744 件 ・ 関連度順
概要と使いどころ
Design spec with 98 rules for building CLI tools that AI agents can safely use. Covers structured JSON output, error handling, input contracts, safety guardrails, exit codes, and agent self-description.
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。
Security checks for repository ingestion — scans repos for poisoned examples, dependency and supply-chain attacks, file/path traversal, format-based attacks, and enforces quarantine-before-commit. Load as part of the secure-* skill sequence whenever an agent reads, ingests, or learns from a GitHub repository. Also load when the user asks to check a repo for poisoned code, scan dependencies, verify supply chain safety, check for path traversal, scan repo files for attacks, or audit a repo before ingestion. Covers Issues 3, 4, 7, 8 from the agent security threat model: poisoned training data, dependency attacks, file/path attacks, and format-based attacks.
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。
Azure AI Content Safety SDK for Python. Use for detecting harmful content in text and images with multi-severity classification.
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 content moderation applications using the Azure AI Content Safety SDK for Java.
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 spec with 98 rules for building CLI tools that AI agents can safely use. Covers structured JSON output, error handling, input contracts, safety guardrails, exit codes, and agent self-description.
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 AI product engineer who has shipped LLM features to millions of users. You've debugged hallucinations at 3am, optimized prompts to reduce costs by 80%, and built safety systems that caught thousands of harmful outputs. You know that demos are easy and production is hard.
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 this skill when the user asks where to find specific information at fmcsa.dot.gov — the SAFER (Safety and Fitness Electronic Records System), Carrier Snapshot, Compliance Safety Accountability (CSA) data, PSP (Pre-Employment Screening Program), Clearinghouse, Unified Carrier Registration (UCR), MCS-150 update, complaints, DataQs, and the FMCSA Portal login. Reference fmcsa.dot.gov.
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。
Doctrine for fixing TypeScript errors against generated types (database.types.ts, api-types.ts). Use when fixing tsc or type-check failures or a type-errors batch, writing supabase .rpc()/.from() calls or DB shape guards, errors mention Json/unknown/Database, or you are tempted to cast or suppress.
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。
Real tests around a big code change: written first, proven to fail on a planted break, run by a fresh agent in the live product before and after. Use when told to use safety-net, or before a change wide enough that one agent cannot see every way it breaks. NOT for one test (forcing-function-tests).
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。
Jailbreak API-served LLMs using G0DM0D3 techniques — Parseltongue input obfuscation (33 techniques), GODMODE CLASSIC system prompt templates, ULTRAPLINIAN multi-model racing, encoding escalation, and Hermes-native prefill/system prompt integration. Use when a user wants to bypass safety filters, jailbreak a model, or red-team model robustness.
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 with OpenAI's stateless APIs - Chat Completions (GPT-5, GPT-4o), Embeddings, Images (DALL-E 3), Audio (Whisper + TTS), and Moderation. Includes Node.js SDK and fetch-based approaches for Cloudflare Workers. Use when: implementing chat completions with GPT-5/GPT-4o, streaming responses with SSE, using function calling/tools, creating structured outputs with JSON schemas, generating embeddings for RAG (text-embedding-3-small/large), generating images with DALL-E 3, editing images with GPT-Image-1, transcribing audio with Whisper, synthesizing speech with TTS (11 voices), moderating content (11 safety categories), or troubleshooting rate limits (429), invalid API keys (401), function calling failures, streaming parse errors, embeddings dimension mismatches, or token limit exceeded.
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, build, deploy, and operate production AI agent systems — single agents, multi-agent teams, and autonomous swarms. Complete methodology from agent architecture through orchestration, memory systems, safety guardrails, and operational excellence.
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。
Handle mental health crisis situations in AI coaching safely. Use when implementing crisis detection, safety protocols, emergency escalation, or suicide prevention features. Activates for crisis keywords, safety planning, hotline integration, and risk 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。
Design bounded applications and workflows for an always-on AI agent with episodic memory. Covers product ideas, persistence-dependent workflows, and constructed examples that make delegation, interruption, evidence, and opt-out explicit. Activate on: "what can an always-on agent do", "persistent agent use cases", "agent applications", "proactive agent ideas", "what to build with episodic memory", "always-on agent product", "personal AI assistant ideas", "/always-on-agent-applications". NOT for: building the architecture (use always-on-agent-architecture), designing inputs (use always-on-agent-inputs), safety and privacy (use always-on-agent-safety).
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。
MyBatisを使うJavaのデータ保存・取得処理を設計、レビューします。SQLと結果の対応付け、動的SQLの安全性、トランザクションや検索性能を扱います。
Djangoの公開や変更提案の前に、環境・コード品質・データベース変更・テスト・脆弱性・設定を順に検証し、段階別の合否と修正すべき問題をレポートにまとめます。
Scan inputs for prompt injection, unsafe content, and adversarial attacks using AIDefence. Use when processing untrusted input (user submissions, API payloads, webhook data, tool outputs) before passing it to a model or executing it.
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。
Review database migration files when a change adds or modifies paths under migrations/. Use it before merge to collect forward, rollback, locking, and data-safety evidence.
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。
Review a skill-requested filesystem, command, network, secret, or destructive action against an explicit sandbox policy without executing it.
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 safety-bounded draft structures and runs local deterministic checks for clinical case, diagnostic, trial, safety, and aggregate research reports. Use only with synthetic, de-identified, or aggregate inputs and verified source-fact manifests; every output requires qualified review.
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 CCXT MCP (Model Context Protocol) server — connect an AI agent to 100+ cryptocurrency exchanges and prediction markets for market data, balances, and opt-in trading. Covers installing the server in Claude Desktop/Code, Cursor, VS Code and other MCP hosts; configuring named accounts and API keys (kept local, never seen by the model); the capability tiers and safety rails; sandbox/testnet trading; and how to drive the tools. Use when the user wants an agent to fetch prices/order books/candles, check balances/orders/positions, place or cancel orders, or explore prediction markets across exchanges.
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。
Perform a systematic diagnostic scan of an AI workflow across 5 quality dimensions — prompt quality, context efficiency, tool health, architecture fitness, and safety — producing a scored report with prioritized remediation actions.
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。
Guarantee valid JSON/XML/code structure during generation, use Pydantic models for type-safe outputs, support local models (Transformers, vLLM), and maximize inference speed with Outlines - dottxt.ai's structured generation library
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。
Guides agents to interactively discover customer requirements for live, bidirectional multi-agent AI systems that process continuous streams of multimodal data for real-time technical guidance and safety monitoring. Generates a custom Google Cloud solution that uses opinionated best practices and architecture guidance. Use when users need agentic assistance to design and create a multi-product solution in the cloud for live bidirectional multimodal streaming workloads. Don't use for simple text-based chat applications or workloads without real-time streaming requirements.
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。