2,262 件 ・ 関連度順
概要と使いどころ
Exploit an application's absolute trust in its underlying LLM (Overreliance). Use this skill to induce critical "hallucinations" (confident falsehoods) that cause downstream logical systems or automated agents tracking the LLM's output to make destructive actions or grant unauthorized access.
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。
HTTP/2 protocol-specific attack playbook. Use when the target supports HTTP/2 and you need to exploit binary framing, HPACK compression, h2c upgrade smuggling, pseudo-header injection, stream multiplexing abuse, or H2→H1 downgrade translation flaws.
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 hunting GitHub Actions cache poisoning vulnerabilities where an attacker can inject malicious content into the CI/CD cache and have it restored by a privileged downstream workflow. Trigger on: "cache poisoning", "actions/cache", "actions/setup-node", "node_modules cache", "GitHub Actions cache", "pnpm cache", "LRU eviction", "10GB limit", "Cacheract", "poisoned cache", "workflow cache attack", supply chain via CI cache, "ng-renovate", "cache stuffing", scheduled workflow cache restore, shared cache key, "hashFiles package.json", cross-workflow cache, PR workflow release workflow same key, "npm install prefer-offline", Cacheract, Gato-X, supply chain npm token.
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。
Insecure deserialization playbook. Use when Java, PHP, or Python applications deserialize untrusted data via ObjectInputStream, unserialize, pickle, or similar mechanisms that may lead to RCE, file access, or privilege escalation.
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。
White-box source code security review structured around OWASP ASVS 5.0 (427 verification requirements across 16 chapters). Reads and understands application source code to build a security-aware knowledge base that enriches all downstream skills. Covers: tech stack identification, route/endpoint mapping, authentication and authorization architecture, dangerous function patterns, source-to-sink data flow tracing, IaC review, dependency analysis, ASVS compliance mapping, and LLM integration security (prompt injection, tool abuse, output handling, RAG poisoning, MCP server patterns). When LLM/AI framework usage is detected, automatically reviews OWASP LLM Top 10 patterns from source code and chains into /ai-redteam with white-box context for live endpoint testing. Chains into /pentester, /threat-modeling, /web-exploit, /api-security, /cloud-security, /analyze-cve, /credential-audit, and /ai-redteam — providing white-box context that transforms black-box testing into targeted, informed assessment.
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 adding multimedia to PyQt/PySide6 apps - audio and video playback with QMediaPlayer, camera capture with QCamera and QMediaCaptureSession, audio/video recording with QMediaRecorder, GStreamer backend setup, or codec and platform compatibility issues
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 building Python async HTTP services or clients with aiohttp - web server routing, middleware, WebSocket, SSE, streaming, client sessions, pytest-aiohttp testing, or troubleshooting SSL and timeout issues
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 building RAG or LLM applications with LlamaIndex - data loaders, node parsing, vector stores, retrievers and rerankers, query engines, agents and workflows, streaming, or evaluation
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 building high-performance APIs with django-bolt - routing, JWT auth, msgspec serializers, WebSockets, SSE streaming, file uploads, ORM patterns, runbolt deployment, or migrating from DRF
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。
Next.js App Router patterns (15-16) - file-based routing, Server/Client Components, streaming, Suspense, metadata API, parallel routes, Server Actions, mutations, revalidation, Cache Components
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。
File-based routing, loaders, actions, defer streaming, useFetcher, error boundaries, progressive enhancement
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。
SvelteKit full-stack framework - file-based routing, load functions, form actions, server hooks, SSR/SSG, API routes, streaming, progressive enhancement
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。
AI integration review patterns. Use when reviewing model API calls, prompt construction, LLM output handling, RAG pipelines, and tool-calling code. Covers prompt-injection call-chain tracing, output validation, token budgets, retry/timeout handling, streaming, and key/PII exposure.
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。
Official Cohere TypeScript SDK patterns -- CohereClientV2, chat, embeddings, rerank, RAG with citations, tool use, streaming, and model selection
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。
Hugging Face Inference SDK patterns for TypeScript/Node.js — InferenceClient setup, chat completion, text generation, streaming, embeddings, image generation, audio transcription, translation, summarization, and Inference Endpoints
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。
Direct MySQL database access with mysql2 driver -- connection pools, prepared statements, transactions, streaming, typed queries, error handling
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。
LangChain.js patterns for building LLM applications — chat models, LCEL chains, prompt templates, structured output, agents, tools, RAG, streaming, and LangSmith tracing
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。
Official Mistral AI TypeScript SDK patterns — client setup, chat completions, streaming, function calling, structured outputs, embeddings, vision, Codestral FIM, and production best practices
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。
Official TypeScript SDK for Google Gemini — client setup, text generation, multimodal input, function calling, structured output, streaming, embeddings, context caching, and chat 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。
Together AI SDK patterns for TypeScript — client setup, chat completions, streaming, structured output, function calling, embeddings, image generation, fine-tuning, and OpenAI-compatible endpoints
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。
Official OpenAI SDK patterns for TypeScript/Node.js — client setup, Chat Completions, Responses API, streaming, structured outputs, function calling, embeddings, vision, audio, and production best practices
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 LLM inference with the Ollama JavaScript client -- chat, streaming, tool calling, vision, embeddings, structured output, model management, and OpenAI-compatible endpoint
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。
Vercel AI SDK patterns - providers, text generation, streaming, structured output, tool calling, chat UI hooks, embeddings, and RAG
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。
Redis in-memory data store patterns with ioredis and node-redis -- caching, sessions, rate limiting, pub/sub, streams, queues, transactions, cluster
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。