「uat」の検索結果

Sets and reviews how text renders in your product, from the type scale and spacing to font features, wrapping, truncation and punctuation.

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 this skill when you need to control a Chrome browser via CDP (Chrome DevTools Protocol) to reuse existing login sessions. Covers: launching Chrome in debug mode, opening URLs, waiting for page load, evaluating JavaScript, taking snapshots, and extracting auth tokens. Trigger phrases: browser automation, CDP, agent-browser, 浏览器操作, 操作浏览器, Chrome CDP, 复用登录态, extract token from browser.

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 the user asks to "triage a vulnerability report", "assess a CVE", "evaluate a bug bounty submission", "decide if a finding is valid", "review a security finding", "dismiss a vulnerability", "should we fix this CVE", "prioritize a vulnerability report", or needs to determine whether an incoming vulnerability report warrants investigation. Applies 7 brocards (rules of thumb) to systematically accept, dismiss, or request more information on vulnerability reports, or needs to filter raw findings from agentic vulnerability discovery pipelines before human review.

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。

Identifies error-prone APIs, dangerous configurations, and footgun designs that enable security mistakes. Use when reviewing API designs, configuration schemas, cryptographic library ergonomics, or evaluating whether code follows 'secure by default' and 'pit of success' principles. Triggers: footgun, misuse-resistant, secure defaults, API usability, dangerous configuration.

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。

Audits GitHub Actions workflows for security vulnerabilities in AI agent integrations including Claude Code Action, Gemini CLI, OpenAI Codex, and GitHub AI Inference. Detects attack vectors where attacker-controlled input reaches AI agents running in CI/CD pipelines, including env var intermediary patterns, direct expression injection, dangerous sandbox configurations, and wildcard user allowlists. Use when reviewing workflow files that invoke AI coding agents, auditing CI/CD pipeline security for prompt injection risks, or evaluating agentic action configurations.

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。

Systematic code maturity assessment using Trail of Bits' 9-category framework. Analyzes codebase for arithmetic safety, auditing practices, access controls, complexity, decentralization, documentation, MEV risks, low-level code, and testing, then produces a scorecard with evidence-based ratings and a priority-ordered roadmap. Use when assessing or scoring the maturity of a smart contract or blockchain codebase, producing a maturity scorecard or evaluation, or judging how mature, well-tested, or well-documented such a project is against a rubric.

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。

Token integration and implementation analyzer based on Trail of Bits' token integration checklist. Analyzes token implementations for ERC20/ERC721 conformity, checks for 20+ weird token patterns, assesses contract composition and owner privileges, performs on-chain scarcity analysis, and evaluates how protocols handle non-standard tokens. Use when integrating or accepting arbitrary ERC20/ERC721 tokens, auditing a token implementation for standards conformity, or assessing risk from weird tokens such as fee-on-transfer, rebasing, missing return values, or blocklists.

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。

Smart contract development advisor based on Trail of Bits' best practices. Analyzes codebase to generate documentation/specifications, review architecture, check upgradeability patterns, assess implementation quality, identify pitfalls, review dependencies, and evaluate testing. Use when asking whether a smart contract project follows development best practices, reviewing on-chain/off-chain split, upgradeability, or delegatecall proxy patterns against guidelines, or seeking recommendations on contract design, inheritance, events, documentation, dependencies, or test strategy.

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。

Deep-dive offensive methodology for dependency confusion and namespace attacks across all major package ecosystems. Covers npm scope confusion exploiting the gap between public and private scoped packages and .npmrc misconfigurations where registry mappings fail to pin internal scopes exclusively. Addresses PyPI namespace attacks through --extra-index-url resolution ordering, NuGet feed priority exploitation when multiple package sources are configured without clear directives, Maven and Gradle repository ordering where artifact resolution traverses repositories sequentially, Go module proxy abuse through GOPROXY misconfiguration, Ruby gems namespace squatting, and Docker image tag confusion with unqualified image references. Provides complete proof-of-concept methodology using safe callbacks including DNS canary via interactsh or Burp Collaborator and HTTP beacon with no destructive payload. Covers reconnaissance techniques for discovering internal package names through GitHub repository analysis, error message harvesting, JavaScript source map extraction, lock file parsing, job postings mentioning internal tools, and package manifest inspection. Directly references and builds upon Alex Birsan's seminal 2021 dependency confusion research. Each ecosystem section includes registry-specific exploitation mechanics, configuration vulnerabilities, and defensive countermeasures for engagement reporting.

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。

JWT attack methodology for penetration testers. Covers algorithm confusion (alg:none, RS256→HS256), weak HMAC secret brute force, kid parameter injection (SQLi, path traversal), jku/x5u/jwk header injection, JWKS cache poisoning, JWS/JWE confusion, timing attacks, and mobile JWT storage extraction. Use when testing JWT-based authentication, hunting auth bypass via token manipulation, or evaluating JWT implementation security in web or mobile apps.

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 offensive methodology for software supply chain attacks covering the full kill chain from reconnaissance through exploitation. Addresses dependency confusion across npm, PyPI, and NuGet ecosystems where internal registry override allows an attacker to inject malicious packages that shadow private dependencies. Covers typosquatting techniques for popular packages, compromised package injection via maintainer account takeover or social engineering, and build system attacks through Makefile injection, setup.py install hooks, and npm postinstall scripts. Extends into CI/CD artifact tampering where build outputs are replaced or modified in transit, code signing abuse through stolen or self-signed certificates, upstream repository compromise via commit injection or force-push to trusted repos, and container image supply chain attacks including base image trojaning and registry confusion. Maps to MITRE ATT&CK T1195.001 (Supply Chain Compromise: Compromise Software Dependencies and Development Tools) and T1195.002 (Supply Chain Compromise: Compromise Software Supply Chain). Integrates tooling such as confused for dependency confusion scanning and dependency-check for known vulnerable component detection. Each technique section provides reproducible proof-of-concept patterns, detection guidance for defenders, and engagement-safe execution notes for authorized red team operations.

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 configuring `rig watchdog` policies, authoring wake/refocus/alignment-checkpoint messages, or choosing the right intervention level for a stale-owner situation. The 3-level continuity-check stack (wake / refocus / alignment-checkpoint), the discipline that prevents cadence pollution and bureaucracy theater, and the artifact-pool loop-edge pattern for evidence-aware waking.

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。

Multi-perspective review of a feature plan or requirements doc before development begins. Evaluates from strategy, design/UX, and engineering angles to catch gaps early.

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。

Build, run, deploy, and evaluate OpenAI Agents SDK apps from Codex. Use when the user asks to create or adapt an Agents SDK app, build from a prompt or Codex thread, prepare a runnable agent prototype, add a focused eval harness, or deploy locally through the Agents SDK Deployment Manager.

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。

Turn plugin-eval findings into a concrete rewrite brief for a Codex skill. Use when the user already evaluated a skill and now wants Codex to improve it, especially after asking what to fix first.

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 building public-equity DCF valuation workbooks. Default to the banker formula workbook path for new model builds; use deterministic exports only for controlled support calculations or explicit lightweight runs. Do not use for standalone workbook audits; use model-audit-tieout.

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 translating a specific event, policy change, macro shock, or industry development into public-equity issuer, sector, earnings, valuation, positioning, and portfolio implications. Do not use for standalone macro strategy, rates, FX, credit, futures, or generic market commentary.

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 turning a public-equity base case, model, thesis, event, or catalyst into scenario skew, sensitivity, breakpoint, and PM action-threshold analysis. Do not use for first-pass model builds, credit-security valuation, or generic planning.

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 or critique a product flow, journey, workflow, funnel, onboarding path, checkout path, settings path, screen, or multi-step product experience by capturing screenshots first, then reporting UX, design, and accessibility findings inline from that evidence. Use Figma only when the user explicitly asks for a board. Use when the user asks to audit, review, critique, inspect, assess, analyze, evaluate, or give feedback on a product experience.

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 product or business data to support a decision or recommendation. Use when a decision depends on metric-backed evidence, such as choosing a direction, prioritizing an opportunity, evaluating a change, segmenting users, sizing tradeoffs, or deciding what to do next.

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。

Evaluate SaaS unit economics and capital efficiency. Use when deciding whether the business can scale efficiently or needs correction.

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。

Evaluate an AI product idea across outcomes, hypotheses, risks, and positioning. Use when deciding whether an AI solution deserves investment or recommendation.

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。

Assess whether your product work is AI-first or AI-shaped. Use when evaluating AI maturity and choosing the next team capability to 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。

Map a market's segments, players, substitutes, and whitespace with cited evidence. Use when entering or re-evaluating a market before sizing, positioning, or picking competitors to study.

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。