「severity」の検索結果

Azure AI Content Safety SDK for Python. Use for detecting harmful content in text and images with multi-severity classification.

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。

Scan container images for known vulnerabilities using Anchore Grype with SBOM-based matching and configurable severity thresholds.

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。

bu skill covers integrating Static Application Security Testing (SAST) tools—CodeQL and Semgrep—into GitHub Actions CI/CD pipelines. It addresses configuring automated code scanning on pull requests and pushes, tuning rules to reduce false positives, uploading SARIF results to GitHub Advanced Security, and establishing quality gates that block merges when high-severity vulnerabilities are Detected.

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。

bu skill teaches security teams how to Dağıt: and operationalize Amazon GuardDuty for continuous threat Tespit across AWS accounts and workloads. It covers enabling protection plans for S3, EKS, EC2 runtime monitoring, and Lambda, interpreting Bul:ing severity levels, and building automated response workflows using EventBridge and Lambda.

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。

Structured validation framework that scores product ideas. Use when evaluating problem severity, willingness-to-pay, or founder-market fit. For market intelligence, see market-research.

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。

This skill teaches security teams how to deploy and operationalize Amazon GuardDuty for continuous threat detection across AWS accounts and workloads. It covers enabling protection plans for S3, EKS, EC2 runtime monitoring, and Lambda, interpreting finding severity levels, and building automated response workflows using EventBridge and Lambda.

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。

Implement a vulnerability aging dashboard and SLA tracking system to measure remediation performance against severity-based timelines and drive accountability.

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 structured communication templates for malware incidents including stakeholder notifications, executive briefings, technical advisories, and regulatory disclosures with severity-based escalation procedures.

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。

Run the full implement-review-fix loop using implementer and reviewer personas. Supports effort-based multi-reviewer scaling (1-5 reviewers) with automatic specialization selection. Includes memory-based feedback loop that learns from past review patterns. Loops until all reviewers find 0 issues of any severity.

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。

Focused Signals scout for PostHog projects using logs. Watches for volume bursts, severity-distribution shifts, service silence, fresh message patterns, and trace-correlated bursts via the logs ingestion pipeline. Emits findings only when they clear the confidence bar; otherwise writes durable memory and closes out empty. Self-contained peer in the signals-scout-* fleet — no dependencies on other skills.

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。

Audit the health of a PostHog project's data warehouse — find every broken or degraded pipeline item across sources, sync schemas, materialized views, batch exports, and transformations. Use when the user asks "what's broken in my warehouse?", "give me a health check", "audit my data pipeline", "why are some dashboards stale?", or wants a one-shot triage summary before deciding where to spend time. Produces a prioritized report of issues grouped by severity and type, with recommended next steps.

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。

Statistical modeling — linear/logistic/ordinal/Poisson regression, ANOVA, Kruskal-Wallis, chi-square, Mann-Whitney, Cox survival, spline fits (R `ns()`), odds ratios, Cohen's d, F-statistic, p-value computation. Specializes in clinical-trial AE analysis (SDTM DM/AE), severity ordinal regression, and per-feature stat workflows.

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。

Automated WCAG 2.1 AA accessibility testing with axe-core and Lighthouse CI. Auto-detects frontend framework (React, Next.js, Vue, Angular, Svelte, Astro, Flutter, React Native), discovers all routes and interactive components, installs Playwright + axe-core for page-level scanning and jest-axe/vitest-axe for component-level testing. Generates tests for color contrast (4.5:1), alt text, form labels, ARIA attributes, heading order, landmark regions, focus visibility, keyboard navigation (tab order, focus traps, modal focus management, skip-to-content), screen reader compatibility (aria-live regions, error announcements, toast notifications), and Flutter Semantics validation (48dp touch targets, semanticLabel). Reports violations by severity (critical, serious, moderate, minor) with WCAG criterion references. Use when adding a11y testing, auditing accessibility compliance, fixing contrast issues, or validating keyboard and screen reader support.

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。

Plan and report authorized vulnerability assessments on owned systems. Prioritize evidence, severity, and remediations. Do not deliver exploit payloads.

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。

Iteratively detects and auto-fixes technical debt in a codebase using three quality criteria from debt-analysis references: conditional branch simplification, encapsulation, and separation of concerns. Applies fixes directly to code, runs existing tests to verify no regressions, re-analyzes, and loops until all high-severity issues are resolved or max iterations (5) reached. Use this skill when you want to automatically improve code quality with real code changes, reduce technical debt systematically, or clean up code to meet quality standards. Trigger on phrases like "fix technical debt", "clean up code quality", "refactor and fix issues", "improve code quality automatically", "apply debt fixes", "fix code problems", "eliminate debt".

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。

Run technical quality checks across accessibility, performance, theming, responsive design, and anti-patterns. Generates a scored report with P0-P3 severity ratings and actionable plan. Use when the user wants an accessibility check, performance audit, or technical quality review.

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。

Azure AI Content Safety SDK for Python. Use for detecting harmful content in text and images with multi-severity classification.

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。

Triage defects with independent severity and priority, reproducible evidence, preserved attempt history, and explicit resolution retesting.

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。

Adversarial triage of open review findings on a PR. Every finding (Codex, Claude bot, Gemini) is a hostile hypothesis to REFUTE — fix only what is proven real, reachable, in the PR's scope, and above the round's severity bar. The bar rises every review round. Everything else is declined or filed as an issue. Use for 'fix the PR findings', '/fix', '/fix 123', or before any fix commit in a review loop.

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。

Azure AI Content Safety SDK for Python. Use for detecting harmful content in text and images with multi-severity classification.

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。

Use this skill when the user asks about DataQ — FMCSA's data review system at dataqs.fmcsa.dot.gov — for disputing inspection violations, crash records, or other entries that appear in a carrier's CSA / SMS score. Covers Request for Data Review (RDR) process, success rates, common dispute grounds, what evidence to attach, timeline expectations, and how successful disputes reduce BSI (BASIC Severity Indicator) scores. Cite 49 CFR 392.7 and the FMCSA DataQs User Guide.

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。

Use this skill when the user asks about accident claims management — first-notice-of-loss procedures, working with insurance adjusters, claim severity classification, subrogation, claim closure timelines, and how to influence claim outcomes. Different from accident-investigation-playbook (which covers facts); this covers the insurance + claim side.

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。

Use this skill to understand how FMCSA calculates the Crash Indicator BASIC. Covers what crashes count, severity weighting, time weighting, and the preventability program.

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。

Use this skill when the user asks about FMCSA Compliance Safety Accountability (CSA) program scoring — the seven BASIC categories, BASIC Severity Indicator (BSI), peer percentiles, intervention thresholds, how violations age out, SMS Methodology, ISS (Inspection Selection System), what "alert" status means, Safety Measurement System mechanics, or DataQ disputes. Cite SMS Methodology v3.20.

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。