'severity' 검색 결과

Audit a dataset or access rule before it joins an identity-graph build (access rules behave like datasets in NQL here). Enumerates failure modes (hub identifiers, high-degree nodes, suspicious values, over-connected identifiers), tests hypotheses in parallel, quantifies damage by rows / edges / entities, and proposes minimal filters ranked by severity. When issues are found, returns a validated `CREATE MATERIALIZED VIEW` NQL the caller can run to produce a graph-ready clean source; if the data passes, says so and recommends it unchanged. Plans and authors the clean-view NQL; does not execute it. Use when: "audit this dataset before the graph build", "find bad edges in <source>", "check identity data quality", "recommend filters for the graph build", "quantify damage from <identifier_type>", "pre-graph DQ". (narrative-identity)

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Knowledge base from FAA-HDBK-006D (2020), the FAA's System Reliability, Maintainability, and Availability (RMA) Handbook for National Airspace System (NAS) hardware and software. Use for RMA figures of merit (MTBF, MTBO, MTTR, the availability variants), the bathtub curve and probability distributions, the four-stage RMA lifecycle mapped to the FAA Acquisition Management System (AMS), the six-task RMA technical-management/acquisition process, service-thread criticality and the NAS-RD severity-to-target table, the RMA analysis toolbox (RBD, FMEA/FMECA, FTA, Ishikawa, Monte Carlo, Bayesian, FRACAS, ALT, reliability-growth/recovery tests), software reliability (early prediction + reliability growth), and tailorable example RMA requirements for a Program Requirements Document. Scope limits: it is FAA/NAS-specific guidance (not a requirement, by its own statement), centred on hardware-plus-software availability — it brackets out human/service/facility/communications availability, names but does not reproduce the math derivations or external standards (MIL-STDs, IEEE 1633, RTCA DO-178C/DO-278A, SAE JA1011), is thin on detailed worked numerical examples and on safety-case method, and predates any current revision beyond 006D.

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Knowledge base from the FAA System Safety Handbook (Dec 30, 2000). Use for system safety as a specialty within systems engineering: the five-step safety risk management process (mandated by FAA Order 8040.4 and the AMS), the severity-by-likelihood risk matrix and acceptance bands, the Safety Order of Precedence, system-description models (5M, SHEL(L)), the hazard-analysis activities (PHL/PHA/RHA/SSHA/SHA/O&SHA/HHA), integrated system hazard analysis, the analysis techniques (FTA, FMEA/FMECA, Fault Hazard, Common Cause, Sneak Circuit, Energy Trace), closed-loop hazard tracking (Safety Action Record), and the specialty domains the handbook covers — acquisition-lifecycle safety and contracting (OSA/CSA/SSPP/ISSPP), software safety (control-capability ranking, SHCM, DO-178B), test & evaluation safety, facilities safety, commercial launch safety (AST licensing, expected casualty, MPL/DAMP), system safety training, and Operational Risk Management (ORM) with human/organizational factors. Scope limits: this is a year-2000 FAA how-to; it predates the modern FAA/ICAO Safety Management System (SMS) and Just Culture regulatory frameworks, and many cited orders, advisory circulars, and launch-licensing rules have since been restructured. It is FAA/NAS-centric, leans on MIL-STD-882C, and does not define the SE life-cycle processes themselves (use nasa-npr-7123 / dau-se-guidebook) or reproduce third-party standard text (MIL-STD-882, DO-178B, ISO/IEC/IEEE).

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Knowledge base from MIL-STD-882E (DoD System Safety). Use for the system safety process, hazard analysis tasks (PHA/SSHA/SHA/O&SHA), the risk assessment matrix (severity x probability), software safety criticality, and risk acceptance. US-gov public domain.

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Use when the user asks to "triage launch feedback", "cluster reviews, comments, and board posts into themes", or "set up a you asked, we shipped loop"; produces a feedback theme digest (frequency, severity, representative quotes per theme), an open→planned→started→completed/declined status loop with duplicate-merge and notification rules, shipped-change announcement material, and a compliant social-proof harvest protocol (never incentivized store reviews). Not for repurposing or amplifying the harvested proof — use content-amplifier; not for executing testimonial outreach threads — use outreach-manager. 反馈分诊/状态环/社证收割/评测合规

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Use when the user asks to "build our social crisis protocol", "mentions are exploding — what do we do first", or "when do we pause the posting queue"; produces a 1-5 severity ladder with tunable Estimated trigger thresholds (mention-velocity multiples vs the 7-day listening baseline, sentiment flip, journalist/regulator contact, employee-conduct class), the first-mechanical-action rule — pause ALL scheduled posts AND paid amplification, with dated state markers dropped to the channels proposal protocol and reconciled post-incident — a pre-approved holding-statement library with committed update cadences, when-NOT-to-post rules, a spokesperson/approval matrix, and a post-crisis retro template; re-runs the social-quality-auditor pre-publish gate before un-pausing the queue. Not for email deliverability incidents (blocklist, spam-rate spikes) — use deliverability-qa; inside a launch window launch-day-conductor owns incident handling. 社媒危机预案/暂停队列/声明库/发言人矩阵

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Scans .NET code for ~50 performance anti-patterns across async, memory, strings, collections, LINQ, regex, serialization, and I/O with tiered severity classification. Use when analyzing .NET code for optimization opportunities, reviewing hot paths, or auditing allocation-heavy patterns.

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

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Use when working with Aws Guardduty — aWS GuardDuty finding analysis, detector management, suppression rule review, and member account oversight. Covers threat detection summary, finding severity distribution, finding type breakdown, trusted IP lists, and organization-wide security posture.

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Use when working with Tfsec — tfsec Terraform security scanning. Covers static analysis of Terraform code, custom rule creation, CI integration, severity filtering, module scanning, and remediation guidance. Use when scanning Terraform configurations for security issues, creating custom security rules, or integrating security checks into CI pipelines.

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Structured 3-phase peer review of a diff or PR — intent, issues by severity, then pointed merge-readiness questions and a verdict. Use for general code review. Not for AI workflow self-fix-and-edit (ai-systems-review) or no-edit devil's-advocate threat models (adversarial-challenge). Phase 3 asks questions only; it is not the adversarial-challenge skill.

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Critique a user interface (screenshot, running page, HTML/CSS, mockup or description) and return severity-ranked findings with evidence and a concrete fix for each. Use when the user asks for design feedback, a UI review, "what's wrong with this screen", or a pre-launch polish pass.

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Diff-scoped correctness review that reads the codebase around the change — callers, contracts, and invariants — and returns line-anchored findings ranked by severity with confidence, then a merge-risk verdict. Use for reviewing a PR, diff, or named change set. Not for style review, approvals-as-rubber-stamp, or editing the code.

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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.

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Multi-agent autonomous startup system for Claude Code. Triggers on "Loki Mode". Orchestrates 100+ specialized agents across engineering, QA, DevOps, security, data/ML, business operations, marketing, HR, and customer success. Takes PRD to fully deployed, revenue-generating product with zero human intervention. Features Task tool for subagent dispatch, parallel code review with 3 specialized reviewers, severity-based issue triage, distributed task queue with dead letter handling, automatic deployment to cloud providers, A/B testing, customer feedback loops, incident response, circuit breakers, and self-healing. Handles rate limits via distributed state checkpoints and auto-resume with exponential backoff. Requires --dangerously-skip-permissions flag.

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

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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.

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

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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.

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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.

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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.

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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.

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

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

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