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概要と使いどころ
Use when planning, drafting, revising, compressing, or auditing conference rebuttals and author responses in OpenReview Markdown or PDF. Supports exact reviewer mapping, evidence and experiment integration, guided risk decisions, venue-format validation, and blind reviewer/AC audits.
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 preparing a desktop or web app update for commit, packaging, installer validation, GitHub publication, release asset upload, or old-release pruning. Protects user data and verifies that published artifacts match the tested build.
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, build, scaffold, or safely extend Atlassian Forge apps using current official documentation. Use for fresh Forge apps, existing-app feature work, module and manifest changes, UI Kit or Custom UI implementation, backend functions and events, Atlassian or external APIs, storage, permissions, environment configuration, distribution-affecting implementation, validation, and explicitly authorized deployment or installation; route standalone debugging and specialist reviews to their dedicated skills.
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 the Crabbox wrapper for OpenClaw remote validation across Linux, macOS, Windows, and WSL2, including delegated Blacksmith Testbox proof. Report the actual provider and id.
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 the Crabbox wrapper for OpenClaw remote validation across Linux, macOS, Windows, and WSL2, including delegated Blacksmith Testbox proof. Report the actual provider and id.
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。
Purple team automation skill that links offensive emulation (Atomic Red Team, Caldera, Stratus Red Team, Leonidas) with detection validation, telemetry-gap analysis, and continuous control testing. Use to design adversary emulation plans tied to MITRE ATT&CK, execute safe tests in lab or controlled environments, and produce evidence-backed detection coverage 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。
Detection engineering skill for writing, testing, and tuning Sigma, YARA, YARA-L, Suricata, Snort, Zeek, KQL, SPL, EQL, Chronicle, and Elastic detection rules. Covers ATT&CK coverage mapping, telemetry gap analysis, false-positive tuning, detection-as-code pipelines, and validation with Atomic Red Team and Caldera. Use to design durable detections backed by tests.
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。
Authorized exploit development and vulnerability research skill for lab environments, CTFs, owned software, crash analysis, memory corruption, exploitability assessment, fuzzing results, proof-of-concept design, and remediation. Use for defensive validation and education; avoid real-world weaponization, stealth, persistence, evasion, or unauthorized targets.
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。
Authorized offensive security skill for ethical penetration testing, red-team planning, vulnerability validation, reconnaissance of owned assets, web/API/mobile/cloud assessment, report writing, remediation guidance, and lab-based security testing. Use only for systems the user owns or is authorized to test; focus on safe, scoped, legal assessment and defensive improvement.
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。
Curate, review, or repair ai-gene-review ModuleReview YAML documents under modules/, including pathway/module boundary setting, parts and variant modeling, annoton-level molecular functions, representative UniProt/PTN grounding, module deep-research provenance, validation, rendering, and project batch updates.
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。
Customize a new project created from this Flutter template, including app identity, package name, platform cleanup, icons, splash screen, Firebase/secrets decisions, setup tool execution, code generation, and validation.
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, inspect, analyze, and edit spreadsheets (`.xlsx`, `.xlsm`, `.csv`, `.tsv`) with local code execution. Use for formulas, formatting, charts, validation, financial models, and spreadsheet deliverables.
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 validating and serializing Python data with Pydantic v2 - models, field and model validators, constrained and special types, TypeAdapter, discriminated unions, BaseSettings, or FastAPI integration
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 implementing PyQt/PySide6 dialogs - standard dialogs (QFileDialog, QMessageBox, QInputDialog, QColorDialog, QFontDialog), custom QDialog patterns, modal vs modeless dialogs, input validation, Qt 6 dialog APIs
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。
Implements features in vertical slices, always starting with homepage. Enforces anti-placeholder rules — no lorem ipsum, no TODO stubs, no gray boxes. Real content, real images, real interactions. TypeScript strict mode, Zod validation, and structured file organization.
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。
Type-safe client-side routing for React with file-based routes, search params validation, loaders, and code splitting
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。
SWR data fetching patterns — keys and fetchers, isLoading vs isValidating, revalidation strategy, useSWRMutation, useSWRInfinite, conditional fetching
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。
tRPC type-safe API patterns — routers and procedures, input validation, context and middleware, TRPCError, and the typed client bridge
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。
File upload patterns - drag-drop dropzones, chunked and resumable uploads, presigned URL flows, file validation (MIME type, magic bytes), progress tracking, accessibility (ARIA)
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。
VeeValidate v4 patterns - useForm, useField, defineField, useFieldArray, schema validation with Composition API
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。
React Hook Form patterns - useForm, Controller, useFieldArray, validation resolver, performance optimization
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。
Next.js App Router patterns (15-16) - file-based routing, Server/Client Components, streaming, Suspense, metadata API, parallel routes, Server Actions, mutations, revalidation, Cache Components
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。
Frontend specification planning frameworks. Use when a spec touches UI components, forms, client state, or user-facing flows. Covers UI-state completeness (loading, error, empty, success), component boundaries, form validation contracts, state ownership, and measurable UI success criteria.
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。
AI integration review patterns. Use when reviewing model API calls, prompt construction, LLM output handling, RAG pipelines, and tool-calling code. Covers prompt-injection call-chain tracing, output validation, token budgets, retry/timeout handling, streaming, and key/PII exposure.
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。