Test ML models with Giskard's scan() vulnerability detector + test catalog (performance, robustness, fairness, data leakage, ethical issues) for tabular and NLP models. Wrap a prediction function in giskard.Model + a DataFrame in giskard.Dataset; emit test suites that pass/fail in CI. Use when a trained tabular or NLP model is about to ship with no test suite of its own, or when a feature-engineering or hyperparameter change needs a pre-merge scan for newly introduced vulnerabilities.
日本語の概要は準備中です。原文の説明を表示しています。
実行スコア 未検証
インストール方法を見るインストールする前に、エージェントに与えられる指示の中身を確認できます。
Giskard wraps any prediction function and DataFrame, then runs a
scan() that surfaces "performance biases, unrobustness, data
leakage, stochasticity, underconfidence, ethical issues" per the
Giskard tabular quickstart.
pip install giskard --upgrade
Per the Giskard tabular quickstart.
from giskard import Dataset
giskard_dataset = Dataset(
df=raw_data,
target=TARGET_COLUMN,
name="Titanic dataset",
cat_columns=CATEGORICAL_COLUMNS,
)
cat_columns matters - Giskard treats categoricals differently for
slicing + drift detection.
from giskard import Model
import numpy as np
import pandas as pd
def prediction_function(df: pd.DataFrame) -> np.ndarray:
preprocessed_df = preprocessing_function(df)
return classifier.predict_proba(preprocessed_df)
giskard_model = Model(
model=prediction_function,
model_type="classification",
name="Titanic model",
classification_labels=classifier.classes_,
feature_names=FEATURE_NAMES,
)
The prediction_function returns probabilities (not class labels)
for classification - required by Giskard's calibration checks.
from giskard import scan
results = scan(giskard_model, giskard_dataset)
results.to_html("scan_report.html")
Per the Giskard tabular quickstart, scan covers categories: performance bias, unrobustness, data leakage, stochasticity, underconfidence, ethical issues. HTML report is artifact-friendly for CI.
test_suite = results.generate_test_suite("My first test suite")
suite_results = test_suite.run()
if not suite_results.passed:
raise SystemExit("Giskard test suite failed; see report")
from giskard import testing
test_suite.add_test(
testing.test_f1(
model=giskard_model,
dataset=giskard_dataset,
threshold=0.7,
)
)
# Slicing test: F1 must hold on a subset
female_slice = giskard_dataset.slice(lambda df: df[df.sex == "female"])
test_suite.add_test(
testing.test_f1(
model=giskard_model,
dataset=female_slice,
threshold=0.65,
)
)
test_suite.run()
Catalog includes test_f1, test_accuracy, test_recall,
test_drift_*, metamorphic transformations. Reference the
Giskard tabular quickstart for the current full list.
- name: Giskard scan
run: |
python ml/giskard_scan.py
# Script raises SystemExit on failure
- name: Upload Giskard report
uses: actions/upload-artifact@v4
with:
name: giskard-report
path: scan_report.html
| Anti-pattern | Why it fails | Fix |
|---|---|---|
Skip cat_columns parameter | Categorical features treated as numeric; bogus drift | Always pass cat_columns (Step 2) |
Wrap a predict() (classes) instead of predict_proba() (probs) | Calibration tests cannot run | Use predict_proba for classification (Step 3) |
| Run scan once, don't add to suite | One-off finding never re-checked | Generate suite from scan (Step 5); CI gates re-run |
| Block CI on every minor scan finding | Noise; team disables Giskard | Set per-test threshold; gate on critical+major only |
| Reuse training dataset for scan | False sense of robustness; scan needs unseen data | Use held-out test split |
scan() is non-deterministic with stochastic models; pin
random_state in the model and Giskard config for reproducible CI.まだレビューはありません。使ってみた感想をお寄せください。
ログインなしで書き込めます。使ってみた感想、質問、動かなかった環境などを気軽にどうぞ。
まだコメントはありません。最初のコメントを書いてみませんか。
Builds a CI gate that fails the build on **new** WCAG / a11y violations introduced by a PR while grandfathering pre-existing violations on a per-rule / per-page baseline. Aggregates verdicts from axe-core / pa11y / Lighthouse a11y / WAVE / IBM Equal Access scans. Use when a project has accumulated a11y debt and a strict "zero violations" gate would block every PR - the ratchet pattern lets the team ship while preventing regressions.
日本語の概要は準備中です。原文の説明を表示しています。
Workflow skill that builds an A/B-test validity checklist from an experiment proposal, walking the canonical design-correctness gates - pre-registered OEC/power/guardrails, randomization unit + SRM check, assignment integrity, telemetry, peeking discipline, novelty/primacy, post-experiment SRM re-check - into a per-experiment checklist + sign-off form. Use when launching, auditing, or governing an experiment. For pitfall mechanics (guardrails, peeking) see experiment-results-interpreter's references; to read an already-valid result use experiment-results-interpreter; for per-SDK harness tests use experiment-sdk-testing - this gates DESIGN, not SDK code.
日本語の概要は準備中です。原文の説明を表示しています。
The single install-flow skill: the reference contract for the PWA install flow (installability gate fields, `beforeinstallprompt` handshake, per-platform paths - Android WebAPK / iOS Share menu / Firefox no-op - and the `display-mode` post-install signal, in references/install-flow-reference.md) plus the build-an-X workflow that emits the Add-to-Home-Screen suite. Walks the four-stage timeline, emitting one test per gate cell per [web.dev/articles/install-criteria][install-criteria], the deferred-prompt → `prompt()` → `userChoice` chain per [web.dev/articles/customize-install][customize-install], the iOS Safari manual-metadata branch (`apple-touch-icon`) per [web.dev/learn/pwa/installation][learn-pwa], and the post-install `(display-mode: standalone)` MQ assertion. Output: a Playwright spec with per-stage cells plus a coverage matrix. Use when a PWA's manifest, icons, or install handler change, when install conversion drops at an unknown stage, or when triaging a flaky install assertion.
日本語の概要は準備中です。原文の説明を表示しています。
Build-an-X workflow that uses an LLM to map existing tests to spec sections - given a spec doc + the test suite, the LLM identifies which tests cover which sections, surfaces uncovered sections (gap), and recommends specific tests to add. Output is a coverage matrix per spec ID. Scope is mapping tests that already exist and naming the gaps, not authoring tests for new acceptance criteria. Use when a spec doc and a test suite both exist but nobody can say which requirements are actually covered - before a release sign-off, an audit, or a decision about where to spend the next round of test effort.
日本語の概要は準備中です。原文の説明を表示しています。
Generates tests from natural-language specs (acceptance criteria, user stories, requirements) using an LLM, with confidence scoring per test case (LLM self-assessment plus heuristics: assertion quality, naming, completeness), batching uncertain cases for human review, and integration with the team's existing test framework. Use when the user asks to generate unit tests from acceptance criteria, convert user stories to test cases, automate test creation from requirements, or augment a spec-driven test suite with AI-generated stubs that are then curated before merge.
日本語の概要は準備中です。原文の説明を表示しています。
Configures Allure Report (test-runner adapter install, `allure-results` directory wiring, `categories.json` for failure classification, `history-trend.json` retention via the copy-history-between-runs pattern), runs the Allure CLI to convert `allure-results` to a static HTML site, and uploads the report as a CI artifact. Use when the team needs richer test reporting than JUnit XML - step-level attachments, per-test history, retry tracking, and severity / epic / feature labeling across framework-agnostic adapters (pytest, Jest, JUnit, TestNG, NUnit, Mocha). As a rich static HTML report generator, it is the open-source alternative to the sunset ExtentReports (JVM/.NET per-test HTML narrative); for hosted cross-run flakiness analytics rather than a static per-run report use currents-integration.
日本語の概要は準備中です。原文の説明を表示しています。