50 Plugins aus öffentlichen Claude Code marketplaces. Jedes bündelt Skills, Slash-Commands, Agents, Hooks oder MCP servers, die zusammen installiert werden.
Zeige 50 Plugins, die diesen Filtern entsprechen.
agents-incagents-inc/skills2
quiveryagizdo/quiver1
lawve-ailawve-ai/awesome-legal-skills1
swe-marketplaceandisab/swe-marketplace1Reiche das repo mit .claude-plugin/marketplace.json ein und wir listen jedes plugin auf.
Marketplace einreichenRun your work like an AI agency, from a single Notion board. Agents discuss, plan, and execute tasks in parallel with dependency ordering and model routing.
DAG-based workflow automation with parallel task execution and dependency management
Google Agent Development Kit (ADK) starter for building production AI agents — ReAct single-agent or multi-agent orchestration (Sequential/Parallel/Loop), tool wiring, evaluation harness, and optional Vertex AI Agent Engine deployment.
Skills and agents for the feature cycle: brainstorm a spec, plan tasks, build with tests (in parallel worktrees for larger plans), run a multi-agent code review that checks each finding against the code, debug from hypotheses, write commit messages and PR descriptions, build UI from Figma, and hand over context between sessions
Apply elite legal writing techniques drawn from Justice Kagan, Boies & Olson, and other top advocates to any legal document — briefs, submissions, letters, opinions, memos, or persuasive correspondence. Covers both prose craft (clarity, concrete examples, parallel construction, voice, strategic quoting) and architectural strategy (argument sequencing, framing, openings, endings), plus a dedicated section on detecting and removing AI-sounding patterns from legal prose. Use whenever drafting, editing, or sharpening any legal document that needs to genuinely persuade, not just be technically correct.
Multi-agent research system for comprehensive topic investigation. The research-team:coordinator skill decomposes a topic into subtopics, spawns parallel research-specialist subagents, and delegates synthesis to a report-writer — all from the main agent thread so nested-spawn constraints don't apply. Reports are plain Markdown by default, with Obsidian and Joplin profiles and five report templates.
End-to-end academic research pipeline — 8-phase (Phase 0..7) script-driven workflow across 7 federated sources (OpenAlex, arXiv, Crossref, PubMed, DBLP, bioRxiv, Exa), cross-source dedup, transparent ranking, dual-backend citation chasing (OpenAlex + Semantic Scholar), parallel deep-read fan-out, mandatory self-critique, and cited reports across 5 archetypes
Deploy a 2-layer parallel agent hierarchy for large, parallelizable work: big refactors, multi-file migrations, codebase-wide audits, and bulk generation. Pick an army size and power level; includes git checkpoints, a pre-deploy gate, phantom-completion checks, and multi-wave follow-up.
Next.js App Router patterns (15-16) - file-based routing, Server/Client Components, streaming, Suspense, metadata API, parallel routes, Server Actions, mutations, revalidation, Cache Components
Playwright E2E testing patterns - test structure, Page Object Model, locator strategies, assertions, network mocking, visual regression, parallel execution, fixtures, and configuration
How a team that works with agents commits and opens pull requests: /commit writes messages that explain why, /git-pr keeps PRs small and can never push to a protected branch, /worktree makes parallel branches the default. Adapts to your repo's existing conventions.
Plan the implementation of an approved requirement or specification: produce an executable, dependency-aware delivery plan covering work breakdown, dependency mapping, critical path, ownership, parallelism and sequencing, rollout strategy, rollback and recovery paths, and verification against the original requirement. Supports cross-team, cross-repository, migration, and staged-rollout scenarios. Do not use for pre-approval discovery or needs-finding, authoring a specification from scratch, coding or implementation, the neckbeard issue-to-PR delivery flow itself, or any work whose prerequisite decision has not been approved — planning unapproved work is an explicit stop condition.
Build multi-agent AI systems with LangGraph — the low-level orchestration framework for stateful, graph-based agent workflows. Covers supervisor, swarm, and hierarchical multi-agent patterns; subgraph composition; state management (checkpointers/stores); persistence; evals; and production debugging. Reach for this when designing agent architectures that need cycles, conditional branching, parallel execution, or human-in-the-loop patterns. Do not use this skill for unrelated requests; route to the nearest named specialist.
Operate Playwright for browser automation end to end: author and debug E2E test suites (robust locators, network interception and mocking, parallel workers, accessibility snapshot checks), wire them into CI, and drive headless browsing and scraping with an extract -> validate -> save loop. Use when writing, running, fixing, or scraping with Playwright, when a Playwright CI failure or JSON report needs triage, or when the bundled pwrun script should analyze a run. Do not use for QA strategy or test framework selection (route to qa-methodology), for frontend component or architecture design (route to frontend-engineering), or for Cloudflare/DDoS-GUARD challenge bypass (use flaresolverr).
Operate, configure, benchmark, and troubleshoot vLLM inference servers: Docker and Kubernetes deployment, quantization-aware model configuration (tensor parallelism, KV cache), OpenAI-compatible API serving, throughput and latency benchmarking, continuous batching tuning, GPU operation, and upgrade/rollback. Use when deploying or running a vLLM server (vllm serve, vllm/vllm-openai), sizing a model and its KV cache for GPUs, selecting quantization and parallelism, serving via /v1 endpoints, measuring serving throughput or latency, tuning batching, or diagnosing GPU, OOM, or startup failures in a vLLM deployment. Do not use for model training, fine-tuning, evaluation-set design, or engine-selection methodology (that is ml-engineering), or for operating the llama.cpp stack with GGUF models (that is llama-cpp); other inference engines (TGI, Ollama, Triton) are out of scope.
10 AI agent engineering skills: knowledge base injection, entity memory, multi-model routing, proactive intelligence, MCP servers, parallel orchestration
The run viewer for sdlc-graph, which declares it as a dependency — one zero-dependency page that renders parallel milestone agents, live or as a self-contained snapshot. Reads the run-directory layout: docs/graph-runs/<run-id>/{state.json,journals/}.
workflow.NET unit testing with xUnit.net: Fact and Theory tests, fixtures, parallel execution and CI, with NUnit, MSTest and FluentAssertions as references. Recommends xUnit for new projects, follows an existing NUnit or MSTest convention, and notes the FluentAssertions v8 license change.
functional-testingCoordinate staged or parallel jobs across live-verified coding-agent runtimes, coding-agent sessions, and in-session subagents through one runtime-adapter contract and a normalized event protocol, routed by deterministic capability- and risk-based policy with an optional provider-neutral System-1 decision plane for trace watching, failure triage, and pre-arbiter gating. Worktree isolation, resumable state, capture, safety gates, and independent arbiter review included.
productivityOpt-in, outside every domain bundle: orchestrate opencode workers from Claude Code to cut token costs, with single workers or parallel scout/coder/tester swarms on free or open-source models and worktree isolation. Requires the third-party opencode CLI and an opencode account; by default worker prompts and code go to opencode's hosted service (point --model at a local model to keep code on your machine), so install it only if you accept that data flow.
