3,071 件 ・ 関連度順
概要と使いどころ
Parses Kubernetes API server audit logs (JSON lines) to tespit etmeexec-into-pod, secret access, RBAC modifications, privileged pod creation, and anonymous API access. Builds threat Tespit rules from audit event patterns. Use investigating yaparken Kubernetes cluster compromise or building k8s-specific SIEM Tespit rules.
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。
Implementing AWS CloudTrail log analysis for security monitoring, threat Tespit, and forensic investigation using Athena, CloudWatch Logs Insights, and SIEM integration to identify unauthorized access, privilege escalation, and suspicious API activity.
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。
tespit etme (s) and responds to OAuth token theft and replay attacks in cloud environments, focusing on Microsoft Entra ID (Azure AD) token protection, conditional access policies, and sign-in anomaly Tespit. Covers access token theft, refresh token replay, Primary Refresh Token (PRT) abuse, and pass-the-cookie attacks. Activates for requests involving OAuth token theft Tespit, token replay prevention, Azure AD conditional access token protection, or cloud identity attack investigation.
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。
Navigate execution flow from an entry point, mapping call chains, external dependencies, and side effects.
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。
Contract risk analysis, obligation mapping, and clause review. Use when the user asks to review, analyze, or check a contract or legal document.
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。
Add GitHub permalinks with fixed SHA/commit references to research documentation containing code snippets. Use when writing technical docs with code examples, creating investigation/research docs that reference source files, user asks to "add permalinks" or "add GitHub links", documenting code research findings with file:line references, or creating audit trails needing permanent code references. Triggers on "add github links", "add permalinks", "link to source", "make code references permanent".
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。
Legal matter-strategy App-in-Skill (Busabase-backed) desk for new-case strategy, issue trees, evidence checklists, risk analysis, pleading outlines, and approval-gated drafting packs grounded in internal precedents. Use when the user invokes $kelly-legal-matter-strategy, mentions 案件策略, 办案辅助, 证据清单, 争议焦点, 文书生成辅助, litigation strategy, arbitration strategy, pleading outline, or wants a Busabase-backed desk where responsible lawyers review agent-prepared strategy before use.
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。
Product-fleet ops desk (Busabase App-in-Skill) for uptime/service health, SSL certificate expiry, domain renewal, API key rotation, cloud spend anomalies, and a review queue of agent-proposed action cards. Use when the user invokes $kelly-devops or /kelly-devops, or asks about uptime, service health checks, SSL certificate expiry, domain renewal, domain expiry, API key rotation, token rotation reminders, cloud spend anomaly, billing spikes, ops desk, incident review, or reviewing agent-proposed action cards (renew domain, rotate key, investigate spend, restart service, acknowledge incident).
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。
Investigate why a defect or insufficiency was introduced and accepted by tracing code, pull requests, task context, and accessible development sessions. Use for second-order learning after a fix, blameless root-cause analysis, or preventing a recurring class of engineering mistakes.
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。
Design, refine, implement, or review application UI and user journeys with minimal controls, clear actions, shared components, realistic prototypes, and polished behavior. Use for product screens, forms, onboarding, navigation, settings, and chat/task interfaces; not backend-only work or general graphic design.
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 and verify Mobile bottom navigation for Commerce and checkout with Bootstrap v5.3 using a working semantic implementation, responsive RTL/LTR behavior, theme tokens, test plan and design verification.
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 and verify Mega navigation menu for Documentation and knowledge with Bootstrap v5.3 using a working semantic implementation, responsive RTL/LTR behavior, theme tokens, test plan and design verification.
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 and verify Mega navigation menu for Marketing landing with Bootstrap v5.3 using a working semantic implementation, responsive RTL/LTR behavior, theme tokens, test plan and design verification.
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 and verify Mega navigation menu for Data-heavy application with Bootstrap v5.3 using a working semantic implementation, responsive RTL/LTR behavior, theme tokens, test plan and design verification.
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 and verify Mega navigation menu for Mobile-first interface with Bootstrap v5.3 using a working semantic implementation, responsive RTL/LTR behavior, theme tokens, test plan and design verification.
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 and verify Mega navigation menu for Commerce and checkout with Bootstrap v5.3 using a working semantic implementation, responsive RTL/LTR behavior, theme tokens, test plan and design verification.
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 and verify Mobile bottom navigation for Mobile-first interface with Bootstrap v5.3 using a working semantic implementation, responsive RTL/LTR behavior, theme tokens, test plan and design verification.
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 and verify Mobile bottom navigation for Data-heavy application with Bootstrap v5.3 using a working semantic implementation, responsive RTL/LTR behavior, theme tokens, test plan and design verification.
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 and verify Mobile bottom navigation for Documentation and knowledge with Bootstrap v5.3 using a working semantic implementation, responsive RTL/LTR behavior, theme tokens, test plan and design verification.
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 and verify Mobile bottom navigation for Marketing landing with Bootstrap v5.3 using a working semantic implementation, responsive RTL/LTR behavior, theme tokens, test plan and design verification.
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。
Detects and prevents code injection attacks targeting serverless functions (AWS Lambda, Azure Functions, Google Cloud Functions) through event source poisoning, malicious layer injection, runtime command execution, and IAM privilege escalation via function modification. The analyst combines static analysis of function code, CloudTrail event correlation, runtime behavior monitoring, and IAM policy auditing to identify injection vectors across the expanded serverless attack surface including API Gateway, S3, SQS, DynamoDB Streams, and CloudWatch event triggers. Activates for requests involving Lambda security assessment, serverless injection detection, function event poisoning analysis, or serverless privilege escalation investigation.
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。
Detects and analyzes malicious behavior in mobile applications through behavioral analysis, permission abuse detection, network traffic monitoring, and dynamic instrumentation. Use when analyzing suspicious mobile applications for data exfiltration, command-and-control communication, credential stealing, SMS interception, or other malware indicators. Activates for requests involving mobile malware analysis, app behavior monitoring, trojan detection, or suspicious app investigation.
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。
Detects and analyzes fileless malware that operates entirely in memory using PowerShell, WMI, .NET reflection, registry-resident payloads, and living-off-the-land binaries (LOLBins) without writing traditional executable files to disk. Activates for requests involving fileless threat detection, in-memory malware investigation, LOLBin abuse analysis, or WMI persistence examination.
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。
Detects and analyzes process injection techniques used by malware including classic DLL injection, process hollowing, APC injection, thread hijacking, and reflective loading. Uses memory forensics, API monitoring, and behavioral analysis to identify injection artifacts. Activates for requests involving process injection detection, code injection analysis, hollowed process investigation, or in-memory threat detection.
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。