8,925 件 ・ 関連度順
概要と使いどころ
Plans security penetration tests for web applications. Analyzes codebase, API routes, auth implementation, and infrastructure config to generate comprehensive pentest plans. For authorized testing only.
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Audit npm dependencies for security vulnerabilities, outdated packages, and unused dependencies. Use when checking for security issues, updating packages, or cleaning up dependencies.
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。
Performs a deep code review of files, modules, a diff, or a branch - finding security vulnerabilities (mapped to OWASP Top 10:2025), correctness bugs, performance problems, and maintainability issues - and returns severity-ranked findings with evidence and concrete fixes. Use when the user asks to review, audit, or sanity-check code, asks "is this safe", "what's wrong with this", "find bugs", or wants a security or performance pass before shipping. For posting line comments on a GitHub pull request, use git-pr-reviewer.
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 creating Claude Code hooks with security-first design. Use for validation and enforcement.
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 hook security, performance, and SDK compliance. Use for 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。
Securely manage passwords and sensitive data in your 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。
Use dynamic data and secure secrets in your 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。
Insecure deserialization hunting across Java / .NET / PHP / Python / Ruby / Node. Gadget-chain RCE, OOB-gated blind detection, magic-byte fingerprinting. Wiki-first, FIND schema output.
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。
LLM / AI application attack hunting - prompt injection (direct + indirect), excessive agency, insecure output handling, system-prompt + data leakage. OWASP LLM Top 10. Wiki-first, FIND schema output.
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。
Financial trading CLIs — spot and perp trading on Hyperliquid, Binance, Coinbase, OKX. Prediction markets on Polymarket. Deposit and withdraw across chains. LLM-enriched price quotes with trend analysis. News and SEC filings. Historical backtesting with forward PnL analysis. Use when: user asks about stock/crypto prices, wants to trade, deposit, withdraw, check portfolio, or backtest a 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。
Produce a plain-language manual test plan from the context supplied to it — an executive summary, a high-level list of named tests, and a detail section per test with the steps a person follows by hand and the outcomes they should expect. Use when you want to create, draft, generate, or outline a manual test plan, manual QA steps, hands-on verification steps, or an acceptance walkthrough for a feature, change, branch, plan, or PR. When the plan holds more than five tests and at least two natural categories emerge, both the test list and the detail sections are organized under plain-language categories. When nothing in the supplied context can be manually tested, it says so and asks for more context instead of producing a document. Does not analyze code for automated test coverage gaps — use automated-test-planning. Does not write test code — use tdd. Does not review code quality — use code-review. Does not stress-test an existing plan — use iterative-plan-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。
Guides SOC 2 Type II Privacy Trust Services Criteria preparation and audit execution. Covers AICPA TSP Section 100 Privacy criteria P1-P8 including notice, choice/consent, collection, use/retention/disposal, access, disclosure, security, and quality. Includes evidence collection, control testing, and report review. Keywords: SOC 2, privacy criteria, TSP, AICPA, Type II, trust 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。
Build privacy-preserving data sharing platforms using synthetic data generation with the SDV library, data clean rooms, secure enclaves, and utility measurement. Covers end-to-end architecture for sharing analytical datasets while preserving individual privacy guarantees.
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 PET selection guide covering differential privacy, homomorphic encryption, secure multi-party computation, federated learning, zero-knowledge proofs, and trusted execution environments. Includes use-case matching matrix, performance comparison, and GDPR alignment assessment for each technology.
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。
Implement privacy-preserving record linkage across datasets using Bloom filter encoding, secure hash matching, threshold tuning for precision and recall, and false positive management. Enables entity resolution without exposing raw personally identifiable information between parties.
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。
Guides compliance with India's Digital Personal Data Protection Act 2023. Covers consent manager registration, data fiduciary obligations under Sections 4-7, significant data fiduciary requirements under Section 10, data principal rights, and Board enforcement framework. Keywords: DPDP Act, India data protection, consent manager, data fiduciary, significant data fiduciary, data principal rights.
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。
Assesses children's data protection in educational technology. Covers COPPA school exception under Section 312.5(c)(4), FERPA intersection, parental rights, teacher consent authority, data deletion at year-end, and Student Privacy Pledge compliance. Keywords: edtech, COPPA school exception, FERPA, student privacy, teacher consent, educational data.
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。
Guides a comprehensive organisational data protection audit against key GDPR requirements including Articles 5, 24, 25, 28, 30, 32, 35, and 37. Includes 50+ control points covering principles, accountability, security, and governance. Activate when performing compliance audits, preparing for supervisory authority inspections, or assessing organisational GDPR maturity. Keywords: data protection audit, compliance audit, GDPR audit, control points, accountability.
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。
Architecture guide for GDPR-compliant federated learning systems. Covers horizontal and vertical FL, aggregation strategies (FedAvg, FedProx), communication efficiency, secure aggregation, and differential privacy integration. Includes privacy guarantees analysis and deployment patterns for cross-organizational ML without data sharing.
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。
Implementation guide for Global Privacy Control (GPC) automated opt-out signal per CPRA Section 1798.135(e). Covers Sec-GPC HTTP header detection, JavaScript navigator.globalPrivacyControl API, and state-specific requirements for CA, CO, CT, MT, TX, and OR. Includes server-side detection code and compliance mapping.
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 CCPA Section 1798.105 right to delete and CPRA amendments including service provider obligations, statutory exceptions for legal, security, and internal uses, consumer identity verification procedures, and 45-day response timeline management. Activate for CCPA deletion, CPRA right to delete, California privacy, consumer deletion queries.
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 security auditor for AI Skills and Bundles. Performs non-intrusive static analysis to identify malicious patterns, data leaks, system stability risks, and obfuscated payloads across Windows, macOS, Linux/Unix, and Mobile (Android/iOS).
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 security assessment of REST, GraphQL, WebSocket, and SOAP APIs: discovery, authentication and authorization flaws (BOLA/IDOR, JWT/OAuth), rate-limit testing, and a structured multi-phase methodology.
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。
Implement secure API design patterns including authentication, authorization, input validation, rate limiting, and protection against common API vulnerabilities
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。