Claude Code 플러그인
공개 Claude Code 마켓플레이스의 플러그인 17개입니다. 각 플러그인은 함께 설치되는 스킬, 슬래시 명령, 에이전트, 훅 또는 MCP 서버를 묶습니다.
필터와 일치하는 17개의 플러그인을 표시합니다. 필터 초기화
Per-deal-and-packaging Commercial domain. v2.8.0 ships 8 skills: orchestrator + pricing-strategist (model picker + Van Westendorp WTP + packaging) + deal-desk (deal scorer + discount approval routing + redline) + partnerships-architect (5-tier classifier + joint GTM + revshare modeler) + channel-economics (cost-to-serve + ROI + mix optimizer) + commercial-policy (data-backed discount matrix + exception flow + linter) + rfp-responder (Shipley structured RFP/RFI/RFQ + win-theme + winrate predictor, context: fork) + commercial-forecaster (4Q-weighted bookings + cohort NRR/GRR + funnel-confidence with mandatory assumption disclosure). Hard rules: pricing outputs model+range (never a single number), deal outputs route to named human approver (never auto-approve), forecast outputs surface conversion assumption, RFP never invents claims for GAP requirements. 21 stdlib Python tools, 28+ reference docs. Distinct from business-growth (sales execution), c-level-advisor/cro-advisor (strategic), fi
Convert long markdown files into world-class single-file interactive HTML — DOMAIN COMPLETE at v2.10.3 (5 skills). v2.10.3 adds md-slides, the slide-deck converter (arrow/Space/PgDn/Home/End/P navigation, presenter mode with clock + speaker notes + next-slide preview, URL-hash deep linking, @media print page-per-slide for browser-native PDF export; vanilla JS only; Prism.js opt-in via --syntax). Joins md-review (v2.10.2 code-review converter: 2-col diff, severity-tagged margin annotations, WCAG-1.4.1 badges, named reviewer footer), md-document (v2.10.1 long-form: sticky TOC, scrollspy, search, code-copy, Prism autoloader), markdown-html-orchestrator (v2.10.0 context: fork; deterministic doc-type classifier), and design-system (v2.10.0 onboarding wizard; WCAG-AA 12-token palette). 15 stdlib-only Python tools, 15 references, 4 template assets. Inspired by Thariq Shihipar's Claude Code HTML output essay.
Get a defense supplier ready for a CMMC 2.0 Level 2 assessment. Three deterministic stdlib scripts: an SPRS scorer that weights all 110 NIST SP 800-171 Rev 2 requirements per 32 CFR 170.24 and blocks gaps that can never go on a POA&M; a POA&M closeout tracker that checks every deferred gap against the 180-day Conditional status deadline and the annual affirmation calendar; and a scope classifier that sorts the asset inventory into the 170.19 categories and blocks on assets missing from the SSP or cloud providers holding CUI without FedRAMP. Ships cs-cmmc-assessor agent, /cs-cmmc-readiness and /cs-sprs-score commands, 3 cited references and a readiness sign-off.
Get products ready for the EU Cyber Resilience Act (Regulation 2024/2847). Four deterministic stdlib scripts: a scope classifier that sorts a portfolio into default, important class I/II and critical (Annex III/IV), blocks conformity routes Article 32 does not allow, checks support periods and names the CSIRT for Article 14 reports; an Article 14 clock for the 24h/72h/14-day/one-month deadlines live since 11 September 2026; an Annex I/II/VII gap check before the EU declaration of conformity; and an SBOM floor check for CycloneDX and SPDX. Ships cs-cra-officer agent, /cs-cra-readiness and /cs-cra-report commands, 3 cited references and an Article 14 runbook.
Get medical device software through IEC 62304 and IEC 81001-5-1. Four deterministic stdlib scripts: a software safety classifier (62304 4.3 with A1 external controls and segregation) that also sets the FDA Basic/Enhanced documentation level and flags 524B cyber devices; a lifecycle gap check scoped by class, with IEC 81001-5-1 security activities; a SOUP register check with SBOM cross-check; and a trace matrix release gate. Ships cs-device-software-lead agent, /cs-medical-device-software and /cs-soup-review commands and 4 references with per-claim confidence levels.
Chief AI Officer advisory for startups: model build-vs-buy calculator (API vs fine-tune vs build with 3-year TCO across 6 paths + breakeven that balances economics with practical feasibility), AI risk classifier (EU AI Act tier with 7 Article citations + US state patchwork: NYC LL 144, CO AI Act, IL HB 53, CA SB 1001, IL BIPA + industry overlays for FDA AI/ML, CFPB Circular 2023-03, NYDFS Reg 23, NAIC, ECOA, Fed SR 11-7), AI cost economics (API vs self-hosted breakeven with 2026 pricing across A100/H100, utilization reality, hidden costs). 4 in-depth references each citing 5+ authoritative sources. Standalone-installable; also bundled in c-level-skills. Strategic only — does not duplicate engineering AI/ML skills.
EU AI Act (Regulation (EU) 2024/1689) operational compliance specialist: AI system risk classifier (Articles 5/6/50 + Annex III), conformity assessment planner (Article 43 + Annex IV checklist), and obligation tracker (provider/deployer/importer/distributor + GPAI Articles 51-55). Article-level references and cross-framework mapping to ISO 42001, NIST AI RMF, GDPR. Stdlib-only.
Depth assessment of whether an AI system is high-risk under Art. 6 of the EU AI Act, grounded in the Commission's draft Art. 6(5) classification guidelines (general principles + Annex I + Annex III). Covers the Annex I product-safety route, all eight Annex III areas with worked examples, the Art. 6(3) exception and its profiling re-exception, and the Art. 25 quasi-provider trap. Outputs a structured decision block, a practitioner memo, and a JSON interchange artefact.
Determine whether a technology qualifies as an AI system under Art. 3(1) and classify its risk tier (prohibited, high-risk, GPAI with systemic risk, limited risk, minimal risk). Covers scope exclusions (Art. 2), AI system definition test (7 criteria), prohibited practice screening (Art. 5), high-risk assessment (Annex I + III with Art. 6(3) exception), GPAI check, and Art. 50 transparency triggers.
Assesses which of the Art. 50(1)-(5) transparency obligations of the EU AI Act apply to a given AI system's provider or deployer, grounded in the final Code of Practice on Transparency of AI-Generated Content (June 2026) and the Commission's draft Art. 50 Guidelines. Covers AI-chatbot disclosure, deepfake and synthetic-content marking/watermarking, emotion-recognition and biometric-categorisation notices, the machine-readable marking duty, the obviousness exceptions, and the implementation timeline. Outputs a formal mini-report plus a per-obligation compliance checklist with gap flags. For breadth-first tier triage use the EU AI Act System Classifier; for raw Art. 50 text and Q&A use the EU AI Act Knowledge Base; for the full role x tier matrix use the EU AI Act Obligations Mapper.
Visual regression testing with Percy, Chromatic, Playwright screenshots and Storybook, plus chart rendering tests for Chart.js, D3 and Vega. Covers responsive breakpoint matrices, baseline conventions, and a binding baseline gate. Agents (visual-diff-classifier, visual-baseline-curator) sort diffs into intentional, incidental or regression and propose an initial baseline set.
quality-engineeringClassify an AI system under the EU AI Act (Regulation (EU) 2024/1689) and map the compliance obligations that follow. Walks Art. 2 scope exclusions, Art. 3(1) AI system definition (7-criteria test), Art. 5 prohibited practice screening, Annex I and Annex III high-risk assessment with the Art. 6(3) narrow procedural exception, Art. 51-56 GPAI with the 10^25 FLOP systemic risk threshold, and Art. 50 transparency triggers. Built for in-house counsel classifying real systems: medical imaging, medical devices, credit scoring, insurance pricing, HR or CV screening, biometric identification, emotion recognition, deepfake or synthetic content, content moderation, fraud detection, generative AI or foundation model deployments. DACH routing for Betriebsrat consultation, BaFin, BSI, BNetzA, BfDI. Pairs with the EU AI Act High-Risk Implementation Readiness, FRIA, GPAI Code of Practice, and Serious Incident Reporting skills for downstream workflow.
Assess compliance with the EU General-Purpose AI (GPAI) Code of Practice under the AI Act (Regulation (EU) 2024/1689), Articles 51-56. Covers GPAI model identification (Art. 3(63)), systemic risk model designation (Art. 3(65), Art. 51, 10^25 FLOP training compute threshold), upstream provider obligations under Art. 53 (transparency, technical documentation, copyright policy, training data summary), additional systemic risk obligations under Art. 55 (model evaluation, adversarial testing, AI Office incident notification, cybersecurity), systemic-risk notification and Commission designation under Art. 52, downstream Art. 25 quasi-provider duties, and Art. 53(2) open-source exemption. Built for in-house counsel and AI governance teams reviewing LLM or foundation model deployments, drafting GPAI compliance plans, or assessing systemic risk thresholds. DACH: BaFin, BSI, BNetzA. Pairs with the EU AI Act System Classifier, High-Risk Readiness, FRIA, and Serious Incident Reporting skills.
This skill helps you apply the NIST AI Risk Management Framework and can quote AI RMF subcategories and GenAI profile action IDs verbatim from NIST AI 100-1 and 600-1. The skill routes you to the correct category as needed and has three modes, picked automatically from your question: 1. Consult: “What should I do per the AI RMF for this system?” 2. Governance Plan: “What should our AI governance program include?” 3. Assessment: “Run a full AI RMF aligned impact assessment for this system.” It knows the difference between the Core and the GenAI overlay, and won’t dump profile actions on a classifier or recommender.
Serious Incident Reporting (SIR) for high-risk AI systems under Article 73 EU AI Act (Regulation (EU) 2024/1689). Assess, qualify, draft, and submit Art. 73 SIR notifications across the four Art. 3(49) harm categories (death or serious health damage, critical infrastructure disruption, breach of fundamental rights, property or environmental damage). Covers Art. 73 immediate, 2/10/15-day reporting windows, market surveillance authority notification, deployer duties under Art. 26(5), and cross-regulation mapping with GDPR Art. 33/34 breach notification, NIS2 Art. 23 incident reporting, DORA Art. 19 ICT incident, and MDR vigilance. Built for in-house counsel and AI risk teams handling AI incidents, building AI incident response playbooks, or managing AI failure, AI bias, or automated decision incidents. DACH routing through German market surveillance (BNetzA, BaFin, BSI, BfArM, KBA). Pairs with the EU AI Act System Classifier, High-Risk Readiness, FRIA, and GPAI Code of Practice skills.
Turns a legal question, a set of draft questions, or source material such as a statute, jury instruction, rubric, contract checklist, or coding manual into a battery of narrow questions that TypeSafe's Jev, a fast classifier that returns probabilities rather than prose, can answer reliably across many documents. The skill breaks conclusions such as negligence, felony murder, or standing into element-level questions, writes the governing rule into each question, and delivers the questions alone, in Jev's request format or in plain English. Built for legal work that asks the same questions of many texts: coding statutes across jurisdictions, coding opinions or case files for empirical research, checking contracts against a review checklist, screening intake narratives, and grading student work against a rubric. Use when asked to write Jev questions, build a classifier for legal documents, or turn a legal test into yes-or-no questions. Running the questions requires Jev access; writing th
Plan and execute production ML engineering work — classifier versus generative model selection, zero-shot baselines, coding specialization, model training and fine-tuning (LoRA/QLoRA), evaluation and eval-set design, quantization decisions, inference deployment, lineage, feature parity, drift response, and regression triage, experimental weight inspection and targeted editing, grounded in practical engineering patterns for production ML systems. Do not use for statistical modeling and experimental design (that's the data scientist) or for operating a specific inference engine (that's a tool skill such as llama-cpp or vllm).