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Use when composing PlayCanvas entity hierarchies from models, colliders, rigid bodies, and scripts, so each object has one semantic root and clear lifecycle.

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Use before repeatedly placing GLB models in PlayCanvas to record each asset's uniform scale, grounding offset, pivot compensation, and yaw once.

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A 15-minute guided setup that teaches Cowork who you are, how you work, and what tools you use — so every result feels tailored instead of generic. Use when setting up Cowork for the first time, for personalizing your workspace preferences, during onboarding after your first session, when configuring your tools and context, or when the user says "set up cowork", "configure cowork", "why is cowork giving me generic results", "cowork setup", "personalize cowork", "make cowork know me", "cowork feels like chatgpt", or "how do I get better results from cowork".

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Teaches you how to break large tasks into stages, save your place between sessions, and recover smoothly when a task gets interrupted. Use when planning a multi-day project, for breaking down large work into manageable stages, during recovery from an interrupted session, when managing complex ongoing work, or when the user says "cowork stopped in the middle", "token limit", "task too large", "context management", "how to do big tasks", "session got cut off", "cowork forgot what we were doing", "how do I work on something over multiple days", "it lost track of the project", or "my task is too big for one session".

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Teaches you how to delegate tasks to Cowork so clearly that you get exactly what you asked for — every time. Use when learning how to give better instructions, for improving your delegation technique, during early Cowork adoption when results feel off, when writing clearer task descriptions, or when the user says "how do I ask cowork to do things", "delegation tips", "better instructions", "cowork isn't doing what I want", "help me delegate", "my results are bad", "how do I give better prompts", "cowork keeps getting it wrong", "I don't know how to use this", or "how do I get good results".

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Gathers information from multiple sources in parallel, compares what they say, flags where they agree or conflict, and delivers a clean synthesis with a confidence rating for each finding. Useful for making evidence-backed decisions, when evaluating competing claims, during due diligence on a vendor or market, and for building research briefs before strategy sessions. Use when the user says "research this", "synthesize multiple sources", "compare sources", "research report", "what does the evidence say", "gather information on", or "deep dive into".

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Audits your Cowork setup across five categories, scores each one, and gives you a prioritized improvement plan. For reviewing how well you are using Cowork, when optimizing your workspace configuration, or during a quarterly productivity review. Use when the user says "health check", "am I using cowork right", "optimize my setup", "cowork audit", "what am I missing", "is my setup good", or "how do I get more out of cowork".

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。

从零到上线的全栈应用构建指南，基于秒悟（Meoo）平台。 触发条件： (1) 用户提到"秒悟"或"Meoo"； (2) 用户要从零构建应用，且需求可被以下架构覆盖：前端 SPA（React/Vue）+ Supabase（数据库/Auth/Storage）+ Deno 边缘函数 + AI 大模型服务； (3) 用户需要部署全栈应用（含后端进程），如 Next.js SSR、Express、FastAPI 等； (4) 用户需要把项目部署到自己的阿里云账号（ECS/RDS/SLB，费用自付） 覆盖完整生命周期：项目初始化、本地开发、云服务开通、数据库管理、边缘函数部署、CDN 发布、全栈镜像部署、沙箱代码同步、账户与权益管理。

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Use this skill when evaluating VCSDD grading dimensions during adversarial review. Provides concrete evaluation checklists for each of the 5 dimensions and convergence signal detection.

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Use when the user asks to write a threat / risk / vulnerability assessment, or wants the appropriate template for each type. Distinct structures and section ordering per assessment kind. Prefers graded evidence items from /quality-of-information-check and runs that skill first when handed raw URLs or text. Confidence is capped by the weakest load-bearing claim, and at Moderate when the evidence is ungraded.

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Use when evidence needs grading before anyone reasons from it — the user asks "how reliable is this?", "grade this source", "check this report", "can I trust this article?", "is this corroborated?", or hands over a URL, a pasted report or a Liberty91 event ahead of an ACH or assessment. The Quality of Information Check from the CIA Tradecraft Primer, adapted for vendor and press CTI reporting. Resolves each document to its originating source, splits it into claims, and grades every claim on two written elements, access level (how the source knows) and claim support (what backs the statement), with a stated rationale, using a fixed rubric with hard caps. The grade is not an Admiralty rating. Five outlets citing one vendor report count as one source; anything that cannot be traced to a primary is capped, not guessed. Returns a graded claim table, gaps, a deception screen and JSON. /ach, /threat-assessment and /writing-assessments run it first by default.

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Use when the user asks "who are our customers?" / "how do we tailor for X stakeholder?" / "who should this report go to?", or wants to map / re-map stakeholder needs. Ensures intelligence reaches the right people in the right format.

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IOC export formats and procedures. CSV, STIX 2.1, OpenIOC, MISP. Handles format conversion and packaging, and carries per-claim evidence grades (access level and claim support) from /quality-of-information-check into each format (STIX confidence, MISP attribute tags).

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Workflow for enriching raw IOCs. Routes each IOC type to the appropriate /lookup-* skills, optionally correlates against MISP, and synthesises a single enrichment record per indicator. Use when the user has a batch of raw IOCs to process before triage or before pushing into a sharing platform.

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Use when the user asks about carding, BIN attacks, payment-card breach markets, fullz/CVV2 trade, autoshops (BidenCash, Brian's Club, Russianmarket, B1ack's Stash), or financial-fraud TTPs. Self-updating knowledge cell.

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Use when you need to hunt in the organisation's own Microsoft Sentinel workspace — sweep the logs for IOC hits (IP, domain, hash, URL, account), hunt behavioural TTPs from MITRE ATT&CK techniques, run KQL against live data, or check which tables the workspace actually ingests. Discovers available tables first and only generates KQL for tables that verifiably exist, so hunts adapt to each environment's connectors. Commonly chained by the /*-investigation skills to answer "were we exposed?". Reads $SENTINEL_TENANT_ID, $SENTINEL_CLIENT_ID, $SENTINEL_CLIENT_SECRET, $SENTINEL_WORKSPACE_ID. Read-only.

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Use when a report or article needs to be split into individual claims before it is graded, cited or used as evidence — the user asks "what does this report actually claim?", "split this into claims", "separate the facts from the judgements", or another skill needs a claim list. Turns one document into five to twelve atomic claims, each typed (observation, attribution, assessment, actor claim, victim disclosure) and anchored to the verbatim sentence it rests on, so every extraction can be checked against the source. Preserves the source's own hedging. Does not grade. Invoked by /quality-of-information-check; its output feeds /ach, /key-assumptions-check, /intelligence-writing, /writing-assessments and /threat-actor-profiling.

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Agentic workflow patterns for autonomous LLM reasoning. Use when building ReAct agents, implementing reasoning loops, or creating LLMs that plan and execute multi-step tasks.

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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.

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Use when mapping audit targets to appropriate config or domain agents. Analyzes file types, patterns, and content to determine which agents should audit each file/target. Returns agents[] needed for audit workflow.

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Build production-ready AI agents using Google's Agent Development Kit with AI assistant integration, React patterns, multi-agent orchestration, and comprehensive tool libraries. Use when appropriate context detected. Trigger with relevant phrases based on skill purpose.

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Build and review accessible web products using WCAG 2.2 AA. Use when implementing or reviewing forms, dialogs, navigation, keyboard flows, focus management, ARIA, color contrast, responsive UI, or framework-specific accessibility in React/Next.js, Angular, and Vue.

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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.

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Optimize oversized agent instruction files using progressive disclosure. Handles CLAUDE.md (Claude Code), AGENTS.md (OpenAI Codex / GitHub Copilot agents), and copilot-instructions.md (GitHub Copilot). Auto-detects all three formats in your repo, analyzes content tiers, creates sub-documents using each format's native mechanism, and rewrites the main file with zero information loss. Triggers: optimize CLAUDE.md, AGENTS.md too long, reduce copilot-instructions.md, agent instructions file too large, shrink context file, apply progressive disclosure, instructions file bloated

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。