1,457 件 ・ 関連度順
概要と使いどころ
Launch new products from idea to first customers. Use when launching products, finding early adopters, building launch week playbooks, diagnosing why adoption stalls, or learning that press coverage does not equal growth. Includes the three-layer diagnosis, the 2-week experiment cycle, and the launch that got 50K impressions and 12 signups.
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。
Prepare for tomorrow's meetings and tasks. Pulls calendar from Outlook via WorkIQ, cross-references open tasks and workspace context, classifies meetings, detects conflicts and day-fit issues, finds learning and deep-work slots, and generates a structured HTML prep file with productivity recommendations.
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。
Provides guidance for LLM post-training with RL using slime, a Megatron+SGLang framework. Use when training GLM models, implementing custom data generation workflows, or needing tight Megatron-LM integration for RL scaling.
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。
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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。
把公众号文章、Markdown、笔记、文档或长文本拆成适合小红书/小绿书的完整发布包。先分析全文、论证关系和模块覆盖，输出含完整逐卡文案、布局 ID、3～5 个标题建议与发布正文的确认方案；确认后生成可逐元素编辑、移动、缩放、切换整套或单卡主题、编辑发布文案并导出 1080x1440 PNG 的独立 HTML。也适用于复用已有拆分方案或 v1/v2 卡片 JSON。
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。
Creates step-by-step tutorials and educational content from code. Transforms complex concepts into progressive learning experiences with hands-on examples. Use PROACTIVELY for onboarding guides, feature tutorials, or concept explanations.
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。
Orchestrate multi-agent incident response with modern SRE practices for rapid resolution and learning:
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。
You are an **expert onboarding specialist and knowledge transfer architect** with deep experience in remote-first organizations, technical team integration, and accelerated learning methodologies. Your role is to ensure smooth, comprehensive onboarding that transforms new team members into productive contributors while preserving institutional knowledge.
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 data scientist for advanced analytics, machine learning, and statistical modeling. Handles complex data analysis, predictive modeling, and business intelligence. Use PROACTIVELY for data analysis tasks, ML modeling, statistical analysis, and data-driven insights.
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。
.NET unit test fundamentals and FIRST principles specialized skill. Used when creating unit tests, understanding testing basics, learning 3A Pattern, and mastering testing best practices. Covers FIRST principles, AAA Pattern, Fact/Theory, testing pyramid, etc. Keywords: unit test, unit test, unit testing, test fundamentals, testing fundamentals, FIRST principle, FIRST principle, 3A pattern, AAA pattern, Arrange Act Assert, Fact, Theory, InlineData, how to write tests, testing best practices, create unit 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。
Use when user requests research requiring multiple sources, comprehensive analysis, or synthesis across topics - technical research, domain knowledge gathering, market analysis, or learning about complex subjects
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。
Extract decisions, lessons, patterns, and surprises from completed phase artifacts
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 AI-generated deepfake audio used in voice phishing (vishing) attacks by extracting spectral features (MFCC, spectral centroid, spectral contrast, zero-crossing rate) and classifying samples with machine learning models. Supports batch analysis of audio files, generates confidence scores, and produces forensic reports. Activates for requests involving deepfake voice detection, vishing investigation, AI-generated speech analysis, voice cloning detection, or audio authenticity 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。
Detect anomalies in DNP3 (Distributed Network Protocol 3) communications used in SCADA systems by monitoring for unauthorized control commands, firmware update attempts, protocol violations, and deviations from baseline traffic patterns using deep packet inspection and machine learning approaches.
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。
Implement User and Entity Behavior Analytics using Elasticsearch/OpenSearch to build behavioral baselines, calculate anomaly scores, perform peer group analysis, and detect insider threat indicators such as data exfiltration, privilege abuse, and unauthorized access 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。
Detects anomalous authentication patterns using UEBA analytics, statistical baselines, and machine learning models to identify impossible travel, credential stuffing, brute force, password spraying, and compromised account behaviors across authentication logs. Activates for requests involving authentication anomaly detection, login behavior analysis, UEBA implementation, or suspicious sign-in 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。
Deploy AI and NLP-powered detection systems to identify business email compromise attacks by analyzing writing style, behavioral patterns, and contextual anomalies that evade traditional rule-based filters.
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。
Full exploit development course roadmap and syllabus: weekly topics, recommended reading, lab setup, and learning path from vulnerability classes through advanced exploitation. Use to structure exploit dev training or onboard new researchers.
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。
Complete bug bounty workflow — recon (subdomain enumeration, asset discovery, fingerprinting, HackerOne scope, source code audit), pre-hunt learning (disclosed reports, tech stack research, mind maps, threat modeling), vulnerability hunting (IDOR, SSRF, XSS, auth bypass, CSRF, race conditions, SQLi, XXE, file upload, business logic, GraphQL, HTTP smuggling, cache poisoning, OAuth, timing side-channels, OIDC, SSTI, subdomain takeover, cloud misconfig, ATO chains, agentic AI), LLM/AI security testing (chatbot IDOR, prompt injection, indirect injection, ASCII smuggling, exfil channels, RCE via code tools, system prompt extraction, ASI01-ASI10), A-to-B bug chaining (IDOR→auth bypass, SSRF→cloud metadata, XSS→ATO, open redirect→OAuth theft, S3→bundle→secret→OAuth), bypass tables (SSRF IP bypass, open redirect bypass, file upload bypass), language-specific grep (JS prototype pollution, Python pickle, PHP type juggling, Go template.HTML, Ruby YAML.load, Rust unwrap), and reporting (7-Question Gate, 4 validation ...
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。
Local-tooling companion to the bug-bounty orchestrator — carries the SAME complete bug-bounty workflow, but reach for THIS variant when you also need to resolve where tools, wordlists, and clones are installed on the local machine (jhaddix, SecLists, trufflehog, ffuf, dalfox, ghauri); for pure orchestration/routing use the bug-bounty skill. Workflow it covers — recon (subdomain enumeration, asset discovery, fingerprinting, HackerOne scope, source code audit), pre-hunt learning (disclosed reports, tech stack research, mind maps, threat modeling), vulnerability hunting (IDOR, SSRF, XSS, auth bypass, CSRF, race conditions, SQLi, XXE, file upload, business logic, GraphQL, HTTP smuggling, cache poisoning, OAuth, timing side-channels, OIDC, SSTI, subdomain takeover, cloud misconfig, ATO chains, agentic AI), LLM/AI security testing (chatbot IDOR, prompt injection, indirect injection, ASCII smuggling, exfil channels, RCE via code tools, system prompt extraction, ASI01-ASI10), A-to-B bug chaining (IDOR→auth bypass...
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。
Exploit development curriculum covering core vulnerability classes with real-world CVE case studies: stack/heap buffer overflows, use-after-free, integer overflows, format strings, type confusion, and race conditions. Use when learning or teaching vuln classes, researching specific CVE patterns, or building exploit dev knowledge.
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。
Git-Notes-Based knowledge graph memory system. Claude should use this SILENTLY and AUTOMATICALLY - never ask users about memory operations. Branch-aware persistent memory using git notes. Handles context, decisions, tasks, and learnings across sessions.
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。
Development skill from everything-claude-code
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。
Generates comprehension questions and language exercises based on the analyzed content to create a complete learning loop.
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。