10 plugins from public Claude Code marketplaces. Each one bundles skills, slash commands, agents, hooks or MCP servers that install together.
Showing 10 plugins that match these filters.
testland-qatestland/qa1
claude-code-plugins-plusjeremylongshore/tons-of-skills-marketplace1Submit the repo with .claude-plugin/marketplace.json and we list every plugin.
Submit marketplaceTwelve 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.
academicAdvanced 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.
Post-hoc verification and trust audit of AI-generated factual and interpretive claims. Classifies claims by type and salience, routes them to domain-appropriate sources, scores trustworthiness on a tiered scale with an Interpolated verdict for plausible-but-unsupported detail, and assesses rhetorical fairness on interpretive claims. Designed with a focus on healthcare law in England and Wales, but with general applicability beyond.
Screens a dismissal — proposed or already done — against the unfair dismissal framework for England & Wales, and shows where it is exposed. Structures the qualifying-service question, the automatically-unfair categories (no qualifying period), and a Burchell / Polkey / band-of-reasonable-responses analysis for a solicitor to verify — it does not determine fairness. Surfaces specific procedural risks. Use when the user says 'is this unfair', 'screen this dismissal', 'ordinary unfair dismissal', 'automatic unfair dismissal', or wants a structured fairness review before or after dismissal.
ML model testing: Giskard and Deepchecks validation, Evidently drift monitoring, Fairlearn fairness metrics, and model performance regression gates. Assigns risk tiers with the fairness and explainability evidence each tier requires, and wires Jupyter notebook CI with papermill, nbval, testbook and nbstripout.
ai-mlAssess 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).
Model technical capacity, unit cost, and budget constraints connected to demand, performance, and reliability decisions. Use when projecting capacity from growth forecasts, sizing for peak events, designing cost-aware scaling policies, defining budget thresholds or quota/rate-limit enforcement, running or planning load/soak tests as capacity evidence, resolving SLO-cost tradeoffs, or modeling multi-tenant demand distributions, hot-tenant skew, pooled or siloed headroom, fairness evidence, and tenant-variable unit cost. Do NOT use for financial P&L statements, fundraising scenarios, or SaaS metrics (route to financial-modeling); for infrastructure implementation or cloud-resource provisioning (route to platform-engineering); or for generic cloud-cost tips and universal utilization targets — this skill does not prescribe fixed savings rates or one-size-fits-all thresholds.
Design and build waiting-list portals for anticipated goods or services: classify whether the experience needs interest capture, verified early access, referral growth, a virtual waiting room, appointment backfill, or scarce-item allocation; choose a reversible architecture; and define the state machine, abuse controls, consent and email lifecycle, fairness, observability, and release gates. Optionally validate supplied email and phone contacts with expiring magic links or provider-managed verification, then feed eligible, consented records to a runtime-configured CRM adapter. Use for prelaunch signups, beta access, launches, bookings, reservations, traffic-spike queues, and premium or cinematic campaign experiences. Do not use for generic landing-page copy, ordinary CRM or email operations, or checkout and inventory systems in isolation; route those parts to the appropriate specialist skill.
AI ethics and fairness validation
