95 Plugins aus öffentlichen Claude Code marketplaces. Jedes bündelt Skills, Slash-Commands, Agents, Hooks oder MCP servers, die zusammen installiert werden.
Zeige 95 Plugins, die diesen Filtern entsprechen.
365-skillsAgents365-ai/365-skills3
copilot-primitives-claudegbb-acelerators/awesome-harness-primitives2
existential-birdsexistential-birds/beagle1
estuaryestuary/agent-skills1Reiche das repo mit .claude-plugin/marketplace.json ein und wir listen jedes plugin auf.
Marketplace einreichenPython, FastAPI, SQLAlchemy, PostgreSQL, and pytest code review
Derivation skills for transforming Estuary collections in SQL, TypeScript, or Python: filters, aggregations, joins, array flattening, stateful logic, and time windowing
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
Rigorous multi-pass editor for law review articles, student notes, seminar papers and other legal scholarship. Runs six specialized passes — structure, substantive critique, Bluebook citations, grammar and clarity, fact-checking against sources, and a synthesizing memo — and delivers an editorial memo with issues prioritized as critical, substantial or minor and keyed to specific footnotes and paragraphs. Handles articles of any length, including 50,000-word pieces, by chunking on section boundaries. Reads .docx directly. Use when editing, critiquing, workshopping or pre-submission-reviewing academic legal writing. Triggers on "edit my article," "review this draft," "critique my note," "check my Bluebook citations," "is my argument sound," "read my law review piece," "workshop this paper," "pre-submission review." Chunking needs Python 3; fact-checking needs web search; both degrade gracefully without.
Create 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.
Design and implement IoT and embedded solutions: Arduino and Python Azure IoT Edge modules, smart-city architectures, embedded C engineering, and retro circuit mockups.
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.
Find target accounts, firmographics, technographics, and contacts with DiscoLike. Knows when to use the MCP server, CLI, or Python SDK, and how to open an account without a browser.
dataComprehensive collection for building production-ready Python integrations with Microsoft Dataverse. Includes official documentation, best practices, advanced features, file operations, and code generation prompts.
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.
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