23 plugins from public Claude Code marketplaces. Each one bundles skills, slash commands, agents, hooks or MCP servers that install together.
Showing 23 plugins that match these filters.
claude-code-skillsalirezarezvani/claude-skills1
pm-claude-skillsmohitagw15856/pm-claude-skills1
magnus-agent-skillsmagnus919/agent-skills1Submit the repo with .claude-plugin/marketplace.json and we list every plugin.
Submit marketplaceTwelve-skill bundle covering the Cognitive Psychology (Elsevier) manuscript lifecycle, including its long-form, multi-experiment, computational-modeling house style and code-transparency expectations.
academicTwelve-skill bundle covering the Criminology (ASC / Wiley) manuscript lifecycle, from theory-forward topic selection through crime-data analysis, transparency, blinded review, and R&R rebuttals.
academicTwelve FAccT-specific skills for the ACM Conference on Fairness, Accountability, and Transparency, spanning fairness/accountability/transparency as first-class contributions across CS, law, social science, humanities, and policy, grounded in the FAccT 2026 CFP and Author/Reviewer Guides, facctconference.org, the ACM Digital Library, and dblp.
academicTwelve-skill bundle covering the International Organization (IO) manuscript lifecycle for international-relations research, centered on IR theory, IPE/security scope, and IO's verify-before-publication transparency policy.
academic
Twelve-skill bundle covering the Journal of Educational Psychology manuscript lifecycle, including its classroom/school study designs, multilevel/SEM analysis, APA 7th format, and Transparency and Openness requirements.
academicTwelve-skill bundle covering the Journal of Personality and Social Psychology (JPSP) manuscript lifecycle: choosing the right section (Attitudes and Social Cognition, Interpersonal Relations and Group Processes, Personality Processes and Individual Differences), building multi-study packages, internal meta-analysis, JARS reporting, APA 7th style, TOP-Level-2 transparency, masked review, and the per-section editorial process.
academicTwelve-skill bundle covering the Psychological Bulletin review / meta-analysis lifecycle — from a meta-analyzable question through PRISMA search, coding, random-effects synthesis, moderators and publication-bias diagnostics, MARS/JARS reporting, transparency, submission, and rebuttal.
academicTwelve-skill bundle covering the Review of Educational Research (RER) review-article lifecycle, from scoping a synthesis-worthy question and a preregistered protocol through systematic search, the organizing framework, comprehensiveness and risk-of-bias, PRISMA/MARS transparency, submission, and revision.
academicResearch orchestrator (hybrid router + fallback). Deterministic SIGNALS classification routes to 6 specialists (pulse/litreview/grants/dossier/patent/syllabus) at >=2 signals, else runs own 8-step plan-decompose-search-synthesize-cite fallback. Routing transparency mandatory. Path-B from megaprompt 13.
Advanced PM skills: AI Product Canvas, Multi-Source Signal Synthesiser, Experiment Designer, Design Handoff Brief, AI Ethics Review. For senior PMs working on complex products — including a structured ethical review framework for AI/ML features covering fairness, transparency, privacy, safety, and accountability.
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.
Draft GDPR-compliant privacy notices as .docx for any EU/EEA jurisdiction and audience. Supports five notice types (Website/App, Applicant, Employee, Business Partner, B2C Customer) across DE, FR, AT, IT, ES, NL, BE, IE, and UK GDPR. Covers Art. 13/14 disclosures, AI Act transparency, cookie policies, multi-language support, DPIA indicators, and post-generation compliance checklists.
Classify 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.
EU AI Act (Regulation (EU) 2024/1689) compliance advisor — risk classification across all four tiers, all 9 prohibited practices (Art. 5, including the nudification/CSAM prohibition from Dec 2, 2026), all 8 Annex III high-risk use case areas, provider and deployer obligations (Arts. 9–17, 26), GPAI model obligations including the July 2025 Code of Practice (Arts. 51–55), conformity assessment and CE marking (Arts. 43–48), EU AI database registration, Art. 50 transparency (chatbots, synthetic media, AI-generated content), governance (AI Office, AI Board), penalties (Art. 99), confirmed phase-in timeline (Digital Omnibus adopted June 29, 2026: Annex III deferred to Dec 2, 2027; Annex I to Aug 2, 2028), and cross-framework mapping to ISO 42001, NIST AI RMF, and GDPR. Use for any EU AI regulation, AI system classification, or AI compliance question. Current as of July 2026. GPAI enforcement powers activate August 2, 2026.
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.
Expert ISO 42001 AI Management System (AIMS) compliance advisor. Use this skill whenever a user asks about ISO/IEC 42001:2023, AI governance, AI management systems, AI risk assessment, AI system impact assessment, Annex A controls for AI, Statement of Applicability for AI systems, AI policy, responsible AI, AI lifecycle management, AI incident management, AI transparency, AI bias, AI certification readiness, or any topic related to implementing or auditing an AI Management System.
Expert NIST AI Risk Management Framework (AI RMF 1.0) advisor covering all four functions: GOVERN, MAP, MEASURE, MANAGE. Use this skill whenever a user asks about NIST AI RMF, AI risk management, AI trustworthiness, GOVERN function, MAP function, MEASURE function, MANAGE function, AI RMF Playbook, AI risk profiles, responsible AI, AI bias management, AI transparency, AI explainability, AI reliability, AI safety, NIST AI 100-1, AI risk assessment, AI incident response, or alignment to EU AI Act, ISO 42001, or NIST CSF via AI RMF.
Assess compliance obligations under the EU Data Act (Regulation (EU) 2023/2854) for connected products, IoT devices, data sharing, cloud switching, B2B fairness, B2G data access, dispute resolution, and international data transfers. Covers scope assessment (manufacturer, data holder, data recipient roles), user data access rights, pre-sale transparency, unfair contract terms, public authority data requests, cloud portability, dispute settlement mechanisms, international transfer restrictions, access-by-design obligations, trade secret protection, and cross-regulation mapping with GDPR, AI Act, and CRA. Use when assessing Data Act obligations, designing connected products, drafting data sharing contracts, responding to B2G requests, planning cloud switching capabilities, or evaluating dispute resolution options.
Design and operate AI governance: principles, decision rights, risk tiers, lifecycle gates, fairness, transparency, privacy, security, compliance mapping, maturity and board reporting across SaaS, API, self-hosted and agentic systems. Use to admit, update or retire Agent Skills, MCP servers and A2A capabilities; approve an agent for production actions; or decide earned-autonomy promotion, reduction or revocation. Cover GxP, ALCOA+, data integrity, electronic records, assurance and QMS interfaces for life sciences. Do not use for legal interpretation (legal-strategy), data-platform mechanics (data-architect/data-engineering), security implementation (secure-software-engineering), skill package inspection (agent-skills), or executing runtime changes (agent-production-operations).
