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概要と使いどころ
Guide to implementing homomorphic encryption for privacy-preserving computation under GDPR. Covers scheme selection (BFV, BGV, CKKS, TFHE), Microsoft SEAL, IBM HELib, and Google FHE transpiler. Includes performance benchmarks, parameter tuning, and basic HE example code for encrypted arithmetic operations.
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 comprehensive, developer-friendly API documentation from code, including endpoints, parameters, examples, and 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。
Debug LangChain and LangGraph agents by fetching execution traces from LangSmith Studio. Use when debugging agent behavior, investigating errors, analyzing tool calls, checking memory operations, or examining agent performance. Automatically fetches recent traces and analyzes execution patterns. Requires langsmith-fetch CLI installed.
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教学过程中自动捕获和记录学习者的概念疑难点（"为什么/是什么/怎么推/什么意思"类型的问题），保存到进度文件的"概念疑难点记录"区，形成考前回顾清单。
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。
Performs deep architectural analysis of a specified module, directory, or feature area by examining structural coupling, data flow, concurrency patterns, risk, and SOLID alignment. Use when the user wants to assess, evaluate, or review the architecture, design quality, dependency structure, coupling, cohesion, or technical debt of an existing part of the codebase. Not for investigating specific bugs, runtime errors, or failures — use investigate. Not for test planning — use automated-test-planning. Not for file-level code review — use code-review. Not for researching open-ended options, prior art, or how something works — use research. Not for designing a new interface or contract — use design-an-api. Not for planning the change its findings imply — use plan-a-change. Not for discovering bounded contexts, ubiquitous language, or where code boundaries diverge from domain boundaries — use ddd-analysis. Not for writing documentation or architectural decision records.
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 requests current information, documentation, coding examples, or web research beyond the knowledge cutoff. Queries Google's AI Search mode to retrieve comprehensive AI-generated overviews with source citations from 100+ websites. Returns markdown with inline footnoted references [1][2][3]. You will receive a detailed Markdown file with references and information summarized directly from Google's AI search. Ideal for you to get new information and clues for further research.
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。
Scaffold or standardize a production-ready repository structure with specs, source layout, tests, CI, agent context, config examples, release notes, and operational docs. Use when starting a new repo, turning a prototype into a maintainable project, adding missing repository foundations, or creating a repo skeleton before implementation. For AGENTS.md-only context scaffolding, use agentsmd-scaffold.
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 a skill is brittle, drifting, repeatedly failing, or needs a Reliable Skill Contract. Trigger for phrases like skill lifeguard, reliable skill, self-maintaining skill, negative examples, verification checkpoints, drift signals, replay hooks, or failure log to skill patch. Audits or patches skills so high-value workflows include explicit forbidden behaviors, checkpoints, machine-checkable done conditions, replay or smoke hooks, and drift 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。
n8n workflow automation knowledge base. Provides n8n node information, node functionality details, workflow patterns, and configuration examples. Covers triggers, data transformation, data input/output, AI integration, covering 10 nodes. Keywords: n8n, workflow, automation, node, trigger, webhook, http request, database, ai agent.
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。
Recommend font combinations for different use cases. Provide Google Fonts alternatives to premium fonts with hierarchy examples.
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。
Develop art direction for image generation with curated style cards distilled from Open Image Prompts. Use when choosing or comparing visual directions, improving an existing generation prompt, or translating a subject, use case, or supplied image into a compact creative spec. Use img-gen-prompts for real archive examples, exact source prompts, or gallery browsing.
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。
Analyze web page content, structure, and layout to understand what a page contains and how it is organized. Trigger when the user asks to: analyze a page, understand page structure, inspect a website, summarize page content, examine page layout, review a web page, or describe what is on a page.
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 distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, artifacts, posters, or applications (examples include websites, landing pages, dashboards, React components, HTML/CSS layouts, or when styling/beautifying any web UI). Generates creative, polished code and UI design that avoids generic AI aesthetics.
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 the required PR-ready summary block, branch suggestion, title, and draft description for openai-guardrails-python after runtime, tests, examples, build/test configuration, or behavior-impacting docs change.
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。
Run the mandatory verification stack when changes affect runtime code, tests, examples, or build and test behavior in the OpenAI Guardrails Python repository.
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。
Assign, validate, normalize, and explain Luhmann/Zettelkasten-style project-note codes for Chinese book manuscripts, Obsidian Markdown folders, Mandala-Grid section files, HyperCard Markdown stacks, and project-note JSON to EPUB workflows. Use when Codex needs to scan folders for 部/章/節/項/目 numbering, generate stable card ids, insert branch codes without renumbering existing notes, map letter-coded branches to Obsidian Mandala sections, preserve K### keyword index cards, sanitize private catalogs into public examples, or prepare card IDs for EPUB and HyperCard output.
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。
Automatically reduces bug-triggering test cases to minimal form while preserving the failure. Use when debugging with large, complex test cases that need simplification, reproducing bugs with minimal examples, or creating regression tests from verbose failure scenarios. Takes a failing test and systematically removes unnecessary inputs, steps, assertions, and code using delta debugging and other reduction algorithms. Supports unit tests, integration tests, input files, and multiple programming languages (Python, JavaScript, Java, C/C++).
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。
Explains runtime errors and compilation failures with actionable debugging guidance. Use when Python or Java code throws runtime exceptions (NullPointerException, TypeError, AttributeError, etc.), compilation errors (syntax errors, type mismatches, import failures), or dependency issues. Analyzes error messages, stack traces, and code context to identify root causes and provide concrete fixes with examples. Distinct from test-related errors - focuses on errors during normal code execution and build processes.
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。
Proposes secure remediation strategies for detected security vulnerabilities including buffer overflows, injection risks, insecure deserialization, improper authentication, and unsafe cryptographic usage. Provides recommended security checks, safer API alternatives, design-level changes, code examples, trade-off analysis, and prioritized remediation plans. Does NOT automatically modify code unless explicitly requested.
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。
Hardware verification tool for checking functional equivalence between two RTL designs (Verilog). Use when users need to: (1) Verify if two RTL versions are functionally equivalent, (2) Compare original vs. refactored RTL code, (3) Validate design changes or optimizations, (4) Identify semantic vs. cosmetic differences, (5) Generate counterexamples for non-equivalent designs. Analyzes interface alignment, state variables, logic differences, and produces detailed equivalence verdicts with plain language explanations. Particularly effective for design verification, code reviews, and regression testing of RTL modifications.
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。
Systematic external reference searching across documentation, open-source repositories, and web resources. Use when working with unfamiliar libraries, APIs, or when needing production-quality implementation examples.
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 and verifies semantic equivalence between two functions, classes, or modules by examining control flow, data flow, and observable behavior. Use when comparing code implementations (refactored vs original, different implementations of same functionality, migration verification), determining if two code artifacts produce identical behavior, identifying behavioral differences between code versions, or validating that code changes preserve semantics. Supports formal reasoning and symbolic execution 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。
Analyze detected vulnerabilities to assess realistic exploitability by examining control flow, input sources, sanitization logic, and execution context. Use when users need to: (1) Determine if a vulnerability is actually exploitable in practice, (2) Assess severity and impact of security issues, (3) Prioritize vulnerability remediation, (4) Understand attack vectors and exploitation conditions, (5) Generate exploitability reports with proof-of-concept scenarios. Focuses on injection vulnerabilities (SQL, command, XSS, path traversal, LDAP) with detailed analysis of reachability, controllability, sanitization, and impact.
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。
Identify and report code smells indicating poor design or maintainability issues in Python code, including duplicate code, magic numbers, hardcoded values, God classes, feature envy, inappropriate intimacy, data clumps, primitive obsession, and long parameter lists. Use when conducting code quality audits, preparing for refactoring, improving codebase maintainability, or performing design reviews. Produces markdown reports with severity ratings, locations, descriptions, and specific refactoring recommendations with before/after examples. Triggers when users ask to find code smells, identify design issues, suggest refactorings, improve code quality, or detect maintainability problems.
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。