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novainsilico/jinko-skills97 installs

jinko-task-extract-data-table

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.

How do I install this agent skill?

npx skills add https://github.com/novainsilico/jinko-skills --skill jinko-task-extract-data-table
view source ↗

Is this agent skill safe to install?

  • Gen Agent Trust Hubpass

    The skill is a tool for extracting biomedical data from various formats into Jinkō data tables. It relies on vendor-specific SDKs and scripts from Nova In Silico. While it handles untrusted data from external sources, no malicious patterns were identified.

  • Socketpass

    No alerts

  • Snykwarn

    Risk: MEDIUM · 1 issue

What does this agent skill do?

Extract Data Table

PREREQUISITE: This skill needs an initialized jinko-sdk connection and an SDK satisfying its metadata.requires_sdk range. Run the jinko-sdk-setup skill (../jinko-sdk-setup/SKILL.md) and proceed only once its check passes. If that skill is not found, install it from novainsilico/jinko-skills.

Preserve what the source reports. Keep estimated, transformed, and directly transcribed values distinguishable.

Inputs

Require the source artifact and the requested series. For a Jinkō-ready output, also require the target obsId mapping, model units, and any scenario/arm scope. Ask for missing mappings rather than guessing them.

Workflow

  1. Locate each requested series and record its citation plus page, table, figure, panel, or supplement. Prefer machine-readable tables over OCR and OCR over graphical digitization. If no suitable extraction tool is available, request a tabular source instead of estimating visually.
  2. Extract only reported values. For graphical digitization, retain the raw digitized points and identify them as estimates. Do not fit, smooth, aggregate, or impute unless explicitly requested; record any such transformation.
  3. Preserve the reported statistic. Do not interchange raw values, means, medians, SD, SE, confidence intervals, IQR, or min/max. Convert units only when the source and target units are known, recording the formula and original values. Express Jinkō time values as ISO-8601 durations.
  4. For general extraction, emit a readable CSV or Markdown table with series, time/condition, value or bounds, unit, and source locator.
  5. For Jinkō output, follow the row schema owned by jinko-data-table. Use point rows for reported point values and range rows only for reported lower/upper bounds. Set obsId, armScope, unit, and experimentRef explicitly.
  6. Run the jinko-data-table creation script in dry-run mode with the expected --allowed-obs-id values, --require-unit, and --require-experiment-ref. On approval, add --require-fitness --apply. The script owns row/schema checks, upload, and the server validForFitnessFunction gate.

Return

Return the extracted file and a compact report containing:

  • source locator and extraction method for each series;
  • original statistic, units, and any transformations or conversions;
  • assumptions, unreadable values, and digitization uncertainty;
  • for Jinkō output, data-table SID, URL, observable mapping, and confirmed validForFitnessFunction: True.

If the server does not explicitly report fitness compatibility as true, return the table as not calibration-ready. Never silently replace or manufacture values to make a table pass validation.

Add the canonical catalog link to the repository README so users can inspect current installs and available audits. The publishing guide covers the complete discovery path.

<a href="https://skillzs.dev/skills/novainsilico/jinko-skills/jinko-task-extract-data-table">View jinko-task-extract-data-table on skillZs</a>