“exposure”的搜索结果

Use Nexscope (Search Intelligence Framework) data to analyze ASIN traffic source composition and exposure distribution, covering current period/previous period/newly entered/exited period comparisons. Trigger when the user mentions ASIN traffic sources, traffic structure analysis, organic traffic vs paid traffic share, exposure score breakdown, period comparison, new/exited traffic keywords, competitor traffic analysis, SP ad keyword count, brand ad exposure, Amazon's Choice exposure, editorial recommendation exposure, Top Rated exposure, video ad exposure, organic search exposure ratio, PPC traffic sources, promotional deal traffic sources, recommendation slot structure breakdown, ASIN traffic analysis, traffic sources, organic traffic share, ad traffic share, exposure analysis, traffic structure, period-over-period comparison, keyword churn, Nexscope. Even if the user does not explicitly mention "Nexscope", if their need involves analyzing an ASIN's traffic sources, exposure channel distribution, cross-period comparison, or competitor traffic structure comparison, this skill should also be triggered.

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Analyze drug concentration and response data to calculate exposure, fit models of drug movement and effects, and simulate dosing schedules. Produces exploratory research results with explicit assumptions and diagnostic checks.

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• Calculate exposure and half-life from concentration data
• Compare models of drug concentration over time
• Simulate dosing schedules for research

Report delta-adjusted notional exposure across all IBKR accounts. Calculates option deltas using Black-Scholes and reports long/short exposure by account and underlying. Use when user asks about delta exposure, portfolio risk, or directional exposure.

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为医疗应用发布前设置患者安全检查，验证临床决策支持、患者信息泄露、数据完整性、诊疗流程及外部集成，并在关键测试失败时阻止部署。

• 电子病历发布前验证
• 验证药物相互作用和剂量规则变更
• 权限变更后检查患者信息泄露

Audit Azure Blob and ADLS storage accounts for public exposure, risky access tokens, encryption settings, and insecure transport configurations. Use the azure-mgmt-storage Python SDK to produce a JSON report with account findings, severity ratings, and remediation recommendations.

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• Auditing storage accounts across an Azure subscription
• Investigating suspected exposure through public blob access
• Reviewing SAS token permissions and expiration

Retrieve security findings from Google Cloud Security Command Center for initial assessment. Filter active vulnerabilities, threats, and external exposures, inspect one finding’s full details, or group counts by category to understand the reported risks.

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• Retrieving full details for a named security finding
• Listing active vulnerabilities or external exposures
• Summarizing active finding counts by category

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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Graded exposure for anxiety disorders: Fear hierarchy, SUDs scale, exposure planning and guidance. Psychoeducation only, not implementation.

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Tests APIs for excessive data exposure where endpoints return more data than the client application needs, relying on the frontend to filter sensitive fields. The tester intercepts API responses and analyzes them for leaked PII, internal identifiers, debug information, or sensitive business data that the UI does not display but the API transmits. This maps to OWASP API3:2023 Broken Object Property Level Authorization. Activates for requests involving API data leakage testing, excessive data exposure, response filtering bypass, or API over-fetching.

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检查运行OpenClaw的设备或服务器的安全状况，核查SSH、防火墙、更新、备份、磁盘加密及网关认证，并提出按优先级分阶段实施的改进计划。

• 检查OpenClaw运行环境的安全状况
• 核查服务器对外开放情况
• 检查备份与磁盘加密状态

帮助医疗应用分类患者、员工及财务敏感信息，设计按医疗机构划分的行级访问控制和审计记录，并检查日志、网址及浏览器存储中的信息泄露。

• 检查处理患者记录的代码
• 设计医疗机构间的访问控制
• 记录敏感信息的查看与修改

Investigate Kubernetes permissions and cluster behavior in CTF sandboxes. Trace role-based access control, service accounts, and automated controllers to explain workload changes, secret exposure, and differences between configuration files and live objects.

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• Checking service account API permissions
• Tracing secret exposure through workload changes
• Comparing YAML manifests with live cluster objects

Assess database security within an authorized scope by reviewing network exposure, authentication, access permissions, risky features, audit logs, and backup access. Distinguish configuration weaknesses from issues that can be demonstrated through safe validation.

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• Reviewing database exposure and TLS settings
• Checking account privileges and sensitive table access
• Assessing dangerous features and configuration weaknesses

Assess potential data breach impact by reviewing sensitive data and its movement through a codebase. Produce an inventory, exposure rankings, financial estimates, and prioritized safeguards, distinguishing legal source figures from planning estimates.

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• Inventory sensitive data before a security review
• Trace where customer data could leak
• Estimate breach impact and costs for planning

Scan code, configuration files, and Git history for exposed AWS credentials with TruffleHog. Checks whether detected keys remain active and guides key replacement, incident investigation, and automated checks before commits and during CI/CD.

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• Auditing Git history for exposed AWS keys
• Adding credential checks before commits and in CI/CD
• Investigating credential exposure after a GuardDuty alert

Audit Python SDK logs, exceptions, and telemetry for sensitive data exposure. Trace values to their sources, fix confirmed leaks, and test that redaction hides sensitive information while preserving the caller's expected behavior.

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• Audit logs and exceptions for sensitive data exposure
• Fix leaks involving model or tool data
• Test redaction and caller behavior

Review startup contracts and investment terms to identify risky clauses, prepare negotiation proposals, and organize questions for qualified attorneys. It also covers intellectual property ownership and regulatory exposure, helping founders prepare for legal review.

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• Checking contract risks before signing
• Reviewing investment terms and founder dilution
• Auditing intellectual property ownership and open source licenses

Assess security risks in AI systems by scanning prompts for known attack patterns and scoring data exposure, poisoning, and tool abuse risks. Map findings to MITRE ATLAS, an AI threat framework, and identify protective controls.

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• Scan application prompts for injection and jailbreak patterns
• Review malicious instructions in retrieved documents
• Assess training data exposure and poisoning risks

Structure open-source intelligence research, meaning investigation using publicly available information, for authorized external assessments. Map organizational assets, record evidence and confidence levels, prioritize exposures, and prepare reports that explain technical findings and business risks.

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• Mapping an organization's externally exposed assets
• Recording evidence sources and confidence levels
• Prioritizing exposures within a limited research budget

Generates complete two-sample Mendelian randomization research designs from a user-provided outcome, exposure or exposure family, and robustness direction. Use when a study centers on summary-statistics causal inference with instrument selection, harmonization, IVW-primary estimation, complementary estimators, sensitivity analyses, optional multivariable upgrades, and conservative evidence interpretation. Covers five study patterns and always outputs Lite / Standard / Advanced / Publication+ with a recommended primary plan, stepwise workflow, figure plan, validation hierarchy, minimal executable version, publication upgrade path, and strictly verified literature retrieval.

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Calculates potential future exposure and CVA with simulation-based approaches and netting agreement analysis. Use when modeling counterparty exposure, calculating CVA/DVA, or assessing counterparty risk.

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Evaluates unfunded commitment obligations with call probability, pacing models, and portfolio-level exposure management. Use when analyzing unfunded exposure, managing commitment pacing, or stress testing capital calls.

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Evaluates geopolitical risk impact on investment portfolios with sanctions exposure, conflict risk, and supply chain disruption analysis. Use when analyzing geopolitical risk, assessing sanctions impact, or evaluating political event exposure.

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Analyze catastrophe modeling systems for natural disaster exposure, PML estimation, and reinsurance optimization. Use when: 'assess cat model', 'evaluate disaster exposure', 'review PML calculations', 'audit reinsurance program', 'check exposure data quality', 'analyze hurricane/earthquake risk models', 'evaluate Oasis or RMS setup'.

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