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概要と使いどころ
Create distinctive, production-grade frontend interfaces with high design quality. Generates creative, polished code that avoids generic AI aesthetics. Use when the user asks to build web components, pages, artifacts, posters, or applications, or when any design skill requires project context.
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 Container Registry SDK for Python. Use for managing container images, artifacts, and repositories. Triggers: "azure-containerregistry", "ContainerRegistryClient", "container images", "docker registry", "ACR".
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 process injection techniques used by malware including classic DLL injection, process hollowing, APC injection, thread hijacking, and reflective loading. Uses memory forensics, API monitoring, and behavioral analysis to identify injection artifacts. Activates for requests involving process injection detection, code injection analysis, hollowed process investigation, or in-memory threat 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。
Detect NTLM relay attacks through Windows Security Event correlation by analyzing Event 4624 LogonType 3 for IP-to-hostname mismatches, identifying Responder/LLMNR poisoning artifacts, auditing SMB and LDAP signing enforcement across the domain, and detecting NTLM downgrade attacks from NTLMv2 to NTLMv1 using event log 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。
Parses and analyzes the Windows Amcache.hve registry hive to extract evidence of program execution, application installation, and driver loading for digital forensics investigations. Uses Eric Zimmerman's AmcacheParser and Timeline Explorer for artifact extraction, SHA-1 hash correlation with threat intel, and timeline reconstruction. Activates for requests involving Amcache forensics, program execution evidence, Windows artifact analysis, or application compatibility cache 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。
Examine file system slack space, MFT entries, USN journal, and alternate data streams to recover hidden data and reconstruct file activity on NTFS volumes.
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。
Captures and analyzes network packet data using Wireshark and tshark to identify malicious traffic patterns, diagnose protocol issues, extract artifacts, and support incident response investigations on authorized network segments.
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。
Parse Windows Prefetch files to determine program execution history including run counts, timestamps, and referenced files for forensic 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。
Extract and analyze Windows Registry hives to uncover user activity, installed software, autostart entries, and evidence of system compromise.
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。
Investigate compromised Docker containers by analyzing images, layers, volumes, logs, and runtime artifacts to identify malicious activity and evidence.
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。
Perform comprehensive forensic analysis of disk images using Autopsy to recover files, examine artifacts, and build investigation timelines.
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 indicators of compromise (IOCs) including IP addresses, domains, file hashes, URLs, and email artifacts to determine maliciousness confidence, campaign attribution, and blocking priority. Use when triaging IOCs from phishing emails, security alerts, or external threat feeds; enriching raw IOCs with multi-source intelligence; or making block/monitor/whitelist decisions. Activates for requests involving VirusTotal, AbuseIPDB, MalwareBazaar, MISP, or IOC enrichment 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。
Convert existing tasks into actionable, dependency-ordered GitHub issues for the feature based on available design artifacts.
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 an actionable, dependency-ordered tasks.md for the feature based on available design artifacts.
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。
Execute the implementation planning workflow using the plan template to generate design artifacts.
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。
Explicit-only request to restate the previous explanation plainly in the current conversation language while preserving facts and uncertainty.
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。
Download and verify the seven platform FFmpeg archives produced for one rust-ffmpeg-splitter commit by GitHub Actions and CircleCI. Use when handing completed splitter CI binaries to rust-ffmpeg-sys without mixing commits or incomplete CI runs.
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。
Cross-reference project artifacts (WBS, meeting minutes, requirements docs) by date, participant, and action items. Extract commitments from meeting minutes and link to WBS tasks. Generate traceability reports showing which decisions were captured in which documents.
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 creating comprehensive project plans for system development or implementation projects. Use this skill when you need to create Project Charters, Project Plans, WBS, Gantt Charts, RACI matrices, Risk analysis, or other PMBOK-aligned project management artifacts. Outputs are in Markdown format with Mermaid diagrams for visualizations. Leverages comprehensive PM knowledge base in references/ directory (544KB, 11 files) including PMBOK standards, Software/SaaS best practices, IT industry guidance, and specialized PM methodologies.
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 pi (coding-agent) session JSONL and ego-benchmark-harness run artifacts to explain slow or failed individual tasks, compare execution-path differences between agents or browser tools, and count tool calls and error types. Use this skill whenever users ask to analyze a pi session, explain why a task was slow or failed, compare two runs, investigate an error, locate the session corresponding to tasks.jsonl, inspect an agent execution path, count tool calls, analyze a run ID, or perform any forensic or retrospective analysis of JSONL under ~/.pi/agent/sessions/ or agent-home/.pi/agent/sessions/.
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 Codex provider rollout JSONL and ego-benchmark-harness run artifacts to identify slow or failed individual tasks, tool retries, token/cost anomalies, and differences in agent execution paths. Use when users ask to analyze a Codex session or rollout, explain why a Codex task was slow or failed, inspect a Codex provider execution trace, compare Codex runs, count function_call or tool output events, or investigate agent-home/.codex/sessions.
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。
Compile deep repository evidence into artifact-bound correctness doctrine — authority/law/proof maps, owned invariants, failure archaeology, and the strongest durable destination for each piece of knowledge, plus an optional minimal repository-specific skill portfolio. Use when the user wants both deep codebase understanding AND durable doctrine, knowledge routing, or repo-specific skill recommendations. Research discoverable facts before asking. Read-only. Not for quick onboarding, one isolated invariant, ordinary implementation, generic review, or direct skill creation.
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。
Review public repository changes for secrets, private paths, signing identities, bundle IDs, generated artifacts, local logs, and private workflow leaks.
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。