1.2万 件 ・ 関連度順
概要と使いどころ
Assistant-behavior design skill for approximating a Claude-like analytical, careful, conversational style without claiming to be Claude or Anthropic. Use when the user asks to duplicate, emulate, adapt, or recreate a Claude-style "mythos" or persona for prompts, agent specs, writing style, reasoning discipline, safety posture, or UX 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。
Detection engineering skill for writing, testing, and tuning Sigma, YARA, YARA-L, Suricata, Snort, Zeek, KQL, SPL, EQL, Chronicle, and Elastic detection rules. Covers ATT&CK coverage mapping, telemetry gap analysis, false-positive tuning, detection-as-code pipelines, and validation with Atomic Red Team and Caldera. Use to design durable detections backed by 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。
Cybersecurity partner skill for acting as a practical security collaborator across architecture review, threat modeling, secure design, risk prioritization, defensive planning, vulnerability remediation, incident readiness, security roadmaps, and technical decision support. Use when the user wants an ongoing security advisor, reviewer, or second set of eyes.
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。
Purple team automation skill that links offensive emulation (Atomic Red Team, Caldera, Stratus Red Team, Leonidas) with detection validation, telemetry-gap analysis, and continuous control testing. Use to design adversary emulation plans tied to MITRE ATT&CK, execute safe tests in lab or controlled environments, and produce evidence-backed detection coverage reports.
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。
Smart contract and Web3 security audit skill for Solidity, Vyper, Move, and Cairo. Covers reentrancy, access control, oracle manipulation, MEV/sandwich exposure, signature replay, upgrade safety, ERC token edge cases, bridge risk, and DeFi invariants. Tooling includes Slither, Mythril, Echidna, Foundry, Halmo, Certora, and custom invariant tests. Use for design review, code audit, and incident analysis on contracts you own or are authorized to assess.
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 well-structured prompts for AI agents using proven architecture patterns. Use when users ask to write agent prompts, system prompts, or agent instructions, or want to improve existing prompts that aren't working.
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 prompt engineer specializing in designing, optimizing, and managing prompts for large language models. Masters prompt architecture, evaluation frameworks, and production prompt systems with focus on reliability, efficiency, and measurable outcomes.
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 agent system prompts with explicit behavior rules and cognitive control. This skill should be used when users need to define agent instructions, create intent-to-tool mappings, establish confirmation policies, or prevent hallucinated actions in AI agents.
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。
North star guidance for the agent-resources project. Use this skill to understand project context, get inspiration for development decisions, and resolve uncertainty about feature priorities or design choices. Reference when asking 'why does this project exist?' or 'what should we optimize for?'
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 Claude Code plugin agent .md files: writes YAML frontmatter (name, model, color, tools, hooks, disallowedTools), crafts system prompts, and creates triggering example blocks with context and commentary. Validates agent structure against naming, length, and format constraints. Use when the user asks to create an agent, add an agent, write a subagent, configure agent frontmatter, design agent system prompts, set agent tools or colors, build an autonomous agent, or add agent hooks.
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。
This skill should be used when building AI agents using prompt-native architecture where features are defined in prompts, not code. Use it when creating autonomous agents, designing MCP servers, implementing self-modifying systems, or adopting the "trust the agent's intelligence" philosophy.
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 applications where agents are first-class citizens. Use this skill when designing autonomous agents, creating MCP tools, implementing self-modifying systems, or building apps where features are outcomes achieved by agents operating in a 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。
Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates.
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 creating, improving, or troubleshooting Claude Code subagents. Expert guidance on agent design, system prompts, tool access, model selection, and best practices for building specialized AI assistants.
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 custom AI agents in Claude Code from a user's problem statement. This skill analyzes the user's use case, asks smart clarifying questions, researches the internet for similar agents (GitHub repos, blogs, Claude Code community patterns), and then architects and builds production-ready Claude Code agents — including subagents, skills, hooks, slash commands, MCP integrations, and CLAUDE.md configuration. Use this skill whenever the user wants to create an agent, build an automation workflow, set up a Claude Code subagent, design a multi-agent system, or says things like 'build me an agent for...', 'automate this with Claude Code', 'I want a subagent that...', 'help me create a workflow', 'set up an agent pipeline', or any variation of wanting Claude Code to do something specialized. Also trigger when the user mentions agent architecture, agent SDK, agentic workflows, or task delegation in Claude Code — even if they don't use the word 'agent' explicitly.
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。
MATLAB R2026a and Simulink workflow for creating, inspecting, modifying, simulating, validating, and exporting .slx models. Use for Simulink, Stateflow, Simscape, model-based design, parameter sweeps, signal logging, requirements, and simulation automation. On this machine, Simulink tasks should be visual-first: open the model in a visible Simulink window before doing substantial automated modeling or simulation so the user can watch the process.
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。
MATLAB R2026a workflow for control systems, system identification, robust control, MPC, Simulink Control Design, parameter tuning, constrained optimization, and numerical validation. Use whenever the user asks for controllers, transfer functions, state-space models, tuning, identification, optimal control, or solver-based engineering.
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 for hands-on repository work when a quiet, risk-scaled engineering method can improve design, execution strategy, implementation, material incorporation, debugging, review, delegation, workspace lifecycle, or verification. Keep clear local changes direct, preserve the complete requested outcome, and add method only where risk or uncertainty justifies it. Do not use for general technical explanations, simple file lookup, casual discussion, or non-engineering writing.
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。
Apply risk-based test-driven development to bugs, domain logic, state transitions, parsers, contracts, migrations, and behavior where a failing test sharpens the design.
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, run, and review fair performance, token, and cost comparisons across GitHub Copilot CLI, Codex CLI, and direct model APIs. Use for AI CLI benchmark, Copilot vs Codex, prompt-cache diagnosis, cache read/write analysis, fresh-process latency measurement, token-cost comparison, or when a benchmark must separate provider caching from local CLI caches.
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。
Safely audit, classify, deduplicate, and reorganize bookmarks or favorites in Google Chrome and Microsoft Edge while preserving intentional toolbar shortcuts, signed-in profiles, and sync state. Use when asked to organize browser bookmarks/favorites, clean duplicate bookmarks, redesign bookmark folders, classify login/reference/self-hosted links, or fix an overgrown bookmark tree.
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。
Plan high-quality PowerPoint presentations before file creation or editing. Use when designing slide structure, storyline, section flow, executive summaries, local PowerPoint production briefs, Copilot prompts, or slide-review/revision prompts.
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, implement, and audit PFC3D 5.0 asphalt wheel-tracking and intersection-rutting simulations using RVE-to-strip-to-full-size scaling, PFC5-compatible vector reaction control, Burger-calibrated contacts, vertical-horizontal coupled loads, rut/shear histories, edition-specific metrics, and explicit surrogate 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。
Design and audit PFC 5.0 wall servo, loading-rate control, equilibrium checks, micro-to-macro calibration, two-target local solves, DOE campaigns, and independent confirmation for asphalt models.
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。