2,002 件 ・ 関連度順
概要と使いどころ
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。
Detect sandbox evasion techniques in malware samples by analyzing timing checks, VM artifact queries, user interaction detection, and sleep inflation patterns from Cuckoo/AnyRun behavioral 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。
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。
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。
Perform cross-artifact consistency analysis across spec.md, plan.md, and tasks.md. Use after task generation to identify gaps, duplications, and inconsistencies before implementation.
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。
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。
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
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。
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。
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。
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。
Jevを使い、日本語中心・英語対応で文書、メール、メッセージ、提案書、技術資料、スライドの抽出テキストを多角的にレビューする。AIっぽさ、定型的な文体、冗長さ、直訳調、過剰敬語などを評価し、根拠引用と具体的な改善案を作成担当者へ返して改善ループを回すときに使う。Japanese-first bilingual editorial style review with Jev scores, source-grounded feedback and bounded revision loops; not an AI-authorship detector.
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。
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。
Verify a tested web agent's real behavior from its recorded Pi or Codex session and selected screenshots before scoring. The judge prompt provides absolute artifact paths; use targeted queries to compare actual outputs, errors, and the final answer with the recorded behavior, then return only the verdict JSON. Only for ego-bench judging—never to drive a browser or rerun the agent.
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。
Mine a codebase for breakthrough, evidence-backed opportunities — features, additions, refactors, simplifications, DX, UX, reliability, performance, or architecture cleanup. Researches project reality first, then forces two escalation gates (reject the merely-adequate idea; expand ambition 10x then collapse to the smallest proof-bearing artifact). Outputs ranked opportunities, an escalation ledger, and one plan seed. Does not implement or create tickets.
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。
Apply a Style to any artifact in the workspace (presentation, card, poster, social post, web page). Use when the user says "apply style X", "render this with style X", or wants to reuse a saved style on a new piece.
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。
Render a Template + data + Style into a final artifact in one of HTML, PDF, PNG, SVG or PPTX. Use whenever the user asks for a concrete deliverable like "make me a poster", "render a card", "give me a one-pager".
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。
Load when invoking the standalone `event-setup` machine CLI to inspect the environment catalogue, validate or render dotenv input, process configuration manifests or tenant packages, export/import operator-identity portability artifacts, validate legal drafts, or run setup diagnostics; do not use for the interactive terminal UI, browser setup, or API endpoint 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。
Load before external product/design research, competitor comparison, clean-room implementation, or adding/updating code, packages, images, fonts, datasets, generated artifacts, or other dependencies whose source/provenance/license may affect outbound licensing; not for purely internal source use with no dependency change.
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。
Refresh `memory/SPEC.md` and `memory/PLAN.md` in mature mode — restore canonical truth, archive retired plan history, delete stale derivative artifacts, and flag drift against code.
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。