データ分析、機械学習、LLMアプリ、プロンプト設計（8,728 件）
概要と使いどころ
Identify ransomware-related network indicators, including C2 beaconing patterns, TOR exit node connections, data exfiltration flows, and encryption key exchange, by analyzing Zeek conn.log and NetFlow data. Use when threat hunting for active ransomware network activity or investigating suspected pre-encryption exfiltration during incident response.
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。
Query a MISP (Malware Information Sharing Platform) instance via PyMISP to compute event statistics, IOC type breakdowns, threat actor galaxy clusters, and tag trends, and generate threat landscape reports with temporal trends. Use when asked to analyze threat intelligence data, summarize top threat actors or malware families, or produce a CTI landscape report from MISP events.
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。
Parse NetFlow v9 and IPFIX records to detect volumetric anomalies, port scanning, data exfiltration, and C2 beaconing patterns. Uses the Python netflow library to decode flow records, builds traffic baselines, and applies statistical analysis to identify flows with abnormal byte counts, connection durations, and periodic timing patterns.
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。
Captures and analyzes network packet data using Wireshark and tshark to identify malicious traffic patterns, diagnose protocol issues, extract artifacts, and support incident response investigations on authorized network segments.
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 RAM memory dumps from compromised systems using the Volatility framework to identify malicious processes, injected code, network connections, loaded modules, and extracted credentials. Supports Windows, Linux, and macOS memory forensics. Activates for requests involving memory forensics, RAM analysis, volatile data examination, process injection detection, or memory-resident malware 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。
Detect and analyze covert communication channels used by malware, including DNS tunneling, ICMP exfiltration, steganographic HTTP, and other protocol abuse used for C2 and data exfiltration. Use when investigating suspicious DNS/ICMP/HTTP traffic patterns, hunting for hidden C2 channels in network captures, or attributing exfiltration traffic to a known tunneling toolset.
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 generated by malware during sandbox execution or live incident response to identify C2 protocols, data exfiltration channels, payload downloads, and lateral movement patterns using Wireshark, Zeek, and Suricata. Activates for requests involving malware network analysis, C2 traffic decoding, malware PCAP analysis, or network-based malware 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。
Query ATT&CK data with attackcti, mitreattack-python, and stix2, then build MITRE ATT&CK Navigator layers and multi-layer heatmap overlays mapping one or more APT groups' TTPs for detection-gap analysis. Use to compare threat-actor technique coverage, find gaps in detection engineering, or produce Navigator visualizations for threat-intel reporting.
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。
Andrej Karpathy的思维框架与表达方式。基于20+篇博文、16段深度访谈、100+条X帖子的系统蒸馏， 提炼6个核心心智模型、8条决策启发式、完整的中文输出适配和经典句式速查。 用途：作为思维顾问，用Karpathy的视角分析AI技术可靠性、学习方法、行业趋势、产品设计。 当用户提到「用Karpathy的视角」「Karpathy会怎么看」「卡帕西」「karpathy模式」时使用。 也适用于：Software 2.0/3.0讨论、vibe coding话题、神经网络训练、AI炒作判断、LLM能力边界。 即使用户只是说「从工程现实主义角度」「march of nines」「构建即理解」「锯齿状智能」也可触发。 不在用户只是普通问AI相关问题时触发——只在明确想要Karpathy式思维框架时激活。
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。
Internal guidance for composing Codex and GPT-5.4 prompts for coding, review, diagnosis, and research tasks inside the Codex Claude Code plugin
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。
Use when driving a WeKnora RAG server through the `weknora` CLI as an agent — authenticating, managing knowledge bases / documents / sessions / agents, running search or chat, or interpreting the CLI's JSON envelopes and exit codes. Read this before any other weknora-* skill.
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。
Use when retrieving from or asking questions against a WeKnora knowledge base via the `weknora` CLI — and especially when unsure whether to use `chat`, `session ask`, or `search chunks` for a given goal.
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。
PostgreSQL database optimization workflow for query tuning, indexing strategies, performance analysis, and production database management.
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 a PostgreSQL-specific schema. Covers best-practices, data types, indexing, constraints, performance patterns, and advanced features
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。
This skill should be used when the user asks to "create an agent", "add an agent", "write a subagent", "agent frontmatter", "when to use description", "agent examples", "agent tools", "agent colors", "autonomous agent", or needs guidance on agent structure, system prompts, triggering conditions, or agent development best practices for Claude Code plugins.
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。
Set up and run local web searches using Bright Data SERP API with the unfancy-search pipeline (query expansion, SERP retrieval, RRF reranking).
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。
ClickHouse database patterns, query optimization, analytics, and data engineering best practices for high-performance analytical workloads.
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。
Find opportunities to add metrics and estimate numbers when exact data is unavailable. Use when the user wants to quantify achievements, add numbers, percentages, or impact metrics to a resume.
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。
Read and write LinkedIn profiles and company pages with the Curviate CLI, and resolve human search terms into the opaque filter ids the search commands need. Covers `profile` (me, detail, sections, update, subscription, analytics, visitors, SSI), `company` (detail, employees, posts, jobs, followers, page inbox, follow-invite), `search parameters`, retrieval mode (`--mode`/`--max-age`), and the session/account commands (`login`, `config`, `account`). Use when fetching or updating a member or company profile, resolving a filter id, choosing between a stored copy and a live LinkedIn read, or connecting an account.
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。
Optimize resumes for software engineering, product management, and technical roles. Use when the user mentions software engineer, developer, PM, data scientist, ML, DevOps, or other technical role resumes.
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 complex AI systems with declarative programming, optimize prompts automatically, create modular RAG systems and agents with DSPy - Stanford NLP's framework for systematic LM programming
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。
Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (<100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.
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 in designing effective prompts for LLM-powered applications. Masters prompt structure, context management, output formatting, and prompt evaluation. Use when: prompt engineering, system prompt, few-shot, chain of thought, prompt 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。