テスト設計、脆弱性診断、品質保証（1.4万 件）
Creates task-oriented technical documentation with progressive disclosure. Use when writing READMEs, API docs, architecture docs, or markdown documentation. Also use this skill at the END of any completed reverse engineering, penetration testing, CTF, or security analysis task to generate a formal report in the user's project directory. Trigger keywords: 写报告, 写文档, 出报告, writeup, 技术文档, report, documentation.
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 for writing, reviewing, debugging, or testing Pandera schemas and runtime validation for Polars DataFrame or LazyFrame pipelines installed with pandera[polars]. Trigger on pandera.polars DataFrameSchema, DataFrameModel, Column, Field, Check, PolarsData, decorators, coercion, strictness, lazy error collection, and validation-depth decisions. Do not use for pandas-backed Pandera, Pydantic object models, Polars transformations without Pandera, static dataframe typing alone, or generic data-quality platforms.
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。
Expert SurrealDB 3 architect and developer skill (tracks v3.1.4+). SurrealQL mastery, multi-model data modeling (document, graph, vector, time-series, geospatial), schema design, security, deployment, performance tuning, SDK integration (JS, Python, Go, Rust, Java, .NET, C, PHP, Swift, Kotlin, Ruby), Surrealism WASM extensions, SurrealML scope coverage (preview), built-in MCP (v3.1+) and standalone SurrealMCP, LangChain Python integration, editor tooling (LSP v0.1.6, tree-sitter, CodeMirror v1.0.6, VS Code/JetBrains/Neovim/Zed), and ecosystem integrations (Surrealist v3.9, Surreal-Sync, SurrealFS, SurrealKit v0.7, n8n, Agent Skills, setup-surreal). Universal skill for 30+ AI agents.
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 QA checklists for verifying design implementation accuracy.
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。
Comprehensive autonomous design quality audit across accessibility (WCAG 2.2), performance, theming, responsive/adaptive design, and anti-pattern detection. Produces a prioritized report with severity ratings and fix recommendations. Use when: 'audit design', 'check accessibility', 'a11y audit', 'design review', 'UI quality check', 'WCAG audit', 'design quality'.
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。
Kaggle-first end-to-end competition workflow for scored submissions. Use when Codex must run Kaggle or competitive ML workflows through scored submission, including code competitions, validation, metrics, policy-safe public notebook/discussion intel, tabular/text/image modeling, tuning, ensembling/stacking, proactive multi-notebook architectures, producer notebooks that train models and export private Kaggle artifact datasets, downstream consumer notebooks, Kaggle GPU offload, kagglehub access, hidden-test debugging, and public score retrieval.
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。
Audit design token usage across a product for consistency and coverage.
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。
Identify, categorize, and prioritize accumulated design inconsistencies and structural problems across a product.
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 cross-platform agent skills from workflow descriptions. Activates when users ask to create an agent, automate a repetitive workflow, create a custom skill, or need advanced agent creation. Triggers on phrases like create agent for, automate workflow, create skill for, every day I have to, daily I need to, turn process into agent, need to automate, create a cross-platform skill, validate this skill, export this skill, migrate this skill, audit this skill, is this skill safe, vet a skill before installing, what does this skill access. Supports single skills, multi-agent suites, transcript processing, template-based creation, interactive configuration, cross-platform export, spec validation, and security auditing of third-party skills before install.
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-powered Claude Code skill that performs deep code review using formal logic and reasoning frameworks to detect bugs, anti-patterns, and security risks beyond what linters catch.
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。
Fast quality gate — detects stack, runs static analysis, checks cross-layer consistency, fixes issues. Run between every pipeline phase, before every commit push, after every feature batch. Lighter than /analyze but catches the same critical issues.
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。
Hardens code against vulnerabilities. Use when handling user input, authentication, data storage, or external integrations. Use when building any feature that accepts untrusted data, manages user sessions, or interacts with third-party services.
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 fix commits address audit findings without new bugs
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。
Security audit of a codebase — web apps, APIs, services, CLI tools, libraries, daemons, and more. Use when asked to find security bugs, do a security review, audit for vulnerabilities, or pen-test the code. Focuses on exploitable issues with real impact, not theoretical concerns or industry-standard behavior.
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 reverse engineering techniques. Use when the main job is to understand how a compiled, obfuscated, packed, or virtualized target works before exploiting or solving it, including binaries, APKs, WASM, firmware, custom VMs, bytecode, malware-like loaders, and anti-debug or anti-analysis logic. Do not use it when the vulnerability is already understood and the remaining task is exploitation; use pwn instead. Do not use it for pure web workflows, log or disk forensics, or standalone crypto problems unless reversing the implementation is the real blocker.
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。
Find bugs, security vulnerabilities, and code quality issues in local branch changes. Use when asked to review changes, find bugs, security review, or audit code on the current branch.
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 for authorized source-code security review and SAST workflows including Semgrep, CodeQL patterns, dangerous API hunting, and fix verification.
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。
Routes reverse engineering, exploitation, penetration testing, malware, mobile, firmware, browser automation, documentation, and security tasks to the appropriate specialist skill. Use when a task spans modules or the correct reverse-skill entrypoint is unclear.
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 for authorized security assessment of REST, GraphQL, WebSocket, or SOAP APIs, including discovery, authentication, authorization, rate-limit, and CI/CD testing.
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。
Audit a website against the Forter Agentic Readiness Guide. Loads the 25 weighted rubrics in `audit/`, probes the target site (and optional source code), scores each guideline Pass/Partial/Fail/N/A with sub-check granularity, and produces a prioritized fix report. Use when a user asks "score my site against the agentic readiness guide", "audit https://… for agent readiness", or "what do I need to fix to be agent-ready".
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。
Native Rust browser automation CLI for AI agents. Use when the user needs to interact with websites — navigating pages, filling forms, clicking buttons, taking screenshots, sharing a live browser view, narrating browser actions, extracting structured data, running assertions, testing web apps, or automating any browser task. Triggers include requests to "open a website", "fill out a form", "click a button", "take a screenshot", "show me the browser", "share the screen", "pause the browser", "step through this", "scrape data from a page", "test this web app", "login to a site", "automate browser actions", "visual regression test", "check for prompt injection", or any task requiring programmatic web interaction.
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。
Patterns and techniques for evaluating and improving AI agent outputs. Use this skill when: - Implementing self-critique and reflection loops - Building evaluator-optimizer pipelines for quality-critical generation - Creating test-driven code refinement workflows - Designing rubric-based or LLM-as-judge evaluation systems - Adding iterative improvement to agent outputs (code, reports, analysis) - Measuring and improving agent response quality
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。
Maxim Brand Intelligence — brand identity, voice architecture, visual identity systems, messaging frameworks, brand consistency enforcement. Activate for brand strategy, brand audits, brand guidelines, tone of voice, asset compliance, color palette, typography specs. Powered by Maxim behavioral science layer.
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。
Delegate tasks to AI agents via Box0. Use when the user asks to review code, check security, run tests, compare tools, get multiple perspectives, research a topic, analyze data, write docs, or any task that could benefit from specialized or parallel execution. Also use when the user mentions agent names or says "ask", "delegate", "get opinions from", or "have someone".
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。