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概要と使いどころ
Apply Anthropic Claude API security best practices for key management, input validation, and prompt injection defense. Use when securing API keys, validating user inputs before sending to Claude, or implementing content safety guardrails. Trigger with phrases like "anthropic security", "claude api key security", "secure anthropic", "prompt injection defense".
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。
Create, improve, or evaluate an existing skill from workflows, prompts, SOPs, scripts. Use for migration/release/package, routing, evals/tests, install/trust checks, 优化已有 skill, 补 trigger 评测. Exclude summary/translation/docs, brainstorming, one-off prompts, copy-only edits, outline-only requests.
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。
A test skill with prompt injection 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。
LLM prompt injection playbook. Use when testing AI/LLM applications for direct injection, indirect injection via RAG/browsing, tool abuse, data exfiltration, MCP security risks, and defense bypass 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。
Use when users ask for a hand-drawn or illustrated image prompt, name one of the repository's 20 numbered styles or the 3.1 stable variant, or mention triggers such as 亲子手绘、家庭蜡笔画、蜡笔、吉卜力、水墨、像素、淡彩速写、动画概念、水粉、北欧绘本、纸雕、软胶潮玩、墨线绘本、暖色扁平绘本、圆头红线、暖黄墨线、family-crayon-card、warm-flat-storybook、roundhead-redline、warm-yellow-ink-story. Produces prompt text or a formal reference-bearing call bundle and does not generate images.
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 designing, generating an MVP blueprint for, auditing, troubleshooting, refactoring, or explaining an agentic harness for any domain. Covers provider-neutral agent architecture for OpenAI, Anthropic, and OpenAI-compatible APIs: agent loops, tool design, record provenance, interactive presentation, user-memory lifecycles, environment-adaptive tools, speculative tool execution, late-bound capabilities, permissions, system prompts, planning, goals, always-on agents and durable runtime, adaptive agent teams, context compaction, memory, skills, MCP/external connectors, public-board communications, realtime voice agents, hardware agents and board deployment, self-refining recursive harnesses, programmable context, continual refinement, observability, evals, prompt caching, agent-legible environments, feedback loops, and 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。
Manage Model Context Protocol (MCP) servers - discover, analyze, and execute tools/prompts/resources from configured MCP servers. Use when working with MCP integrations, need to discover available MCP capabilities, filter MCP tools for specific tasks, execute MCP tools programmatically, access MCP prompts/resources, or implement MCP client functionality. Supports intelligent tool selection, multi-server management, and context-efficient capability discovery.
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。
Replay an LLM inference request trace (Mooncake / vLLM / SGLang hash_ids format) against a block-level KV prefix cache and compute hit statistics. Use when given a request trace plus cache configuration and asked for hit rate, hit tokens, or final cache contents. Covers the longest-contiguous-prefix semantics that distinguishes KV prefix caching from full-prompt prompt caching, the policy-specific residency and eviction rules (LRU, LFU, S3FIFO), and the partial-last-block accounting rule.
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。
Generate interactive command-line prompts using Inquirer.js with validation, conditional logic, and custom renderers. Creates user-friendly input collection flows for CLI applications.
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。
Token-efficient prompt compression techniques for cost 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。
Prompt injection detection and prevention for secure LLM applications
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。
Structured prompt template creation with variables, formatting, and version control
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。
Constitutional AI and safety guardrail prompts for aligned LLM 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。
Chain-of-thought and step-by-step reasoning prompts for complex problem solving
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 tackling complex reasoning tasks requiring step-by-step logic, multi-step arithmetic, commonsense reasoning, symbolic manipulation, or problems where simple prompting fails - provides comprehensive guide to Chain-of-Thought and related prompting techniques (Zero-shot CoT, Self-Consistency, Tree of Thoughts, Least-to-Most, ReAct, PAL, Reflexion) with templates, decision matrices, and research-backed 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。
PromptHub release update skill. Use for `/update-readme`, version bumps, changelog updates, website sync, GUI screenshot/doc refresh, and multilingual release/documentation alignment.
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。
Diagnose why Claude Code is (or isn't) prompting for permission. By default reads only repo-local layers (CLI project, CLI project-local, VSCode workspace). Host-global layers (CLI user `~/.claude/`, VSCode user settings) are read ONLY when the user explicitly confirms — those files may contain unrelated paths or secrets. Use when user says "why is it asking me to approve?", "permission check", "why am I getting prompts?", "bypass isn't working", "check my permissions". Read-only diagnostic.
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 and retrieves AI video and image-related assets via the Arcads external API (Seedance 2.0, Sora 2, Veo 3.1, Kling, Grok Video, Nano Banana, b-roll, scene, script/actor flows). Loads prompts from the bundled prompting guide and model library, respects HTTP Basic auth from ARCADS_API_KEY, and polls assets/videos until ready. Use when the user mentions Arcads, external-api.arcads.ai, Seedance, Sora2, Veo, Kling, Nano Banana, b-roll, UGC scripts, or generating marketing creative through Arcads.
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。
Fine-tune models on Microsoft Foundry using SFT (supervised), DPO (preference), or RFT (reinforcement with graders). Covers dataset preparation, training job submission, deployment, and evaluation. USE FOR: fine-tune, SFT, DPO, RFT, training data, grader, distillation, fine-tuned model, training job, large file upload, calibrate grader, deploy fine-tuned model, evaluate fine-tuned model. DO NOT USE FOR: general model deployment without fine-tuning (use deploy-model), agent creation (use agents), prompt optimization without training (use prompt-optimizer).
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。
Research any company, product, or service from a URL plus description and build a comprehensive, evidence-bound AEO/GEO/AI-visibility prompt panel across buyer jobs, information acts, journey states, B0-B5 proximity, aided status, roles, locales, variants, partitions, surfaces, and measurement lanes. Use when a user wants prompts to track in ChatGPT, Claude, Gemini, Perplexity, AI search, or answer engines; wants an AI-visibility measurement design; or needs a versioned panel rather than an SEO keyword list.
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。
Authors Vally conformance tests for prompts, instructions, agents, and skills, and refuses jailbreak, prompt-injection, harmful-elicitation, TOS, CoC, and PII-extraction stimuli
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。
通用学习器 - 从任何领域的Prompt中自动提取可复用元素，持续学习和积累知识
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。
智能提示词生成器 v2.0 - 支持人像/跨domain/设计三种模式，语义理解、常识推理、一致性检查
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。
Calibrate an LLM judge against human labels using data splits, TPR/TNR, and bias correction. Use after writing a judge prompt (write-judge-prompt) when you need to verify alignment before trusting its outputs. Do NOT use for code-based evaluators (those are deterministic; test with unit tests per `write-code-eval`).
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。