「exam」の検索結果

This skill should be used when the user asks to "identify web application vulnerabilities", "explain common security flaws", "understand vulnerability categories", "learn about injection attacks", "review access control weaknesses", "analyze API security issues", "assess security misconfigurations", "understand client-side vulnerabilities", "examine mobile and IoT security flaws", or "reference the OWASP-aligned vulnerability taxonomy". Use this skill to provide comprehensive vulnerability definitions, root causes, impacts, and mitigation strategies across all major web security categories.

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。

Curated collection of high-quality prompts for various use cases. Includes role-based prompts, task-specific templates, and prompt refinement techniques. Use when user needs prompt templates, role-play prompts, or ready-to-use prompt examples for coding, writing, analysis, or creative tasks.

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。

Generate comprehensive, developer-friendly API documentation from code, including endpoints, parameters, examples, and 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。

Use when enforcing Axel's coding standards across any language for architecture, reuse, imports, and maintainability, plus React/Next.js-specific rules only where explicitly stated (for example useEffect and JSX handlers).

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 comprehensive, developer-friendly API documentation from code, including endpoints, parameters, examples, and 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。

Provides AI and machine learning techniques for CTF challenges. Use when attacking ML models, crafting adversarial examples, performing model extraction, prompt injection, membership inference, training data poisoning, fine-tuning manipulation, neural network analysis, LoRA adapt

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。

Pitfalls, examples, and edge cases for FETIH Kanban workers. The lifecycle itself is auto-injected into every worker's system prompt as KANBAN_GUIDANCE (from agent/prompt_builder.py); this skill is what you load when you want deeper detail on specific scenarios.

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 to start a new product on Cradle or to add the Cradle skeleton to an existing repository — writes cradle.json, the project-facts instruction (.apm/instructions/project.instructions.md), documents/{ddd,ai-notes,infra-design,codebase} templates, CI, and a compiling Lean executable-spec scaffold with a minimal example domain, mockup and CLI.

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。

Add GitHub permalinks with fixed SHA/commit references to research documentation containing code snippets. Use when writing technical docs with code examples, creating investigation/research docs that reference source files, user asks to "add permalinks" or "add GitHub links", documenting code research findings with file:line references, or creating audit trails needing permanent code references. Triggers on "add github links", "add permalinks", "link to source", "make code references permanent".

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。

入試の志望理由書や総合型選抜書類に生成 AI を使った場合の扱いを決めたい、 出願要項に AI 使用可否をどう書くか悩む、AI 検知ツール導入を検討している、 志望理由書に AI 使用の疑義が生じた時の判断基準を整備したい、 不正認定と教育機会のどちらで運用するか迷う場面で使う入試部門職員向けスキル。 文科省通知を骨格に、3 類型判断・二重確認プロセス・運用語彙選択・ツール未導入大学向け代替案を提供する。

変更内容を確認し、論理的な単位で複数のコミットに分けて実行します

Curate Busabase training examples, create immutable dataset snapshots, run reproducible LoRA or QLoRA fine-tunes on a local Apple Silicon Mac with MLX-LM, compare the untouched baseline and adapter on locked evaluations, and register approved adapters. Use when the user wants to train, fine-tune, evaluate, compare, promote, or manage a local language model with Busabase as the workflow source of truth. Do not use for RAG-only knowledge retrieval, cloud training, or training a foundation model from scratch.

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。

Kelly Education Intel: App-in-Skill daily industry intelligence cockpit for education, training, tutoring, and admissions services (Busabase App-in-Skill). Use when the user asks about education, training centers, exams, study abroad, visas, school policy, parent FAQ,招生,升学, or教育机构 sales scenes. Prepares news/source signals, buyer-intent interpretation, approved sales actions, and channel drafts for review before any external handoff.

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。

Friendly elementary-school homework tutoring App-in-Skill. Use when the user invokes $kelly-homework-coach or /kelly-homework-coach, mentions 小学生作业辅导, homework photo tutoring, 拍照讲题, 作业答疑, 错题本, error notebook, mistake-book review, practice paper generation, exam paper analysis, parent/teacher review of AI explanations, or wants a child-friendly UI for step-by-step homework help with parent/teacher review and safety boundaries.

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 speakers, talks, schedules, or CFP dates for any Devoxx or VoxxedDays conference. Covers: speaker bios/socials, talk topics/tracks/levels, daily program or room agenda, most popular talks, upcoming or past events, and CFP open/close dates. Queries the public CFP API at [event-slug].cfp.dev and the devoxxians.com event registry. Example triggers: who speaks about Kubernetes at Devoxx UK, what is on Wednesday in Room 8, find AI talks at Voxxed Zurich, when does the CFP open for Devoxx Belgium.

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 fileless malware that operates entirely in memory using PowerShell, WMI, .NET reflection, registry-resident payloads, and living-off-the-land binaries (LOLBins) without writing traditional executable files to disk. Activates for requests involving fileless threat detection, in-memory malware investigation, LOLBin abuse analysis, or WMI persistence examination.

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。

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 malicious VBA macros embedded in Microsoft Office documents (Word, Excel, PowerPoint) to identify download cradles, payload execution, persistence mechanisms, and anti-analysis techniques. Uses olevba, oledump, and VBA deobfuscation to extract the attack chain. Activates for requests involving Office macro analysis, VBA malware investigation, maldoc analysis, or document-based threat examination.

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 Linux system artifacts including auth logs, cron jobs, shell history, and system configuration to uncover evidence of compromise or unauthorized activity.

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 the NTFS Master File Table ($MFT) to recover metadata and content of deleted files by examining MFT record entries, $LogFile, $UsnJrnl, and MFT slack space using MFTECmd, analyzeMFT, and X-Ways Forensics.

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 RAM memory dumps from compromised systems using the Volatility framework to identify malicious processes, injected code, network connections, loaded modules, and extracted credentials. Supports Windows, Linux, and macOS memory forensics. Activates for requests involving memory forensics, RAM analysis, volatile data examination, process injection detection, or memory-resident malware 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。

Required reading before writing any HogQL/SQL or calling execute-sql against PostHog. Use whenever the user wants to search, find, or do complex aggregations PostHog entities (insights, dashboards, cohorts, feature flags, experiments, surveys, hog flows, data warehouse, persons, etc.) and query analytics data (trends, funnels, retention, lifecycle, paths, stickiness, web analytics, error tracking, logs, sessions, LLM traces). Covers HogQL syntax differences from ClickHouse SQL, system table schemas (system.*), available functions, query examples, and the schema-discovery workflow.

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。