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概要と使いどころ
A pessimistic go/no-go the moment you pick up a GitHub issue — read it skeptically and hunt for reasons NOT to do it (already done? obsolete? duplicate? premise wrong? cheaper way? net-negative?), returning proceed / reshape / drop with one-line evidence. The pickup-time half of the dedup gate (the create-time half is /issue-dedup). Use as step 1 of the Task Execution Workflow, or run /issue-sanity-check
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。
Deploy a crouton app to Cloudflare Workers PRODUCTION (<app>.friendlyinter.net). A deliberate, human-initiated action — separate from the default staging /deploy skill. Use ONLY when explicitly asked to ship/release to production. Never run as part of routine work or an automated flow.
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 ruPet as a practical companion for Ruflo and ruvnet tool workflows, or improve ruPet creation and repair prompts. Use for requests that name ruPet or ask for its tool-aware guidance; ordinary tasks and merely displaying the pet do not need this skill.
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。
Solves complex, open-ended problems using an evolutionary search mechanism inspired by genetic algorithms. Generates a diverse population of candidate answers, scores them against a rubric, then iteratively applies selection, crossover, and mutation to breed progressively better solutions. Use this skill whenever the user wants to explore a solution space deeply, asks for the "best" answer to a subjective or multi-dimensional problem, wants to evaluate competing approaches and distill the strongest, or uses any of these triggers: "evolutionary search", "genetic algorithm", "evolve an answer", "breed solutions", "population-based search", or phrases like "give me multiple approaches and refine the best one". Also trigger when the problem is complex, open-ended, or has no obvious single correct answer and the user wants a high-quality, well-explored result, not just a quick response.
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。
Chrome DevTools MCPを使ってWeb performanceを分析する。Core Web Vitals（LCP、INP、CLS）とFCP、TBT、Speed Indexを計測し、render-blocking resources、network dependency chains、layout shifts、cache、accessibilityの問題を特定する。表示速度の監査・計測・デバッグ・最適化、Lighthouse scoreの改善で使用する。既定スタックの必須スキルではないoptional群であり、要件で明示された場合、またはapp-orchestratorがv1前の計測として指示した場合のみロードする。事前学習の知識より最新の公式ドキュメントを優先する。
Azure AI Document Intelligence SDK for .NET. Extract text, tables, and structured data from documents using prebuilt and custom models. Use for invoice processing, receipt extraction, ID document analysis, and custom document models. Triggers: "Document Intelligence", "DocumentIntelligenceClient"...
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。
Azure AI Voice Live SDK for .NET. Build real-time voice AI applications with bidirectional WebSocket communication. Use for voice assistants, conversational AI, real-time speech-to-speech, and voice-enabled chatbots. Triggers: "voice live", "real-time voice", "VoiceLiveClient", "VoiceLiveSession"...
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。
Azure AI Projects SDK for .NET. High-level client for Azure AI Foundry projects including agents, connections, datasets, deployments, evaluations, and indexes. Use for AI Foundry project management, versioned agents, and orchestration. Triggers: "AI Projects", "AIProjectClient", "Foundry project"...
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。
Azure AI Agents Persistent SDK for .NET. Low-level SDK for creating and managing AI agents with threads, messages, runs, and tools. Use for agent CRUD, conversation threads, streaming responses, function calling, file search, and code interpreter. Triggers: "PersistentAgentsClient", "persistent a...
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, create, and configure production-ready Azure Kubernetes Service (AKS) clusters. Covers Day-0 checklist, SKU selection (Automatic vs Standard), networking options (private API server, Azure CNI Overlay, egress configuration), security, and operations (autoscaling, upgrade strategy, cost anal...
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。
Detect lateral movement in network traffic using Zeek (formerly Bro) log analysis. Parses conn.log, smb_mapping.log, smb_files.log, dce_rpc.log, kerberos.log, and ntlm.log to identify SMB file transfers, NTLM account spray activity, remote service execution, and anomalous internal connections.
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。
Detects and analyzes malicious behavior in mobile applications through behavioral analysis, permission abuse detection, network traffic monitoring, and dynamic instrumentation. Use when analyzing suspicious mobile applications for data exfiltration, command-and-control communication, credential stealing, SMS interception, or other malware indicators. Activates for requests involving mobile malware analysis, app behavior monitoring, trojan detection, or suspicious app investigation.
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。
Detect unauthorized SaaS and cloud service usage (shadow IT) by analyzing proxy logs, DNS query logs, and netflow data using Python pandas for traffic pattern analysis and domain classification.
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。
Write and deploy Falco rules with the modern eBPF driver to detect container escape, namespace abuse, privileged mounts, and anomalous syscalls at runtime in Kubernetes and Docker.
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。
Detect and prevent ARP spoofing attacks using ARPWatch, Dynamic ARP Inspection, Wireshark analysis, and custom monitoring scripts to protect against man-in-the-middle interception.
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 covers deploying anomaly detection systems for industrial control environments using machine learning models trained on OT network baselines, physics-based process models, and behavioral analysis of industrial protocol communications. It addresses building normal behavior profiles for SCADA polling patterns, detecting deviations in Modbus/DNP3/OPC UA traffic, identifying rogue devices, and correlating network anomalies with physical process data from historians.
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。
Deploying Palo Alto Networks Prisma Access for SASE-based zero trust network access using GlobalProtect agents, ZTNA Connectors, security policy enforcement, and integration with Strata Cloud Manager for unified security management.
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 statistical analysis of Zeek conn.log connection intervals to detect C2 beaconing patterns. Uses the ZAT library to load Zeek logs into Pandas DataFrames, calculates inter-arrival time standard deviation, and flags periodic connections with low jitter. Use when hunting for command-and-control callbacks in network 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。
Deploy a Software-Defined Perimeter using the CSA v2.0 specification with Single Packet Authorization, mutual TLS, and SDP controller/gateway configuration to enforce zero trust network access.
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。
Configure microsegmentation policies to enforce least-privilege workload-to-workload access using tools like VMware NSX, Illumio, and Calico, preventing lateral movement in zero trust architectures.
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 execute a social engineering penetration test including phishing, vishing, smishing, and physical pretexting campaigns to measure human security resilience and identify training gaps.
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。
Simulates man-in-the-middle attacks using Ettercap, mitmproxy, and Bettercap in authorized environments to intercept, analyze, and modify network traffic for testing encryption enforcement, certificate validation, and detection capabilities.
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 outlines methodologies for performing authorized penetration testing against AWS, Azure, and GCP cloud environments. It covers understanding the shared responsibility model for testing scope, leveraging cloud-specific attack tools like Pacu and ScoutSuite, exploiting IAM misconfigurations, testing for SSRF to cloud metadata services, and reporting findings aligned to MITRE ATT&CK Cloud matrix.
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。
Detect and analyze covert communication channels used by malware including DNS tunneling, ICMP exfiltration, steganographic HTTP, and protocol abuse for C2 and data exfiltration.
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。