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概要と使いどころ
Red team engagement planning is the foundational phase that defines scope, objectives, rules of engagement (ROE), threat model selection, and operational timelines before any offensive testing begins.
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 before loading, placing, scaling, or animating GLB models in PlayCanvas to measure bounds, grounding, clips, joints, and hierarchy with the bundled offline inspector.
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 composing PlayCanvas entity hierarchies from models, colliders, rigid bodies, and scripts, so each object has one semantic root and clear lifecycle.
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 loading glTF/GLB models or textures in PlayCanvas, choosing Draco or Basis/KTX2 compression, or setting up decoders and preloading.
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 the user wants a complete SPSS or SPSS-MCP empirical research workflow: organize source data, prepare datasets, design variables and models, run SPSS analyses, export Chinese result paragraphs and tables, write a Chinese LaTeX paper, and compile the final PDF.
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 Quarkus + LangChain4j projects end-to-end and add agentic components to existing ones — AI services, tools, agents and multi-agent workflows, RAG pipelines, MCP clients and servers (Model Context Protocol), guardrails, and embedding store setups. Use this whenever the user wants a new Quarkus + LangChain4j project or module created, or an agentic component added to an existing project in this stack, including a baseline pom.xml, application.properties, project layout, or starter classes.
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 you need to know where a piece of reporting actually came from — the user gives an article URL and asks "where does this come from?", "who is the original source?", "is this just a rewrite?", or another skill needs the provenance chain before grading or citing a report. Follows an article's links and named attributions back to the originating vendor, government, victim or actor document and returns the chain, the primary's stated access and confidence language, publication dates, and every hop it could not resolve. Deterministic — a script or the Liberty91 platform resolves the chain; the model does not reconstruct it from memory. Invoked by /quality-of-information-check, /threat-actor-profiling, /campaign-tracking and /intelligence-writing. Works on any URL with no API key.
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 documenting a named campaign across time and victims, the user asks to start or update a campaign record, or another skill identified a multi-incident cluster that warrants formal tracking. Provides the template (timeline, attribution, victimology, attack chain, Diamond Model mapping, IOC clusters) and the lifecycle from active to historical.
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 challenging a prevailing analytical judgment, the user asks "what is the opposing case?" / "argue the other side" / "play devil's advocate", or wants an assessment stress-tested before release. Takes the team's own lead judgment and builds the strongest case that it is wrong. A contrarian technique. It does not model what an adversary would do next, which is Red Team Analysis. Formerly /red-team-analysis.
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 assessing the maturity of a CTI programme, the user asks "how mature is our CTI?" / "what should we improve next?", or wants a benchmark against the five-level model across six dimensions.
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 the user asks about ISAC participation, TAXII feeds, MISP communities, FIRST, STIX-based sharing, or how to publish intelligence externally. Covers sharing models, standards, communities, and TLP-governed dissemination.
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。
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。
This skill should be used when the model's ROLE_TYPE is orchestrator and needs to delegate tasks to specialist sub-agents. Provides scientific delegation framework ensuring world-building context (WHERE, WHAT, WHY) while preserving agent autonomy in implementation decisions (HOW). Use when planning task delegation, structuring sub-agent prompts, or coordinating multi-agent workflows.
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 quantitative analyst specializing in financial modeling, algorithmic trading, and risk analytics. Masters statistical methods, derivatives pricing, and high-frequency trading with focus on mathematical rigor, performance optimization, and profitable strategy development.
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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。
Complete operational workflow for implementer agents (Codex, Gemini, etc.) making code changes and writing tests. Drives all work through atomic commits — each loop operates on the smallest complete, reviewable change. Defines the Code Change Loop, Test Writing Loop, Lint Gate, and Issue Filing process with circuit breakers, severity levels, and escalation rules. Requires `committer` for all commits. Includes bundled provider-aware review scripts that keep same-model shell-outs as the last resort, plus a fresh-context Codex fallback for code review and test audit. Use this skill when starting any implementation task.
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 specialized agent experts with pre-loaded domain knowledge using the Act-Learn-Reuse pattern. Use when building domain-specific agents that maintain mental models via expertise files and self-improve 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 and build custom Claude Code agents with effective descriptions, tool access patterns, and self-documenting prompts. Covers Task tool delegation, model selection, memory limits, and declarative instruction design. Use when: creating custom agents, designing agent descriptions for auto-delegation, troubleshooting agent memory issues, or building agent pipelines.
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。
Guide for creating effective Claude Code agents. This skill should be used when users want to create a new agent (or update an existing agent) that configures Claude with specialized system prompts, tool restrictions, model selection, and MCP/skill integrations.
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。
Especialista en configuración de agentes de IA: templates, tools, models, prompts y seed data.
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。
Validates agent configurations for model selection, tool permissions, focus areas, and approach quality. Use when reviewing, auditing, improving agents, or learning agent 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。
Guide for authoring specialized AI agents. Use when creating, updating, or improving agents, choosing models, defining focus areas, configuring tools, or learning agent 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。