374 件 ・ 関連度順
概要と使いどころ
Analyzes Windows Security, System, and Sysmon event logs in Splunk to detect authentication attacks, privilege escalation, persistence mechanisms, and lateral movement using SPL queries mapped to MITRE ATT&CK techniques. Use when SOC analysts need to investigate Windows-based threats, build detection queries, or perform forensic timeline analysis of Windows endpoints and domain controllers.
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Analyzes malicious VBA macros embedded in Microsoft Office documents (Word, Excel, PowerPoint) to identify download cradles, payload execution, persistence mechanisms, and anti-analysis techniques. Uses olevba, oledump, and VBA deobfuscation to extract the attack chain. Activates for requests involving Office macro analysis, VBA malware investigation, maldoc analysis, or document-based threat examination.
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Examine Linux system artifacts (auth logs, cron/systemd persistence, shell history, SSH keys, and system configuration) to uncover evidence of compromise, detect rootkits or backdoors, and reconstruct user/attacker activity. Use when investigating a compromised Linux server or workstation, hunting for persistence mechanisms, or scoping a Linux-based breach during incident response.
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Use Sysinternals Autoruns to systematically enumerate and analyze malware persistence mechanisms across Windows registry run keys, scheduled tasks, services, drivers, and startup locations. Use when hunting for persistence during Windows incident response, triaging a compromised endpoint, or validating that malware autostart entries have been fully identified and removed.
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Train Mixture of Experts (MoE) models using DeepSpeed or HuggingFace. Use when training large-scale models with limited compute (5× cost reduction vs dense models), implementing sparse architectures like Mixtral 8x7B or DeepSeek-V3, or scaling model capacity without proportional compute increase. Covers MoE architectures, routing mechanisms, load balancing, expert parallelism, and inference optimization.
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Investigate a GitHub issue or reported bug the way a seasoned maintainer would — form an independent model before reading the thread's theories, trace the mechanism and history, verify the diagnosis, and return a concise briefing with a verdict and fix direction. Use for triage, root-cause analysis, or re-investigation.
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Identify growth loops (flywheels) for sustainable traction. Evaluates 5 loop types: Viral, Usage, Collaboration, User-Generated, and Referral. Use when designing growth mechanisms, building product-led traction, or understanding how growth loops work.
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。
Poll AI-Trader heartbeat and notifications reliably through the primary pull-based mechanism.
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。
Evolutionary explanation-discovery engine for hard-to-vary causal theories of unknown phenomena. USE WHEN develop a new theory, explain a mystery, solve an explanatory gap, discover a mechanism, consciousness, origin, or scientific breakthrough. Also use for multi-cycle creative exploration. NOT FOR a single-pass divergent brainstorm (use BeCreative) or delivery validation alone (use Evals).
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Export raw OpenTelemetry traces from an AI application to Confident AI's Observatory. TRIGGER when the user wants to send OpenTelemetry or OTLP traces/spans from an LLM app, agent, RAG pipeline, or chatbot to Confident AI; configure the Confident AI OTLP endpoint; set confident.span.* or confident.trace.* attributes; export AI-app traces without the deepeval Python package; wire an OTLPSpanExporter, OpenTelemetry Collector, or vendor-neutral OTel SDK to Confident AI; or pick the US vs EU OTLP endpoint. Language-agnostic: the mechanism is OTLP attribute keys plus an exporter endpoint. DO NOT TRIGGER for building DeepEval pytest eval suites, datasets, goldens, metrics, or deepeval test run (use the `deepeval` skill); for instrumenting with the DeepEval SDK's @observe decorator or framework integrations (use the `deepeval-tracing` skill); or for non-AI software such as web servers, CRUD backends, or infrastructure: the confident.* attributes describe AI components only.
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This skill should be used when the harness, scaffold, workflow, or optimizer itself is the optimization target: recursive self-improvement (RSI) loops, meta-harnesses, self-improving harnesses that mine their own failures and propose bounded edits, evolutionary or population-based search over agent scaffolds, acceptance gates for self-modifying systems, and agentic context evolution where the mechanism that produces context is versioned and evolved. Route governance of a single autonomous loop (locked surfaces, durable logs, rollback, novelty gates, approval boundaries) to harness-engineering, measurement and quality-gate design to evaluation, judge design to advanced-evaluation, and remote sandbox infrastructure to hosted-agents.
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Build a tiny interactive HTML playground only when someone asks to see, play with, or step through a mechanism.
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Runs a route through ScenarioRunner's RouteScenario mode (--route / --route-id) with an autonomous agent driving the ego — the same mechanism the CARLA Leaderboard uses, and the only way to reach the ~34 route-only scenario classes (Accident, ParkedObstacle, HazardAtSideLane, InvadingTurn, HardBreakRoute, YieldToEmergencyVehicle, junction and actor-flow scenarios). Inspects route files, lists route ids and their scenarios, and runs one or all of them. Use when the user asks to "run a route", "drive a route with my agent", "run leaderboard scenarios without the leaderboard", or names a route-only scenario type.
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。
Train Mixture of Experts (MoE) models using DeepSpeed or HuggingFace. Use when training large-scale models with limited compute (5× cost reduction vs dense models), implementing sparse architectures like Mixtral 8x7B or DeepSeek-V3, or scaling model capacity without proportional compute increase. Covers MoE architectures, routing mechanisms, load balancing, expert parallelism, and inference optimization.
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。
Create and manage Claude Code skills following Anthropic best practices. Use when creating new skills, modifying skill-rules.json, understanding trigger patterns, working with hooks, debugging skill activation, or implementing progressive disclosure. Covers skill structure, YAML frontmatter, trigger types (keywords, intent patterns, file paths, content patterns), enforcement levels (block, suggest, warn), hook mechanisms (UserPromptSubmit, PreToolUse), session tracking, and the 500-line rule.
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Delegate web search tasks to shell_agent when direct search_web tool fails, using its retry mechanism and multi-step capabilities for resilient data gathering
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Trigger: root audit, atacar la raíz, issue roots, backlog roots, mechanism map, deletion-driven fix, resolver issues de raíz, close outdated issues. Audit and resolve issue clusters by verified root cause.
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Adapt this C/C++ ASAN vulnerability pipeline to a different vulnerability class, target shape, language, or detection mechanism. Use when the user wants to port, migrate, retarget, customize, or fork the pipeline for something other than C/C++ memory-safety bugs — web apps, smart contracts, deserialization, ML systems, or any other domain.
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Comprehensive Windows privilege escalation methodology for offensive security engagements. Covers the full attack surface from a standard user shell to NT AUTHORITY\SYSTEM: token impersonation via SeImpersonate and SeAssignPrimaryToken privileges using JuicyPotato, PrintSpoofer, GodPotato, SweetPotato, and RoguePotato; service misconfigurations including unquoted service paths, weak service DACLs, writable service binaries, and insecure service creation permissions; AlwaysInstallElevated MSI exploitation; DLL hijacking through search order abuse, phantom DLL loading, and writable PATH directory injection; UAC bypass techniques via fodhelper.exe, eventvwr.exe, CMSTP, and environment variable manipulation; scheduled task abuse for writable task actions and new task creation; registry autorun exploitation for persistence and escalation; PrintNightmare (CVE-2021-34527) for remote and local privilege escalation; and credential harvesting from SAM database extraction, DPAPI blob decryption, LSA secret dumping, and Credential Manager enumeration. Integrates automated enumeration with WinPEAS, PowerUp, SharpUp, Seatbelt, and BeRoot. Each technique includes detection signatures and defender-side visibility for purple team operations. Maps to MITRE ATT&CK T1548 (Abuse Elevation Control Mechanism) and T1574 (Hijack Execution Flow). Designed for authorized penetration testing, red team engagements, and CTF competitions where you hold a standard user shell and need to escalate to SYSTEM or local Administrator.
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。
Comprehensive persistence tradecraft for authorized red team engagements covering Windows and Linux mechanisms. Windows techniques include registry Run/RunOnce keys, scheduled tasks, WMI event subscriptions, DLL search order hijacking, COM object hijacking, Startup folder drops, service creation, Security Support Provider (SSP) DLL injection, and Active Directory persistence (AdminSDHolder abuse, DCShadow, Golden Ticket, Silver Ticket, Skeleton Key, SID History injection). Linux techniques include cron and at jobs, systemd timers and services, SSH authorized_keys injection, shell profile backdoors (.bashrc/.bash_profile), PAM module backdoors, LD_PRELOAD hijacking, kernel module rootkits, web shells, and Git hook abuse. Provides operator-ready command sequences for SharPersist, Impacket ticketer, schtasks, sc.exe, crontab, and systemctl with OPSEC considerations for each method. Maps to MITRE ATT&CK T1547 (Boot or Logon Autostart), T1053 (Scheduled Task/Job), T1546 (Event Triggered Execution), T1556 (Modify Authentication Process), and sub-techniques. Includes detection indicators and a rapid engagement cheatsheet.
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。
Comprehensive Linux privilege escalation methodology for offensive security engagements. Covers the full attack surface from a low-privilege shell to root: SUID/SGID binary abuse via GTFOBins, Linux capabilities exploitation (cap_setuid, cap_dac_override, cap_dac_read_search), sudo misconfigurations including NOPASSWD rules and Baron Samedit (CVE-2021-3156), cron job abuse through writable scripts, PATH hijacking, and wildcard injection with tar/rsync/chown. Includes writable /etc/passwd attacks, NFS no_root_squash exploitation, kernel exploits (DirtyPipe CVE-2022-0847, DirtyCow CVE-2016-5195, PwnKit CVE-2021-4034), Docker group container escapes, LD_PRELOAD and LD_LIBRARY_PATH hijacking for shared library injection, systemd service misconfigurations, and sensitive file enumeration for credential harvesting. Integrates automated enumeration with LinPEAS, linux-exploit-suggester, pspy for process monitoring, and GTFOBins for binary exploitation. Each technique includes detection signatures and defender-side visibility to support purple team operations. Maps to MITRE ATT&CK T1548 (Abuse Elevation Control Mechanism) and related sub-techniques. Designed for authorized penetration testing, red team engagements, and CTF competitions where you hold a low-privilege shell and need to escalate to root.
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Submit compact ChEMBL API requests for activity, molecule, target, mechanism, and text-search endpoints. Use when a user wants concise ChEMBL summaries
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This skill covers game-theoretic methods in structural econometrics and industrial organization. Use when the user is working with strategic interactions, equilibrium analysis, or game-theoretic structural models — including entry games, conduct testing, auction models with strategic bidding, bargaining, or matching markets. Triggers on "Nash equilibrium", "subgame perfect", "best response", "strategic interaction", "entry game", "conduct testing", "auction", "mechanism design", "matching market", "bargaining", "BNE", "Bayesian Nash", "static game", "dynamic game", "repeated game", "multiple equilibria", "equilibrium selection", "discrete game", "oligopoly", "game-theoretic", "player", "payoff", "strategy", "dominant strategy", "Bresnahan-Reiss", "Ciliberto-Tamer", "partial identification", "set identification", or markup test.
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。
Generate testable hypotheses. Formulate from observations, design experiments, explore competing explanations, develop predictions, propose mechanisms, for scientific inquiry across domains.
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。