709 件 ・ 関連度順
概要と使いどころ
Parse Windows PowerShell Script Block Logs (Event ID 4104) from EVTX files to detect obfuscated commands, encoded payloads, and living-off-the-land techniques. Uses python-evtx to extract and reconstruct multi-block scripts, applies entropy analysis and pattern matching for Base64-encoded commands, Invoke-Expression abuse, download cradles, and AMSI bypass attempts.
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。
Map advanced persistent threat (APT) group TTPs to the MITRE ATT&CK framework using the attackcti Python library to query STIX/TAXII data for group-technique associations, then generate ATT&CK Navigator layer files to visualize and compare defensive coverage against adversary profiles. Use when profiling an APT group's techniques, building Navigator coverage heatmaps, or assessing technique coverage gaps against a specific threat actor.
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。
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.
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。
Analyzes network traffic captures and flow data to identify adversary activity during security incidents, including command-and-control communications, lateral movement, data exfiltration, and exploitation attempts. Uses Wireshark, Zeek, and NetFlow analysis techniques. Activates for requests involving network traffic analysis, packet capture investigation, PCAP analysis, network forensics, C2 traffic detection, or exfiltration 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。
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.
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。
Master modern SQL with cloud-native databases, OLTP/OLAP optimization, and advanced query techniques. Expert in performance tuning, data modeling, and hybrid analytical systems.
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。
Expert guide on prompt engineering patterns, best practices, and optimization techniques. Use when user wants to improve prompts, learn prompting strategies, or debug agent behavior.
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。
Curated collection of high-quality prompts for various use cases. Includes role-based prompts, task-specific templates, and prompt refinement techniques. Use when user needs prompt templates, role-play prompts, or ready-to-use prompt examples for coding, writing, analysis, or creative tasks.
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。
Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability.
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。
Compress large language models using knowledge distillation from teacher to student models. Use when deploying smaller models with retained performance, transferring GPT-4 capabilities to open-source models, or reducing inference costs. Covers temperature scaling, soft targets, reverse KLD, logit distillation, and MiniLLM training strategies.
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。
Master guide for using Claude Code effectively. Includes configuration templates, prompting strategies "Thinking" keywords, debugging techniques, and best practices for interacting with the agent.
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。
Merge multiple fine-tuned models using mergekit to combine capabilities without retraining. Use when creating specialized models by blending domain-specific expertise (math + coding + chat), improving performance beyond single models, or experimenting rapidly with model variants. Covers SLERP, TIES-Merging, DARE, Task Arithmetic, linear merging, and production deployment strategies.
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。
Build immersive, cinematic 2.5D interactive websites using scroll storytelling, parallax depth, text animations, and premium scroll effects — no WebGL required. Use this skill for any web design task: landing pages, product sites, hero sections, scroll animations, parallax, sticky sections, section overlaps, floating products between sections, clip-path reveals, text that flies in from sides, words that light up on scroll, curtain drops, iris opens, card stacks, bleed typography, and any site that should feel cinematic or premium. Trigger on phrases like "make it feel alive", "Apple-style animation", "sections that overlap", "product rises between sections", "immersive", "scrollytelling", or any scroll-driven visual effect. Covers 45+ techniques across 8 categories. Always inspects, judges, and plans assets before coding. Use aggressively for ANY web design task.
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。
Guided concept ideation using professional studio techniques, player psychology, creative exploration.
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。
Expert guidance for proactive threat hunting in Google SecOps. Use when proactively hunting for threats, retroactively analyzing indicators of compromise (IoCs), performing prevalence searches across enterprise events, hunting for MITRE ATT&CK techniques, or detecting behavioral and statistical outliers using UDM queries. Don't use for incoming alert triage (use secops-triage), active incident response and timeline deep-dives on a known breach (use secops-investigate), or detection rule authoring (use secops-detection-engineering).
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。
Improve LifeOS from what the best practitioners are shipping around AI harnesses — Anthropic first (changelogs, docs, releases), then trusted creators, trending repos, and the system's own reflections — extracting concrete techniques and filtering them against verified current state so nothing already-done or rejected is re-recommended. USE WHEN upgrade, system upgrade, check Anthropic, new Claude features, algorithm upgrade, LifeOS upgrade, mine reflections.
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。
CSS expert for flexbox, grid, animations, responsive design, and modern layout techniques
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。
Compress large language models using knowledge distillation from teacher to student models. Use when deploying smaller models with retained performance, transferring GPT-4 capabilities to open-source models, or reducing inference costs. Covers temperature scaling, soft targets, reverse KLD, logit distillation, and MiniLLM training strategies.
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。
Merge multiple fine-tuned models using mergekit to combine capabilities without retraining. Use when creating specialized models by blending domain-specific expertise (math + coding + chat), improving performance beyond single models, or experimenting rapidly with model variants. Covers SLERP, TIES-Merging, DARE, Task Arithmetic, linear merging, and production deployment strategies.
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。
Writes, optimizes, and debugs C++ applications using modern C++20/23 features, template metaprogramming, and high-performance systems techniques. Use when building or refactoring C++ code requiring concepts, ranges, coroutines, SIMD optimization, or careful memory management — or when addressing performance bottlenecks, concurrency issues, and build system configuration with CMake.
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。
Anti-forensics and evidence destruction techniques for red team operators conducting authorized engagements. Covers log clearing on Windows (wevtutil, Clear-EventLog, ETW provider patching) and Linux (journal truncation, utmp/wtmp binary editing, syslog manipulation), timestamp manipulation via Timestomp and SetMACE to defeat timeline analysis, filesystem-level anti-forensics including NTFS Alternate Data Streams for payload hiding and secure deletion with sdelete/shred, memory artifact removal to counter live forensics, disk artifact manipulation targeting MFT entries and USN journal records, network forensics evasion through encrypted C2 channels and DNS-over-HTTPS tunneling, and anti-VM/sandbox detection to avoid dynamic analysis environments. Tools: Timestomp, wevtutil, sdelete, shred, MimiPenguin, Invoke-Phant0m. Aligns to MITRE ATT&CK T1070 (Indicator Removal), T1027 (Obfuscated Files or Information), T1497 (Virtualization/Sandbox Evasion). Each technique includes the forensic artifact it targets, the destruction or manipulation method, and the defender perspective so operators understand detection gaps they must account for.
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。
Shellcode development reference for offensive security engagements. Use when writing custom x86/x64 shellcode, implementing position-independent code (PIC), building shellcode loaders, evading AV/EDR detection, or converting PE files to shellcode. Covers null byte avoidance, API hashing, encoder/decoder patterns, staged vs stageless payloads, Windows PEB traversal, and cross-platform shellcode techniques.
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。