82건 · 관련도순
Monitor requests to an AI prediction API for signs of model copying or training-data exposure. Test an authorized model with simulated attacks, measure the risk, and evaluate defenses such as request limits and reduced prediction detail.
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Conducts privacy auditing of AI models including training data extraction testing, membership inference attacks, model inversion testing, and attribute inference assessment. Uses ML Privacy Meter and related tools to quantify privacy leakage. Keywords: model audit, membership inference, privacy meter, model inversion, training data extraction.
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Flip a problem, goal, or decision 180 degrees to find what forward thinking misses. Asks what would guarantee failure, then works backward to what must be avoided or changed. Load when the user says "invert this", "flip this problem", "what would guarantee failure", "think backward", "reverse engineer the goal", "think about it backwards", or when deep-thinking diagnoses an inversion frame. Also triggers on "what's the opposite of success here", "how would we sabotage this". Two methods: Failure Inversion and Opposite Goal. Max 2 clarifying questions before inverting. Always returns forward actions. For broader analysis, deep-thinking calls this.
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Assess the security of RAG systems, which retrieve documents to support AI answers. Test whether stored vectors expose source text, leak documents between tenants, or allow inserted content to dominate search results, then document findings and controls.
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Extract sensitive training data and internal capabilities from AI models through repeated programmatic querying. Use this skill when testing LLMs and ML models for data privacy leaks, intellectual property exposure, or unintended memorization. Covers membership inference, prompt extraction, training data reconstruction, and API boundary testing.
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Application architecture patterns and code organization — clean architecture, hexagonal architecture, feature-based structure, dependency inversion, module boundaries, SOLID principles at module scale. Activate on "clean architecture", "hexagonal architecture", "feature-based structure", "dependency inversion", "module boundaries", "architecture pattern", "folder structure", "fat controller", "circular dependency", "IoC container". NOT for microservice decomposition (use microservices-patterns) or database schema design (use database-design-patterns).
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Structured second-pass reasoning after code review or plan evaluation. Offers named reasoning methods (pre-mortem, inversion, red team, Socratic, etc.) to re-examine findings through a specific lens. Use after /mk:review verdict or any time deeper analysis is needed on an existing output. NOT for initial review (see mk:review); NOT for creating implementation plans (see mk:plan-creator).
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変更が波及する全モジュール・全フローを特定し、集計ルール、状態遷移、符号反転、 コピペ差分、表示/帳票/APIの整合性を監査するスキル。Impact Mapで変更核から 影響範囲を可視化し、Consistency Matrixで横断整合を検証し、Copy Propagation Review戦略でコピペ実装を効率的にレビューする。 Use when one change affects multiple modules, reports, flows, or copied implementations. Audits aggregation totals, state transitions, sign inversion, and cross-module consistency.
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Detect model stealing, model inversion, and membership inference performed through inference-API abuse by monitoring query patterns, applying output perturbation, and red-teaming your own model's extractability.
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Test vector stores for embedding inversion, cross-tenant leakage, and poisoning.
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Flip core assumptions to reveal hidden constraints and alternative approaches - "what if the opposite were true?
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Clean architecture v3 — use cases, entities, interfaces, dependency inversion, testing, boundaries
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Expert en clean architecture (use cases, entities, interface adapters, dependency inversion)
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Application architecture patterns and code organization — clean architecture, hexagonal architecture, feature-based structure, dependency inversion, module boundaries, SOLID principles at module scale. Activate on "clean architecture", "hexagonal architecture", "feature-based structure", "dependency inversion", "module boundaries", "architecture pattern", "folder structure", "fat controller", "circular dependency", "IoC container". NOT for microservice decomposition (use microservices-patterns) or database schema design (use database-design-patterns).
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업무 로직을 데이터베이스, 외부 API, 프레임워크에서 분리합니다. 경계와 테스트를 정리해 새 서비스를 설계하고 기존 코드를 기능별로 단계적으로 개선하도록 지원합니다.
Apply 12 structured investing frameworks to evidence already collected. Each framework orders the questions to ask, defines reasons to reject a case, and identifies its limitations, helping explain why different approaches reach different conclusions.
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Assess security risks in AI systems by scanning prompts for known attack patterns and scoring data exposure, poisoning, and tool abuse risks. Map findings to MITRE ATLAS, an AI threat framework, and identify protective controls.
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Guide substantial Swift changes in cmux with rules for package boundaries, dependency direction, state management, concurrency, and API documentation. Structure code so public package types can be tested without launching the app.
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Plan and audit preprocessing for grouped time-series data, such as matches or patient records. Check missing-value handling, scaling scope, feature meaning, and data leakage, where information unavailable at prediction time enters model inputs.
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Comprehensive guide for dependency injection (DI) in Golang. Covers why DI matters (testability, loose coupling, separation of concerns, lifecycle management), manual constructor injection, and DI library comparison (google/wire, uber-go/dig, uber-go/fx, samber/do). Use this skill when designing service architecture, setting up dependency injection, refactoring tightly coupled code, managing singletons or service factories, or when the user asks about inversion of control, service containers, or wiring dependencies in Go. For a specific DI library, → See `samber/cc-skills-golang@golang-google-wire`, `samber/cc-skills-golang@golang-uber-dig`, `samber/cc-skills-golang@golang-uber-fx`, or `samber/cc-skills-golang@golang-samber-do` skills.
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Comprehensive framework for deep analysis of articles, papers, and long-form content using 10+ thinking models (SCQA, 5W2H, critical thinking, inversion, mental models, first principles, systems thinking, six thinking hats). Use when users want to: (1) deeply understand complex articles/content, (2) analyze arguments and identify logical flaws, (3) extract actionable insights from reading materials, (4) create study notes or learning summaries, (5) compare multiple sources, (6) transform knowledge into practical applications, or (7) apply specific thinking frameworks. Triggered by phrases like 'analyze this article,' 'help me understand,' 'deep dive into,' 'extract insights from,' 'use [framework name],' or when users provide URLs/long-form content for analysis.
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Detect structural variants from long-read alignments using Sniffles, cuteSV, and SVIM. Use when detecting deletions, insertions, inversions, translocations, or complex rearrangements from ONT or PacBio data, especially those missed by short-read methods.
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Use when the user asks to "triage our comments, DMs, and mentions", "draft replies to this thread", "can we repost this fan post", or "set up inbox SLAs and an escalation path"; produces a ranked triage queue with register detection (sincere / ironic / performative / parasocial, sentiment-inversion table included — "this is so bad" under a comedy register is praise), a commenter taxonomy (troll monitor-only / rager / misguided / unhappy-customer / advocate) with a response-tier ladder and per-channel SLAs, an escalation matrix ending at the crisis path, a moderation ladder plus house rules for owned spaces, and a UGC curation-and-rights mode whose dated permission entries route to the channel registry — every reply is a ranked DRAFT a human posts; nothing is ever auto-sent. Not for launch-window feedback triage — use launch-feedback-synthesizer. 评论私信提及分诊/语域识别/回复草稿/UGC授权
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AI/ML security playbook. Use when assessing model supply chain attacks (pickle RCE, poisoned weights), adversarial examples, model poisoning, model stealing, data privacy attacks (membership inference, model inversion), and autonomous agent security risks.
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