공개 Claude Code 마켓플레이스의 플러그인 26개입니다. 각 플러그인은 함께 설치되는 스킬, 슬래시 명령, 에이전트, 훅 또는 MCP 서버를 묶습니다.
필터와 일치하는 26개의 플러그인을 표시합니다.
copilot-primitives-claudegbb-acelerators/awesome-harness-primitives2
awesome-journal-skillsbrycewang-stanford/Awesome-Journal-Skills1
testland-qatestland/qa1
ai-skillsAmey-Thakur/AI-SKILLS1.claude-plugin/marketplace.json이 포함된 repo를 제출하면 모든 plugin을 목록에 추가합니다.
marketplace 제출Governed Exa operator pack with 30 workflows for Search, Contents, Agent, Monitors, Websets, access, reliability, cost, and security.
Operator-grade Firecrawl v2 skill pack with 30 governed acquisition, security, reliability, and production workflows
Operator-grade Fireflies.ai GraphQL and Webhooks V2 pack with 24 governed workflows for meeting data, AskFred, ingestion, security, reliability, and production operations.
Operator-grade Instantly API v2 pack with 24 source-grounded skills for campaigns, accounts, leads, scoped access, webhooks, reliability, and governed delivery.
Twelve-skill bundle covering the 《南开管理评论》 manuscript lifecycle: workflow router, fit positioning, theory-gap articulation, mechanism-backed hypothesis development, measurement rigor (reliability / validity / common-method-bias), survey-SEM (mediation / moderation / moderated mediation / HLM), experiments, multi-case / grounded theory, China contextualization, discussion that advances a management theory, submission preflight, and rebuttal.
academicOpen Horizons agentic DevOps platform toolkit for Azure, AKS, Backstage, AEG, GitHub, Azure DevOps, Terraform, security, reliability, architecture, and deployment operations. Use it to install specialist agents, reusable skills, safety hooks, MCP integrations, and a safe publisher for repository customizations.
Track SLAs, SLIs, and SLOs for service reliability
Background job and queue testing for Sidekiq, Celery, BullMQ, SQS, RabbitMQ and Kafka consumers, plus cron schedules and idempotency guarantees. A read-only critic agent (async-job-health-critic) flags missing retry limits, absent dead-letter routing, and other at-least-once reliability defects in queue and worker code.
backend-distributed12 working methods for reliability: capacity-forecasting, chaos-engineering, disaster-recovery-testing, failure-mode-analysis, and more
Context curation and distractor filtering for production-grade agent reliability
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.
Operate Azure workloads with cost management, production diagnostics and triage, Application Insights instrumentation, observability stack design, and reliability posture assessment.
Iteration, accessibility, reliability, and design process. Covers Accessibility, Iteration, Factor of Safety, Prototyping, Weakest Link, Scaling Fallacy, and Ockham's Razor.
designThree agent skills: general engineering guidance and Go HTTP and TCP server reliability checklists
workflowModel 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.
Guide embedded technical engagements from ambiguous stakeholder need through discovery, framing, hypothesis, build, evaluation, deployment, adoption, measurement, and generalization while preserving evidence, decision rights, and field learning. Use when one accountable technical lead must carry continuity across customer or stakeholder discovery, implementation, production fit, adoption, and measurable outcomes. Do not use for a bounded repository change, product investment governance, ongoing reliability or platform ownership, an isolated specialist task, or advisory work that ends before implementation and adoption.
Operate, configure, secure, and troubleshoot the LiteLLM AI gateway (proxy) and Python SDK: run the proxy (litellm --config), route to 100+ providers through one OpenAI-compatible API, configure model lists and routing/reliability, virtual keys, teams, budgets, rate limits, caching, guardrails, observability, and spend, and diagnose request failures. Use when deploying or running a LiteLLM proxy or gateway (config.yaml, ghcr.io/berriai/litellm), wiring the Python SDK or OpenAI SDK through it, or hardening a public-facing deployment. Do not use for operating a single inference engine (vllm, llama-cpp), for engine-selection methodology (ml-engineering), or for building applications on top of an LLM API (backend/frontend engineering).
Optimize software performance through user-journey baselines, profiling, representative benchmarks, controlled experiments, and field verification. Use when asked to make an application, service, workflow, or hot path faster or more efficient, or to build the measurement loop first. Do not use for a reliability incident, capacity forecast, generic product A/B test, or operating a named telemetry or load-testing tool without an optimization objective.
Define observable, governed evidence for product outcomes through metric trees, event/tracking plans, instrumentation QA, and measurement governance. Use when defining product metrics, designing tracking plans, building event taxonomies, running product funnels or cohort analysis, setting up dashboard contracts, or auditing instrumentation quality. Do NOT use for statistical inference or experiment design (route to data-scientist), data pipeline implementation (route to data-engineering), data architecture decisions (route to data-architect), observability or infrastructure monitoring (route to site-reliability-engineering), or general business intelligence and dashboard building (route to BI tooling).
Compare intended product outcomes against observed results to close the launch-to-learning loop: collect post-launch evidence, distinguish expected from observed from uncertain from inferred claims, update assumptions, assess feature health, and choose among continue/improve/harvest/pivot/pause/retire — including retirement lifecycles with deprecation, migration, customer treatment, and retained reusable learning. Do not use for incident postmortems or root-cause analysis (routes to incident-learning or site-reliability-engineering); do not use for analytics instrumentation or metric dashboard design (routes to product-analytics-and-measurement); do not use arbitrary thresholds as universal retirement rules — decisions require human judgment and context.
