「severity」の検索結果

[omh] Wants Jev risk flags on a diff under review: Jev review flags for a diff: auth, tests, migration risk, severity; flags only, never approves a merge. Use when the user says: jev-review-gate, jev review gate, ask jev to review this diff.

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[omh] Production is down or an incident is open: command an incident that is still open -- severity as declared live state, commander and roles, an append-only timeline, a recorded temporary mitigation, verified recovery, and the customer notice. Use when the user says: live-incident-response, live incident response, incident response, active incident, ongoing incident, open incident, incident open, incident commander.

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Azure AI Content Safety SDK for Python. Use for detecting harmful content in text and images with multi-severity classification. Triggers: "azure-ai-contentsafety", "ContentSafetyClient", "content moderation", "harmful content", "text analysis", "image analysis".

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Analyzes documents and processes against FINRA, SEC, Federal Reserve, and CFPB regulatory frameworks. Identifies compliance gaps, classifies findings by severity, and recommends remediation. Use when performing compliance audits, regulatory reviews, gap analyses, or verifying policy adherence to financial regulations.

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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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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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Run an expert review against Nielsen's heuristics and domain criteria, with severity ratings. Use when you need findings without recruiting participants. For a facilitated team feedback session, use `design-critique` (design-ops).

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Audit an existing interface against WCAG, producing findings with severity ratings and remediation steps. Use when you have a design or build to assess now. Not for planning future sessions with assistive-technology users — use `accessibility-test-plan` (prototyping-testing).

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Runs and interprets AWS Resilience Hub v2 failure mode assessments. Covers starting assessments, understanding findings (severity, categories, recommendations), triaging by achievability, working with AI-generated service functions, and resolving findings. Applies when the user wants to run an assessment, review findings, or understand failure modes, or has a specific finding and asks how to resolve, remediate, or fix it. Does not apply to initial setup (use resilience-hub-getting-started) or FIS experiments.

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AWS security service suitability, coverage and cost recommendations: WAF/origin overlap, AWS Network Firewall inspection coverage and policy review, VPC endpoint, Transit Gateway/Cloud WAN segmentation and DNS Firewall security-control suitability, certificate renewal ownership and monitoring, incident response triage ownership, centralized security log store (CloudWatch or Security Lake). Also factual configuration, findings, data sources and severity scoring for GuardDuty, Inspector, Security Hub V2/OCSF (Exposure, connectors, automation), Security Hub CSPM/ASFF (standards, controls), Macie, Detective, Security Lake, AWS Security Incident Response membership and case coverage, and Organizations security policies. Applies to advisory security-service choices even when one service is named. Implementation, rule or code authoring, deployment, scans and troubleshooting outside these factual procedures belong to specialist skills. Supports local AWS CLI and AWS MCP.

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FAIR-aligned exposure quantification: turns a pile of recon findings into a defensible 0-100 + A-F org risk score (Likelihood x Impact, three ownership-aware factors: exposure/threat/impact), an ownership + proof demotion cap so unproven or weakly-owned findings can't inflate the number, a $-denominated FAIR loss-magnitude estimate (IBM/Ponemon per-record cost bands, cross-source record dedup, threat-factor annualization), attack-path amplification (curated red-team chain catalog + generic graph-walk engine, with a kill-chain vs shared-fate honesty gate), and a board-ready one-pager deliverable (hero $ + letter grade + top-3 findings + top attack path + the ask). Extends osint-methodology's severity rubric and client deliverable templates with quantification. Passive analysis only -- operates on findings already collected, no target traffic, no API keys. Use when asked to score risk, quantify exposure, estimate breach cost, build a board report, translate technical findings to dollars, or explain why a grade or dollar figure came out the way it did.

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Comprehensive OSINT methodology for external red-team operations and authorized attack-surface assessments. Covers the 6-stage recon pipeline (seed → asset expansion → enrichment → exposure analysis → convergence → operator-armed active validation) with connector-resilience and stage-vs-gating discipline, asset-graph discipline, severity rubric, confidence upgrade workflows, time budgeting, identity-fabric mapping, breach×identity correlation with per-person identity dossiers, detectability tagging, detection-aware probing, WAF/CDN bypass, vulnerability prioritization, phishing infrastructure planning, bug bounty submission, and client deliverable templates. Use when planning or executing reconnaissance against authorized targets, mapping an organization's external attack surface, investigating a person/entity, or producing client deliverables.

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Organization-grade cloud and supply-chain attack-surface discovery: S3/GCS/Azure Blob bucket discovery via observed-name mining (CNAME/cert-SAN/Wayback) and bounded two-class permutation (6 prefixes x 15 suffixes on trusted tokens, bounded target-bound expansion on subdomain stems), existence (HEAD/GET) vs public-listing confirmation, object-key triage into 9 value tiers (database dumps, credentials, IaC state, kubeconfig, VCS dirs, config, archives, PII, logs), dangling-CNAME bucket-takeover detection, and the ownership-gated severity model that stops an unattributable public bucket from becoming a false CRITICAL; the fully offline AWS-account-ID recovery from a leaked AKIA/ASIA/AROA access key (base32 decode, runnable stdlib Python, canonical test vector, AWS-documentation-example-ID screening); dependency-confusion confirmation for npm/PyPI (internal-signal classifier -- private-registry binding vs org-namespace match -- paired with a read-only public-registry 404 check and the npm scope-claimability nuance the public search API misses); and passive cloud-native/container/Kubernetes/CI control-plane fingerprinting (Lambda URLs, API Gateway, Cloud Run, App Service, kubelet/etcd/K8s API/dashboard, Jenkins/GitLab/Argo CD) as an org-attribution and exposure surface. Passive/discovery only -- no exploitation, no credential submission, no active control-plane confirmation (a stage-6 validate_cloud active tier is described but out of scope). Use when enumerating a target's cloud storage footprint, recovering an AWS account ID from a leaked key, confirming a supply-chain dependency-confusion vector, or fingerprinting cloud-native/K8s/CI infrastructure for an authorized external recon engagement.

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Evaluate and improve interface usability using heuristic analysis. Use when the user mentions "usability audit", "users are confused", "form usability", "navigation problems", "Nielsen heuristics", "cognitive walkthrough", or "is this easy to use". Also trigger when reviewing a design for usability issues, improving form-completion rates, or evaluating information architecture and navigation. Covers Krug's laws, Nielsen's 10 heuristics, severity ratings, dark-pattern recognition, and accessibility. For visual design fixes, see refactoring-ui. For conversion-focused audits, see cro-methodology.

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Detect keyword cannibalization across blog posts by extracting primary keywords from titles and headings, clustering semantically similar targets, and flagging posts competing for the same search intent. Supports local-only mode (grep-based) and DataForSEO API mode (Page Intersection endpoint at ~$0.01/call). Outputs severity-scored report with merge or differentiate recommendations. Use when user says "cannibalization", "keyword overlap", "competing pages", "duplicate keywords", "cannibalize".

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Clicks through every flow of an app clone and tests it for bugs: a test plan generated from the recon flows with happy paths and edge cases, Playwright end-to-end tests where possible, a browser click-through where not, and bug reports in a fixed format with severity, steps and evidence. Use when the user says "test my clone", "find bugs", "QA this", "click through everything", "write e2e tests", "does it work", or after /replica-build or /replica-backend.

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Adversarial dual-model code review that runs two AI models in parallel, cross-references findings, and produces a consensus severity table with fix confidence ratings. Use when asked for a thorough code review, adversarial review, dual-model review, confidence-rated review, or 'Hanselman review'.

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Audits a website, app, onboarding flow, or design for usability, conversion, and product problems, tying every finding to a business outcome and a severity. Use this to review an interface, diagnose low conversion or activation, find where users drop off, get structured product feedback, or decide which UX fixes to ship first. For raising visual quality rather than finding problems, use `interface-craft`.

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Handles customer situations that have exceeded normal support — severity assessment, incident communication, executive escalation, and recovering a relationship after a failure. Use this when a customer issue is escalating or has gone to leadership, during a customer-affecting outage, when a major account is at risk, when a relationship needs repairing after a failure, or to design the escalation path itself.

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Classify XQUIC GitHub issues with an evidence-backed L1-L4 resolution-complexity label while keeping validity, priority, severity, and disposition separate and skipping uniquely labeled issues unless the user explicitly forces a recheck. Use when triaging one issue or a batch of issues, creating an issue-complexity backlog, or applying or correcting L1-L4 GitHub labels.

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Generate structured CSV security audit reports from vulnerability data with proper filtering and formatting. This skill covers CSV schema design for security reports, using Python csv.DictWriter, severity-based filtering, and field mapping from JSON to tabular format.

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Detect flood events by comparing water levels to thresholds. Use when determining if flooding occurred, counting flood days, aggregating instantaneous data to daily values, or classifying flood severity.

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Security vulnerability assessment identifying OWASP risks, injection vectors, authentication issues, and data exposure with severity classification.

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Clause-by-clause contract risk analysis with severity scoring, financial exposure estimates, and prioritized remediation guidance

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。