (QSP/PK-PD) via the jinko-sdk: parameters, categorical parameters, compartments, species, ODEs, reactions, dosing events, algebraic rules, baseline checks, solving options, units, and component tags. Use this skill whenever the user wants to create a model from scratch, create an empty model, edit an existing model, add or modify components, apply input/source/output tags, configure unit checking, define model-level dosing events, validate diagnostics, or debug model sanity or simple_solve errors. Prefer editing existing models over recreating them. Do not use this skill for running trials; use jinko-trial for trial execution.
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   Jinko Trial · novainsilico bundle

   Create, sanity-check, run, poll, and download results for Jinkō in-silico trials via the jinko-sdk. Use this skill whenever the user wants to set up a trial from a computational model and simple output set, optionally attach a vpop, protocol, data table, or advanced scoring output set, launch a trial, wait for completion, inspect completed trials, or download TimeSeries and Scalar results as pandas DataFrames. Do not use this skill for model editing, vpop creation, protocol design authoring, data-table upload, output-set creation/editing, or trial visualization.
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   Jinko Context · novainsilico bundle

   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.
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   Jinko Document · novainsilico bundle

   Create or update a Jinkō document from markdown through the jinko-sdk, including headings, tables, code blocks, links to Jinkō project items, uploaded images, and links to existing Jinkō References. Use this skill whenever the user wants to turn local markdown into a Jinkō document, refresh an existing document from edited markdown, prepare markdown so Jinkō renders cards and images correctly, or cite existing project References.
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   Jinko Protocol · novainsilico bundle

   Design or edit multi-arm Jinkō protocol designs via the jinko-sdk. Use this skill whenever the user wants to compare doses, schedules, administration routes, treatment activation flags, or combinations of treatments by overriding model component values per arm. Protocol designs assign values to model-defined inputs; dosing functions, schedule parameterization, treatment activation logic, and administration-mode logic belong in the model. Use jinko-model when those functions or inputs do not exist yet. Use jinko-trial for running trials.
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   Jinko Reference · novainsilico bundle

   Create, inspect, download, and organize Jinkō reference PDFs and their extracts through the jinko-sdk. Use this skill whenever the user wants to upload a paper or source PDF to a Jinkō project, retrieve a reference PDF already in the project so it can be read, create textual highlights from a quoted passage, create rectangular or formula extracts, inspect a paper's bibliography or existing extracts, or use a project reference while reproducing a publication. Do not use it for literature search, model authoring, or data-table creation.
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   Jinko Sdk Setup · novainsilico bundle

   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 JINKO_API_KEY and JINKO_PROJECT_ID 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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    Jinko Trial Viz · novainsilico bundle

    Create, update, inspect, sanity-check, and retrieve Jinkō TrialVisualization project items for completed or running trials. Use this skill whenever the user wants a trial visualization, trial viz, time-series plot setup, scalar result plots, scatter plots, contribution analysis, survival analysis, data overlays, or to fetch the current visualization JSON. The SDK exposes a typed TrialVisualization API: creation helpers plus a per-section subservice for every plot type.
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    Jinko Data Table · novainsilico bundle

    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.
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    Jinko Output Set · novainsilico bundle

    Create, inspect, validate, and incrementally edit Jinkō output sets via the jinko-sdk: simple output sets (measure designs) that list scalar measures derived from model outputs, and advanced output sets (scoring designs) that define constraints, scalars, and weighted objectives for scoring virtual populations. Validate scoring expressions and read diagnostics before attaching an output set elsewhere. Do not use this skill for attaching a simple or advanced output set to a trial and running it; use jinko-trial for that. Do not use this skill for data-table creation or fitness-function metadata; use jinko-data-table for that. Do not use this skill for calibration setup or CMA-ES options.
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    Jinko Task Cmaes · novainsilico bundle

    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.
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    Jinko Task From Nonmem · novainsilico bundle

    Convert a NONMEM run into a Jinkō trial set-up via the jinko-sdk. Use this skill whenever the user wants to import, translate, port or migrate a NONMEM model, control stream, .mod/.ctl/.lst file, .ext estimates, .phi individual estimates or a NONMEM data set into Jinkō, or asks to reproduce a published population-PK model that exists as NONMEM code. Emits the computational model, virtual population, protocol design and output set, checks the result numerically against a declared reference, and can publish the evidence as a Jinkō document. Use jinko-model to author a model from scratch, jinko-protocol to edit an existing protocol, jinko-vpop to edit a population, and jinko-calibration-cmaes to fit parameters.
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    Jinko Calibration Cmaes · novainsilico bundle

    Create, run, poll, and inspect results for Jinkō CMA-ES calibrations via the jinko-sdk: attach data tables and/or an advanced output set as fitness-function sources, set CMA-ES options and parameter priors, launch and monitor the run, and read performance/results payloads. Use whenever the user needs the SDK mechanics of building or driving a Calibration object. Do not use this skill for calibration business rules (defaults, diagnostics, deliverable rules). Do not use this skill for advanced output set / scoring design authoring — use jinko-output-set. Do not use this skill for data-table creation or validForFitnessFunction checks — use jinko-data-table. Do not use this skill for model or protocol authoring — use jinko-model / jinko-protocol. Do not use this skill for calibration-plan orchestration or iteration workflow.
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    Jinko Task Export To Matlab · novainsilico bundle

    Export a Jinkō computational model to SimBiology, apply its exported simulation settings in MATLAB, and compare selected time series with Jinkō simple_solve results. Use this skill whenever the user wants to transfer a Jinkō model to MATLAB/SimBiology, validate an Excel-based SimBiology export against Jinkō, reproduce a Jinkō simple solve in SimBiology, or investigate divergence between the two solvers. Use it even when the user gives only a model short ID or link and asks for a SimBiology comparison.
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    Jinko Task Literature Search · novainsilico bundle

    Find and shortlist biomedical publications from PubMed for knowledge, data, or reusable-model evidence. Use for query framing, PMID/DOI discovery, bibliographic normalization, evidence prioritization, and best-effort public full-text retrieval before synthesis, extraction, or modeling. Do not use for ClinicalTrials.gov-only scoping, systematic reviews, quantitative extraction, curve digitization, calibration, or model implementation.
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    Jinko Calibration Subsampling · novainsilico bundle

    Create, validate, run, inspect, reuse, and edit Jinkō virtual-population subsampling designs with the jinko-sdk. Use whenever a completed Trial's simulated patients must be filtered or selected to match population-level targets, then emitted as a matched Vpop. This is SDK mechanics only: do not use it to choose scientific targets, filters, or algorithm settings; do not use it to create or run the source Trial, author a Vpop, or orchestrate a calibration workflow.
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    Jinko Task Extract Data Table · novainsilico bundle

    Extract or digitize reported biomedical values from papers, figures, tables, supplements, images, or web sources into traceable CSV/Markdown, optionally as a calibration-ready Jinkō data table. Use when numeric evidence must be transcribed, normalized, unit-converted, or bound to model observables. Do not use for literature discovery, evidence synthesis, or inventing values absent from the source.
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    Jinko Task Trial Data Scoping · novainsilico bundle

    Find and shortlist ClinicalTrials.gov registry and posted-results records for biomedical modeling evidence. Use for NCT discovery, status/phase/results screening, endpoint and population inventory, comparator landscapes, and ongoing-trial intelligence. Do not use for PubMed publication discovery, quantitative extraction, protocol authoring, Jinkō trial execution, calibration, model building, or systematic reviews.
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    Jinko Solution And Product Guide · novainsilico bundle

    The jinko-solution-and-product-guide skill provides users with clear, concise information about solutions (services capabilities) and product features in jinko to solve the users scientific and modeling objectives. Use this skill whenever you need guidance on finding models in the library to help get a fast start or understand jinko features to accelerate your integrated modeling strategy.
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    Jinko Task Define Param To Calibrate · novainsilico bundle

    Classify directly valued Jinkō model inputs by evidence source and assign inputs needing calibration to explicit calibration steps. Use when the user wants to decide which parameters, categorical parameters, or species initial conditions should be calibrated and record the decision with `s::*` and `CalibIter::*` tags. Do not use for choosing datasets, estimating priors, drafting calibration plans, or running calibrations.
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