111 Plugins aus öffentlichen Claude Code marketplaces. Jedes bündelt Skills, Slash-Commands, Agents, Hooks oder MCP servers, die zusammen installiert werden.
Zeige 111 Plugins, die diesen Filtern entsprechen.
lawve-ailawve-ai/awesome-legal-skills2
testland-qatestland/qa2
rotetrevhud/rote1
gibek-skillsjamarpl21/prawo-pl-eli1Reiche das repo mit .claude-plugin/marketplace.json ein und wir listen jedes plugin auf.
Marketplace einreichenCreate original, competition-ready commercial mediation problems and confidential packets with iterative validation. Use when a user provides a topic, industry, dispute sketch, or existing general-information packet and wants a public packet plus confidential packets for both sides—or one named side. Build a narrow but workable settlement corridor; verify factual and numerical consistency, information asymmetry, balance, originality, and commercial feasibility; and revise until deterministic and semantic exit criteria pass. Do not use merely to summarize an existing packet or to advise parties in a real dispute. Requires local file access; Python 3 is recommended for bundled deterministic checks, with disclosed manual fallbacks when unavailable.
Analyze a U.S. Supreme Court oral argument transcript for a lawyer who has not read it: each advocate's position, authorities discussed, the bench's concerns, tentative judicial leanings, possible dispositions, and advocacy strengths and weaknesses, with transcript pinpoints. Use for oral argument analysis, a SCOTUS argument report, or an assessment of how the justices approached a case. Requires readable transcript content; research, Python tallying, and document creation are optional, with disclosed fallbacks.
Ai2 Asta MCP — Semantic Scholar academic graph over MCP (no Python). Intent-to-tool routing, safe fields defaults to avoid context blowups, citation traversal, author lookup, snippet evidence retrieval, and DOI/arXiv/PMID via externalIds.
Journal impact factor (JCR IF) lookup — find a journal's IF by name, compare IF across journals, and rank publication venue quality, backed by a bundled journals_if.csv dataset and a local Python CLI
Fetch and export public X (Twitter) data with a self-contained Python CLI built on the twikit library — no developer API keys, just the user's own login cookies. Covers user profiles, a user's tweets/replies/likes, single tweets, threads, search, followers/following, home/bookmark timelines, with JSON/JSONL/CSV/SQLite output and resumable pagination. Use whenever the user wants to scrape, download, collect, archive, monitor, or analyze tweets or X/Twitter accounts, export a timeline or follower list to a file or dataset, pull X data for research, or build a tweet corpus — even if they don't name the tool. Requires the user's own X login cookies (auth_token + ct0, or username/password login).
Property-based testing with Hypothesis (Python), fast-check (JS/TS), proptest (Rust) and jqwik (JVM): generators, shrinking, and CI integration. Agents (property-based-test-author, vacuous-property-critic) write one property per invariant and flag assume-heavy or always-true properties.
functional-testingNew to rote? /rote:setup walks you through importing your MCP servers, compiling one of your skills, and checking it gives the same answer. Compile an AI agent skill into a deterministic workflow for DBOS, Temporal, Cloudflare Workflows, Inngest, or plain Python with /rote:compile, fill in its emitted stub modules with /rote:implement, then trigger the deployed workflows from Claude over MCP with /rote:serve.
Build, configure, flash, test, debug, and recover firmware for ESP32-family boards, including ESP-Mosaico kit bring-up, ESP-IDF C/C++, Arduino/PlatformIO, MicroPython, CircuitPython, ESPHome, Zephyr, Rust, and NuttX. Use when identifying an ESP32 board, choosing a framework, wiring GPIO or peripheral buses, integrating sensors or actuators, diagnosing serial/boot/power/network failures, planning OTA and production security, or automating hardware tests and fault injection. Do not use as a substitute for the exact board schematic, SoC datasheet, or attached component datasheet.
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).
Operate, configure, benchmark, and troubleshoot llama.cpp across CPU, Metal, CUDA, HIP/ROCm, Vulkan, SYCL, and hybrid or multi-GPU systems. Use when installing or building llama.cpp, selecting or inspecting GGUF models, running llama-cli or bounded llama-completion, serving an OpenAI-compatible API with llama-server, tuning memory and performance, or diagnosing backend, context, template, and API failures. Do not use for model training or fine-tuning, general inference-framework selection, llama-cpp-python or other bindings, LlamaIndex, Ollama, or LM Studio operation.
Build type-safe AI agents and graph-based workflows with PydanticAI and PydanticGraph. Agent creation, function tools, capabilities, dependency injection, structured output, streaming, multi-agent patterns, testing, evals, and graph state machines. Use whenever you are building agents, tool-using LLM workflows, or graph-based state machines in Python. Do not use this skill for unrelated requests; route to the nearest named specialist.
Orzeczenia sądów powszechnych (SA/SO/SR) bezpośrednio z Portalu Orzeczeń MS: wyszukiwanie, metryka, pełne uzasadnienia, powołane przepisy, PDF i RSS. Publiczny HTML/XML przez standardową bibliotekę Pythona, bez przeglądarki.
Onboard an existing app to RudderStack AI events (spec v0.3) with the @rudderstack/ai (Node.js), rudderstack-ai (Python) and ai-sdk-go (Go) SDKs: discover AI surfaces, plan ai-events.md, instrument, and verify.
devtoolsPerformance engineering: frontend performance budgets and Core Web Vitals, backend latency, memory and resource profiling, caching, and Python performance.
Agent skill for LandingAI's Agentic Document Extraction (ADE): production-ready document AI with 99%+ accuracy. Convert complex, real-world documents into accurate, structured data with full traceability. Parse, extract, classify, and build end-to-end pipelines through the REST APIs or the Python and TypeScript libraries, without templates or ML training.
Complete skill set for designing FastAPI backends using Domain-Driven Design and Onion Architecture in Python: architecture patterns, presentation layer, testing, and tooling
Opt-in blocking gate. Registers one PreToolUse hook that denies agent edits to trust surfaces and asks a human before force pushes, tags, releases, publishes, and risky deletes. Needs python3 on PATH. See HOOKS.md.
Python unit testing with pytest: fixtures, parametrize, markers, mocking, coverage and CI, plus async tests with pytest-asyncio. Covers framework choice and test conventions, with unittest and doctest as references.
functional-testingAI skill for Python backend development - FastAPI, SQLAlchemy, Redis, security patterns
Skills for interacting with the Cogniac platform via the cogniac CLI and Python SDK
