28sungmin/m4-add-skills/vitrine-api

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Use the vitrine display API for M4 research visualization, review cards, forms, study tracking, approvals, exports, and recovery of displayed results.

Install

npx vskill@latest install 28sungmin/m4-add-skills/vitrine-api

SKILL.md

Vitrine API

Vitrine is the live display and research journal used by M4 analyses. Use it to show DataFrames, plots, markdown findings, forms, and approval gates in a browser while keeping a persistent study trail.

When to Use This Skill

  • Display query or analysis results from M4 in the browser
  • Collect structured researcher input with forms
  • Ask for review or approval before continuing an analysis
  • Organize outputs by study and export a provenance trail
  • Recover a card, study context, selected rows, or a timed-out response

Quick Start

python
from vitrine import show, section, confirm, ask
show(df, title="Patient Demographics")
show(fig, title="Age Distribution", description="Distribution after exclusions.")
show("## Finding\nMortality was higher in the exposed group.")
show({"patients": 4238, "mortality": "23%"})
section("Outcome Analysis")
if confirm("Proceed with adjusted model?"):
    score = ask("Which severity score?", ["SOFA", "SAPS-II", "OASIS"])

show() starts the display server automatically. The CLI is available through the M4 project environment:

bash
uv run vitrine status
uv run vitrine start
uv run vitrine restart

show()

python
show(obj, title=None, description=None, *, study=None, source=None,
     replace=None, position=None, wait=False, prompt=None, timeout=600,
     actions=None, controls=None)

Common parameters:

ParameterUse
objDataFrame, Plotly/matplotlib figure, markdown string, dict, or Form
titleCard title
descriptionShort explanation or interpretation
studyGroup cards into a named study
sourceProvenance such as dataset, table, script, or SQL summary
replaceExisting card id to update in place
wait=TrueBlock until the researcher responds
promptReview question shown with wait=True
actionsQuick action buttons
controlsForm fields attached to the card

Return values:

  • wait=False: a string-like card handle with .url
  • wait=True: a DisplayResponse with action, card_id, message, summary, and values

Forms

python
from vitrine import Form, Question, show
response = show(Form([
    Question("score", "Severity score?",
             options=[("SOFA", "Organ dysfunction"), ("SAPS-II", "Mortality prediction")]),
    Question("exclusions", "Exclusions?",
             options=["Readmissions", "Age < 18", "ICU LOS < 24h"],
             multiple=True),
]), wait=True, prompt="Confirm study parameters.", study="sepsis-v1")
score = response.values["score"]
exclusions = response.values["exclusions"]

Use multiple=True whenever several answers can be selected. Use allow_other=True when free-text additions are expected.

Study Management

python
from vitrine import (
    register_output_dir, list_studies, study_context,
    export, section, get_card, list_annotations,
)
STUDY = "early-vasopressors-sepsis-v1"
out = register_output_dir(study=STUDY)
section("Cohort Definition", study=STUDY)
context = study_context(STUDY)
export("output/study.html", format="html", study=STUDY)

Useful functions:

FunctionUse
register_output_dir(path=None, study=None)Create/register artifact directory
list_studies()List known studies
study_context(study)Re-orient to prior cards and decisions
section(title, study=None)Add a visual section divider
export(path, format="html", study=None)Export HTML or JSON
get_card(card_id)Fetch card metadata by id/prefix
list_annotations(study=None)Read researcher annotations

Interaction Patterns

Blocking Review
python
response = show(cohort_df, title="Cohort Preview", wait=True,
                prompt="Does this cohort look correct?", timeout=300,
                study=STUDY)
if response.action == "confirm":
    selected_rows = response.data()
elif response.action in {"skip", "timeout"}:
    raise RuntimeError("Researcher did not approve the cohort.")

Narrate the handoff in the terminal before using wait=True, so the researcher knows to respond in vitrine.

Progressive Updates
python
card_id = show(preliminary_df, title="Cohort (preliminary)", study=STUDY)
show(final_df, title="Cohort (final)", replace=card_id, study=STUDY)
Passive Selection and Recovery
python
from vitrine import get_selection, wait_for
subset = get_selection(card_id)
response = wait_for(card_id, timeout=600)

Plot Guidance

  • Use plots for distributions with many categories or continuous variables.
  • Always pass description= for plots, describing what the plot shows and why it matters.
  • Save Plotly figures as JSON from scripts, then reload with plotly.io.from_json() before calling show().

References

  • Vitrine Python package API, installed as the M4 dependency vitrine>=0.1.0.

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