'tia' 검색 결과

Investigate Windows credential theft by correlating endpoint monitoring data, Sysmon events, and security logs. The skill provides detection queries and a reporting format to identify suspicious access and assess which credentials may be compromised.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating alerts about suspicious LSASS access
• Hunting for credential theft across Windows systems
• Assessing the scope of potentially compromised credentials

Assess Shadow Credentials account takeover during authorized Active Directory testing. Examine permissions that allow public key registration, validate authentication without a password reset, and document credential cleanup, detection opportunities, and access control changes needed for remediation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Assessing risky permissions identified in BloodHound
• Validating account takeover without resetting a password
• Generating directory change records during security exercises

Assess exposure of Windows secrets protected by DPAPI, the operating system’s data protection service, during authorized security tests. Guide examination and decryption of saved credentials and browser data using available user sessions, credentials, or backup keys.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Assessing saved credential exposure in authorized tests
• Examining collected DPAPI files offline
• Reviewing protected browser and RDP credentials

Investigate how credentials can be exposed in CI/CD, the systems that build and deploy software. Covers environment variables, logs, credential stores, cloud trust settings, and runner tokens, with methods to assess access scope and defensive controls.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Review secret exposure in build environments
• Search build logs and caches for leaked credentials
• Assess trust settings between pipelines and cloud services

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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

Implement Terraform provider configuration and authentication with the Plugin Framework: provider schema for credentials (Optional + Sensitive attributes), environment variable fallbacks, credential provider chains (static config, then environment variables, shared credentials file, and platform identity), unknown-value guards in Configure(), secret redaction, configure-time credential validation, and diagnostics that name every source tried. Use when implementing or reviewing a provider's Configure method or provider schema, adding authentication options (API keys, tokens, profiles, credentials files, assume-role), deciding how a provider should resolve credentials, debugging "no valid credential sources" or missing-credentials errors, or unit testing credential resolution.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when designing, reviewing, or troubleshooting Salesforce connected apps, External Client Apps, Named Credentials, External Credentials, and OAuth-based integration access. Triggers: 'connected app', 'OAuth flow', 'client credentials', 'JWT bearer', 'Named Credential', 'External Credential', 'integration user', 'IP restrictions', 'connectedApp-meta.xml', 'ExternalClientApplication', 'ExtlClntAppOauthSettings', 'isAdminApproved', 'refreshTokenPolicy', 'ipRelaxation', 'consumer key', 'consumer secret', 'permittedUsersPolicyType'. NOT for OAuth error codes you are already seeing — use admin/connected-app-troubleshooting. NOT for External Credential principal-type setup steps — use integration/named-credentials-setup.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Investigate compromised credentials across AWS, Azure, and GCP by correlating security alerts and audit logs. Examines unusual API activity and suspicious sign-ins, helps assess the affected scope, and guides containment actions such as disabling keys and revoking sessions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigate cloud API activity from unfamiliar locations
• Assess exposure after a credential leak notification
• Correlate suspicious sign-ins across cloud providers

Identifies essential genes from CRISPR-Cas9 fitness screens using BAGEL2 (Kim & Hart 2021 Genome Med), a Bayesian classifier scoring per-gene Bayes Factors via log-likelihood ratios over per-sgRNA fold changes, calibrated against CEGv2 core-essentials (Hart 2017 G3, ~684 genes) and NEGv1 non-essentials (Hart 2014, ~927 genes). Covers the fc + bf + pr workflow, the linear-extrapolation improvement over BAGEL1 truncation, multi-target off-target correction, tumor-suppressor sensitivity (BAGEL2 detects enrichment), and BF calibration (BF >6 ≈ 90% posterior per Hart 2017; ~5% FDR by BAGEL convention). Use when classifying essential vs non-essential genes, calibrating BAGEL2 thresholds against PR curves, identifying tumor suppressors alongside essentials, comparing BAGEL2 hits to MAGeCK / drugZ, or generating publication-quality essentiality calls.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Identifies essential genes from CRISPR-Cas9 fitness screens using BAGEL2 (Kim & Hart 2021 Genome Med), a Bayesian classifier scoring per-gene Bayes Factors via log-likelihood ratios over per-sgRNA fold changes, calibrated against CEGv2 core-essentials (Hart 2017 G3, ~684 genes) and NEGv1 non-essentials (Hart 2014, ~927 genes). Covers the fc + bf + pr workflow, the linear-extrapolation improvement over BAGEL1 truncation, multi-target off-target correction, tumor-suppressor sensitivity (BAGEL2 detects enrichment), and BF-to-FDR calibration (BF >6 ≈ FDR 0.05 from Hart 2017). Use when classifying essential vs non-essential genes, calibrating BAGEL2 thresholds against PR curves, identifying tumor suppressors alongside essentials, comparing BAGEL2 hits to MAGeCK / drugZ, or generating publication-quality essentiality calls.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Identifies essential genes from CRISPR-Cas9 fitness screens using BAGEL2 (Kim & Hart 2021 Genome Med), a Bayesian classifier scoring per-gene Bayes Factors via log-likelihood ratios over per-sgRNA fold changes, calibrated against CEGv2 core-essentials (Hart 2017 G3, ~684 genes) and NEGv1 non-essentials (Hart 2014, ~927 genes). Covers the fc + bf + pr workflow, the linear-extrapolation improvement over BAGEL1 truncation, multi-target off-target correction, tumor-suppressor sensitivity (BAGEL2 detects enrichment), and BF calibration (BF >6 ≈ 90% posterior per Hart 2017; ~5% FDR by BAGEL convention). Use when classifying essential vs non-essential genes, calibrating BAGEL2 thresholds against PR curves, identifying tumor suppressors alongside essentials, comparing BAGEL2 hits to MAGeCK / drugZ, or generating publication-quality essentiality calls.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Identifies essential genes from CRISPR-Cas9 fitness screens using BAGEL2 (Kim & Hart 2021 Genome Med), a Bayesian classifier scoring per-gene Bayes Factors via log-likelihood ratios over per-sgRNA fold changes, calibrated against CEGv2 core-essentials (Hart 2017 G3, ~684 genes) and NEGv1 non-essentials (Hart 2014, ~927 genes). Covers the fc + bf + pr workflow, the linear-extrapolation improvement over BAGEL1 truncation, multi-target off-target correction, tumor-suppressor sensitivity (BAGEL2 detects enrichment), and BF-to-FDR calibration (BF >6 ≈ FDR 0.05 from Hart 2017). Use when classifying essential vs non-essential genes, calibrating BAGEL2 thresholds against PR curves, identifying tumor suppressors alongside essentials, comparing BAGEL2 hits to MAGeCK / drugZ, or generating publication-quality essentiality calls.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Identifies essential genes from CRISPR-Cas9 fitness screens using BAGEL2 (Kim & Hart 2021 Genome Med), a Bayesian classifier scoring per-gene Bayes Factors via log-likelihood ratios over per-sgRNA fold changes, calibrated against CEGv2 core-essentials (Hart 2017 G3, ~684 genes) and NEGv1 non-essentials (Hart 2014, ~927 genes). Covers the fc + bf + pr workflow, the linear-extrapolation improvement over BAGEL1 truncation, multi-target off-target correction, tumor-suppressor sensitivity (BAGEL2 detects enrichment), and BF-to-FDR calibration (BF >6 ≈ FDR 0.05 from Hart 2017). Use when classifying essential vs non-essential genes, calibrating BAGEL2 thresholds against PR curves, identifying tumor suppressors alongside essentials, comparing BAGEL2 hits to MAGeCK / drugZ, or generating publication-quality essentiality calls.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Examine Windows credential material in a CTF sandbox, including LSASS memory and Kerberos tickets. Distinguish stored secrets from credentials that actually work, and document the evidence linking their source to successful authentication or resulting privileges.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Examining LSASS secrets and Windows logon sessions
• Checking whether recovered Kerberos tickets are usable
• Tracing DPAPI and SSP credential material

Manage which n8n instance an MCP session targets when working across production, staging, teams, or clients. Guides instance switching, checks before credential changes, and recovery from unexpected missing data or ambiguous credential writes.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Switching between production and staging
• Checking the target before credential changes
• Investigating unexpected missing data

Implement full-screen ads at natural transitions in mobile apps using the Google Mobile Ads SDK. Follow platform-specific guidance for Android, iOS, or Unity, covering ad loading, event handling, display, and implementation verification.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Add full-screen ads between app screens
• Implement interstitial ads on Android or iOS
• Integrate interstitial ads into a Unity app

Manage credentials for Git access and deployments in KubeSphere DevOps. Covers SSH keys, passwords, API tokens, and Kubernetes connection settings, including their use in Jenkins pipelines and troubleshooting synchronization failures.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Register credentials for private Git repositories
• Use deployment credentials in Jenkins pipelines
• Investigate credentials missing from Jenkins

Get documentation-based help with deepsec, an AI vulnerability scanner. Initialize or resume setup, configure scans and model credentials, and create detection rules or plugins using the documentation bundled with the installed version.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Initializing deepsec and resuming interrupted setup
• Configuring scans, models, and credentials
• Creating or customizing vulnerability detection rules

Review git and gh operations in gh-aw workflows to check credential availability for each repository. Identify missing token handoffs, risky expansions of limited checkouts, and error handling needed where credentials are intentionally unavailable.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing git operations after repository checkout
• Checking tokens for private cross-repository access
• Reviewing sparse and shallow checkout changes

Compare cell-type composition and spatial features across conditions in IMC/MIBI cohorts with the patient as the experimental unit, covering pseudoreplication, per-patient aggregation, mixed models, compositional (Dirichlet/scCODA) differential abundance, diffcyt, per-image-to-patient spatial differential testing (SpaceANOVA), batch covariates, and FDR. Use when testing whether a cell type or spatial niche differs between groups, avoiding cell-level pseudoreplication, choosing a differential-abundance method, or correctly powering an IMC cohort comparison.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Compare cell-type composition and spatial features across conditions in IMC/MIBI cohorts with the patient as the experimental unit, covering pseudoreplication, per-patient aggregation, mixed models, compositional (Dirichlet/scCODA) differential abundance, diffcyt, per-image-to-patient spatial differential testing (SpaceANOVA), batch covariates, and FDR. Use when testing whether a cell type or spatial niche differs between groups, avoiding cell-level pseudoreplication, choosing a differential-abundance method, or correctly powering an IMC cohort comparison.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Compare cell-type composition and spatial features across conditions in IMC/MIBI cohorts with the patient as the experimental unit, covering pseudoreplication, per-patient aggregation, mixed models, compositional (Dirichlet/scCODA) differential abundance, diffcyt, per-image-to-patient spatial differential testing (SpaceANOVA), batch covariates, and FDR. Use when testing whether a cell type or spatial niche differs between groups, avoiding cell-level pseudoreplication, choosing a differential-abundance method, or correctly powering an IMC cohort comparison.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Evaluate job offers by separating guaranteed pay, conditional bonuses, and estimated equity value. Compare terms, develop an evidence-based counteroffer, draft emails and call scripts, rehearse recruiter conversations, and prepare deadline extensions, acceptance, or decline messages.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Comparing pay and terms across job offers
• Drafting a counteroffer email
• Rehearsing a salary negotiation call

Trace how access to cloud metadata leads to issued credentials and accepted privileges in a CTF sandbox. Organize endpoints, identity details, and API evidence into a minimal, reproducible path showing where trust is granted.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating access to cloud metadata services
• Tracing instance or workload credentials
• Verifying privileges accepted through metadata credentials