3.2万 件 ・ 関連度順
Apply the Opquast Digital Quality Framework (245 rules, 14 categories) when building, reviewing, or auditing websites and web applications. Use for web development quality assurance, accessibility compliance, security hardening, privacy implementation, e-commerce best practices, and holistic digital quality checks.
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Comprehensive guide for configuring and managing GitHub Dependabot. Use this skill when users ask about creating or optimizing dependabot.yml files, managing Dependabot pull requests, configuring dependency update strategies, setting up grouped updates, monorepo patterns, multi-ecosystem groups, security update configuration, auto-triage rules, or any GitHub Advanced Security (GHAS) supply chain security topic related to Dependabot. For pre-commit dependency vulnerability scanning in AI coding agents via the GitHub MCP Server, this skill references the Advanced Security plugin (`advanced-security@copilot-plugins`). Use this skill when an agent needs to scan dependencies for known vulnerabilities before committing.
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AI-powered codebase security scanner that reasons about code like a security researcher — tracing data flows, understanding component interactions, and catching vulnerabilities that pattern-matching tools miss. Use this skill when asked to scan code for security vulnerabilities, find bugs, check for SQL injection, XSS, command injection, exposed API keys, hardcoded secrets, insecure dependencies, access control issues, or any request like "is my code secure?", "review for security issues", "audit this codebase", or "check for vulnerabilities". Covers injection flaws, authentication and access control bugs, secrets exposure, weak cryptography, insecure dependencies, and business logic issues across JavaScript, TypeScript, Python, Java, PHP, Go, Ruby, and Rust.
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Check any AI agent codebase against the OWASP Agentic Security Initiative (ASI) Top 10 risks. Use this skill when: - Evaluating an agent system's security posture before production deployment - Running a compliance check against OWASP ASI 2026 standards - Mapping existing security controls to the 10 agentic risks - Generating a compliance report for security review or audit - Comparing agent framework security features against the standard - Any request like "is my agent OWASP compliant?", "check ASI compliance", or "agentic security audit"
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Protocol for auditing API surface coherence and type safety. Trigger when: - Evaluating API designs, interface type safety, or design elegance. - Prompt contains: /api-audit, API surface, API coherence, type safety.
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Idiomatic programming style, patterns, and conventions for TypeScript and JavaScript development. Trigger when: - Writing, refactoring, reviewing, or debugging TypeScript or JavaScript code. - Files matching the patterns **/*.ts, **/*.tsx, **/*.js, **/*.jsx, package.json are in the workspace or referenced. - Tasks involve: tsc, npm, yarn, pnpm, bun, eslint, prettier, vite, next.js. - Prompt contains keywords: typescript, ts, js, javascript, interface, type, async, await, promise, es6, node, deno.
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Core technical, safety, and maintainability rules for codebase edits. Trigger when: - Writing code, refactoring, modifying files, or creating unit tests. - Proposing commits, reviewing git history, or auditing code quality. - Modifying files in any programming language (e.g. *.go, *.rs, *.py, *.ts, *.js, *.c, *.cpp). - Prompt contains keywords: refactor, build, compile, lint, test, git, commit, rule priority, type safety, error handling, defensive programming.
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。
Improves typography by fixing font choices, hierarchy, sizing, weight, and readability so text feels intentional. Use when the user mentions fonts, type, readability, text hierarchy, sizing looks off, or wants more polished, intentional typography.
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。
Convex is the backend agents get right on the first try: an all-TypeScript reactive platform where the database, server functions, scheduling, file storage, auth, and realtime sync are one type-safe system, every function is a transaction, and `tsc` catches most mistakes before deploy. Ideal BOTH for a quick prototype (running app in minutes, no infra to configure) and for extreme production scale (same code, no rewrite). Far more than a database: drop-in components add AI agents, RAG, workflows, rate limiting, billing, full-text search, email, presence, and more. Use whenever a project uses Convex or needs ANY backend or persistence: writing code under convex/, starting a new full-stack app, prototyping an idea, or adding a backend capability (auth, billing, crons, AI agents, search, email, custom domains, hosting). Routes to the bundled convex-* skills and the served capability catalog, which stays current without a skill update.
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。
Guide for when to use and when not to use return validators in Convex functions. Use this skill whenever the user is writing Convex queries, mutations, or actions and needs guidance on return value validation. Also trigger when the user asks about Convex type safety, runtime validation, AI-generated Convex code, Convex AI rules, Convex security best practices, or when they're debugging return type issues in Convex functions. Trigger this skill when users mention "validators", "returns", "return type", or "exact types" in the context of Convex development. Also trigger when writing or reviewing Convex AI rules or prompts that instruct LLMs how to write Convex 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。
Improves typography by fixing font choices, hierarchy, sizing, weight, and readability so text feels intentional. Use when the user mentions fonts, type, readability, text hierarchy, sizing looks off, or wants more polished, intentional typography.
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。
Guide for when to use and when not to use return validators in Convex functions. Use this skill whenever the user is writing Convex queries, mutations, or actions and needs guidance on return value validation. Also trigger when the user asks about Convex type safety, runtime validation, AI-generated Convex code, Convex AI rules, Convex security best practices, or when they're debugging return type issues in Convex functions. Trigger this skill when users mention "validators", "returns", "return type", or "exact types" in the context of Convex development. Also trigger when writing or reviewing Convex AI rules or prompts that instruct LLMs how to write Convex 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。
Convex is the backend agents get right on the first try: an all-TypeScript reactive platform where the database, server functions, scheduling, file storage, auth, and realtime sync are one type-safe system, every function is a transaction, and `tsc` catches most mistakes before deploy. Ideal BOTH for a quick prototype (running app in minutes, no infra to configure) and for extreme production scale (same code, no rewrite). Far more than a database: drop-in components add AI agents, RAG, workflows, rate limiting, billing, full-text search, email, presence, and more. Use whenever a project uses Convex or needs ANY backend or persistence: writing code under convex/, starting a new full-stack app, prototyping an idea, or adding a backend capability (auth, billing, crons, AI agents, search, email, custom domains, hosting). Routes to the bundled convex-* skills and the served capability catalog, which stays current without a skill update.
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 teaches security teams how to deploy and operationalize Amazon GuardDuty for continuous threat detection across AWS accounts and workloads. It covers enabling protection plans for S3, EKS, EC2 runtime monitoring, and Lambda, interpreting finding severity levels, and building automated response workflows using EventBridge and Lambda.
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Responds to security incidents in cloud environments (AWS, Azure, GCP) by performing identity-based containment, cloud-native log analysis, resource isolation, and forensic evidence acquisition adapted for ephemeral cloud infrastructure. Activates for requests involving cloud incident response, AWS security incident, Azure compromise, GCP breach, cloud forensics, or cloud identity compromise.
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Pre-deployment security audit of Solidity smart contracts in a Foundry project. Combines static analysis (Slither, Aderyn), symbolic execution (Mythril), and property-based testing (forge fuzz + invariant tests with handlers) to catch reentrancy, access-control, oracle/price manipulation, and arithmetic bugs BEFORE deploying to an EVM chain. Also enforces key hygiene (no plaintext private keys, encrypted cast keystore) and a secure deploy workflow. Use when writing, reviewing, testing, or deploying Solidity/Foundry contracts, building a dApp, or working with forge/cast/anvil, MetaMask, or Web3/DeFi code.
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Pre-fills a customer's or prospect's security or third-party risk questionnaire from the organisation's own answer sources (prior questionnaires, answer library, policies), returning one row per question with a draft answer marked Reused, Adapted, Missing or Routed, its source, date, scope match and owner, plus the Missing list by owner. Never invents an answer, certification or control and never submits. Use when the user asks to "pre-fill this security questionnaire", "take a first pass at the customer's due diligence form", "answer this vendor assessment from our previous responses", "populate the third-party risk questionnaire" or "which questions can we reuse answers for". Do not use for screening a vendor's own answers, use vendor-risk-screening-brief instead; for RFP or tender answers use rfp-response-drafter. Drafts for human review; never approves, authorises or signs off.
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Reviews one deal's notes and timeline (CRM opportunity record, activity log, emails, meeting notes, forecast entries) for risk signals such as a single contact thread, no identified budget owner, slipped or missing dates, silence after a proposal, an undefined decision process or a competitor named late, and returns a DRAFT risk register: each signal with the quoted evidence and its date, a severity on a stated scale, and one proposed next action with an owner role for the user to decide on. Use when the user asks to "review the risks on this deal", "why is this opportunity stalling", "is this deal single-threaded", "deal health check before the forecast call" or "what should we do next on <opportunity>". Do not use for a whole-account plan, use account-plan-builder instead; for scoring early leads against a rubric use lead-qualification-scorer. Drafts for human review; never approves, authorises or signs off.
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Compares one clause (liability, termination, confidentiality or any other) across several contracts, templates or successive versions and returns a DRAFT comparison table: verbatim clause text per document, the clause split into elements with every difference from the baseline highlighted and categorised, and neutral questions for counsel. Never says which wording is better or enforceable. Use when the user asks to "compare the liability caps across these five supplier contracts", "line up the termination clause in these three drafts", "show how the confidentiality clause changed between versions", "contrast this clause with our template" or "benchmark the indemnity wording across our customer agreements". Do not use for a whole-contract review, use contract-review-pack instead; for screening one NDA against standard positions, use nda-triage. Drafts for human review; never approves, authorises or signs off.
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Reviews a proposal, business case, deck or plan in character as a Chief Technology Officer archetype and returns a DRAFT review in the chat with a verdict, findings cited to the exact passage, technology risks (architecture, build versus buy, vendor lock-in, security of design, engineering capacity), what would change the verdict and five interrogation questions. Use when the user asks to "run a CTO review", "pressure-test the technical side of this plan", "what would a CTO ask about this" or "rehearse the technology seat before the review". Do not use for cash, payback or budget questions, use cfo-reviewer instead; for capacity and timeline questions, use coo-reviewer; for security controls, privacy or compliance, use ciso-reviewer. Drafts for human review; never approves, authorises or signs off.
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Reviews a proposal, business case, deck or plan in character as a Chief Data Officer archetype and returns a DRAFT review in the chat with a verdict, findings that cite the exact passage, data and AI governance risks, what would change the verdict and five interrogation questions. Use when the user asks to "run a CDO review", "pressure-test the data governance in this plan", "what would a CDO say about this", "check the metric definitions and data sources" or "prepare this for the data governance board". Do not use for security controls or third-party security risk, use ciso-reviewer instead; for contract, liability or regulatory terms, use general-counsel-reviewer. Drafts for human review; never approves, authorises or signs off.
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。
Flags where a P&ID draft raises a process safety question and points to the candidate evidence an engineer would review (HAZOP register, relief study, SIL assessment, isolation philosophy), as questions and quoted passages with UNKNOWN for missing documents, plus safety functions, relief devices and isolation requirements carried over as stated. Never rates, sizes, classifies, resolves or declares protection adequate. Use when the user asks to "write the process safety flags", "produce the process safety section of the P&ID enrichment", "list which protection questions this P&ID raises" or "map the HAZOP and relief study evidence to the draft" after gate G2, once the piping and instrumentation sections exist. Do not use for proposing loops, alarms or trips from the control philosophy, use instrumentation-and-control-enrichment instead; do not use for checking identifiers, use tagging-and-numbering. Drafts for human review; never approves, authorises or signs off.
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。
4人の異なる専門家ペルソナ（20年ベテランエンジニア、TDDエキスパート、Clean Codeエキスパート、バグハンター）で ソースコードを批判的にレビューするスキル。設計判断、テスト容易性、可読性、バグ検出の観点から コードの問題を検出し、改善提案を行う。Python/JavaScriptに対しては追加のチェックポイントを適用。 バグハンターは状態遷移、例外パス、依存関係、非同期競合に特化し、本番環境での問題を事前に検出する。 Use when reviewing source code from multiple expert perspectives to find design flaws, testability issues, code quality problems, and runtime bugs. Triggers: "critical code review", "multi-persona code review", "expert code review", "code quality assessment", "find bugs".
Audit a dataset or access rule before it joins an identity-graph build (access rules behave like datasets in NQL here). Enumerates failure modes (hub identifiers, high-degree nodes, suspicious values, over-connected identifiers), tests hypotheses in parallel, quantifies damage by rows / edges / entities, and proposes minimal filters ranked by severity. When issues are found, returns a validated `CREATE MATERIALIZED VIEW` NQL the caller can run to produce a graph-ready clean source; if the data passes, says so and recommends it unchanged. Plans and authors the clean-view NQL; does not execute it. Use when: "audit this dataset before the graph build", "find bad edges in <source>", "check identity data quality", "recommend filters for the graph build", "quantify damage from <identifier_type>", "pre-graph DQ". (narrative-identity)
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。