Claude Code 플러그인
공개 Claude Code 마켓플레이스의 플러그인 111개입니다. 각 플러그인은 함께 설치되는 스킬, 슬래시 명령, 에이전트, 훅 또는 MCP 서버를 묶습니다.
필터와 일치하는 111개의 플러그인을 표시합니다. 필터 초기화
Modern Python best practices. Use when creating new Python projects, and writing Python scripts, or migrating existing projects from legacy tools.
Implement TypeScript, Python, Go, Rust, native apps, APIs and terminal workflows.
Browser automation MCP server with progressive disclosure design -- uses 3.2x fewer tokens than Playwright MCP across 6 tasks. Single execute_code tool runs Python with async browser functions, persisted login sessions, CLI daemon workflows, and compact description mode for token optimization.
Robot skill bundles for the gap engine — perception, grasping, transport, tracking, and policy-steering strategies (skills/) plus model-backed tool bundles (tools/: SAM3, Grounding DINO, Gemini-ER, Molmo, generic VLM, CuRobo planning, pure-math geometry). Each bundle is an Agent Skills directory; Python deps install via one pip extra per bundle.
Produce real, editable Office files (not markdown): Excel Model (live formulas), Slide Deck (.pptx), and Word Document (.docx). Generated via openpyxl / python-pptx / python-docx — needs a code-execution environment (Claude Code, API code tool, or Claude.ai).
Explicit 8-step empirical-analysis pipeline in the traditional Python econometrics stack (pandas + statsmodels + linearmodels + pyfixest + rdrobust + econml + causalml): cleaning -> variable construction -> Table 1 -> diagnostics -> estimation (OLS/IV/DID/RDD/PSM/SCM/DML/Causal Forest) -> robustness -> mechanism/heterogeneity/mediation -> publication-ready tables & figures. Includes epidemiology and ML-causal modes.
Migrate, modernize, and upgrade codebases to AWS. Transforms .NET Framework to .NET 8/10, mainframe COBOL to Java, VMware VMs to EC2, SQL Server to Aurora, and upgrades Java/Python/Node.js versions and AWS SDKs. AWS Transform - continuous modernization analyzes codebases for tech debt, security issues, and upgrade opportunities, then remediates them.
Eight Kafka skills (topic audit, consumer lag, perf review, schema, security, connectors, DLQ, python client scaffold) powered by the Lenses MCP server
Fifteen-skill bundle covering the AER manuscript lifecycle: topic selection, literature positioning with citation verification, identification, experimental pre-registration with power/MDE sizing, robustness, body-section drafting, introduction, tables/figures, manuscript consistency audit, adversarial referee simulation, replication, submission, rebuttal, and the workflow router, plus the optional StatsPAI implementation engine. Ships with Stata, R, and Python templates plus replication-package skeleton and classic-paper exemplars.
academic
API/interface design and OpenAPI, backend review with integration boundaries, auth/session and authorization, error handling and observability, and Python/FastAPI project review.
Perform language and framework specific security best-practice reviews and suggest improvements. Covers Python, JavaScript/TypeScript, and Go. Use when the user requests security best practices guidance, a security review or report, or secure-by-default coding help.
Turn an interpretive-ambiguity audit of a legal text — contract, statute, regulation, or judicial opinion — into a polished deliverable. Produces a multi-page website (default), a single-page site, a self-contained single-file HTML page, a Word document, a PowerPoint deck, or LaTeX source. Use whenever a user has a stress-test result, ambiguity audit, or "where will this be litigated" scenarios and wants to publish, present, or share them. Triggers on "publish the audit," "make a report from this," "turn this into a website / deck / brief / Word doc," "make a Netlify site," "build slides of these ambiguities," "render the scenarios," "package this stress test," "produce a LaTeX version," and similar. Pairs with the ambiguity-stress-test skill (the underlying analysis) but works on any structured or semi-structured ambiguity analysis. Design is opinionated but colors, fonts and brand label are overridable. Most formats need Python and a writable filesystem; single-file HTML works where
Build, test, and ship voice agents with LiveKit. Skills for the LiveKit Agents SDK (Python and Node.js) covering voice AI architecture, live debugging, turn-level tests, simulations, and deployment, plus the LiveKit Docs MCP server for current docs, SDK code search, and changelogs.
Data science and data engineering agents for visualization (D3, Highcharts), notebooks (Jupyter, Google Colab), R, and Python data engineering.
Rigorous methods for statistics, machine learning, and meta-analysis, from question to paper: pre-specified SAPs, statistical and reporting-checklist review, and reproducible R and Python workflows. Every study compounds.
Self-contained production skill plus on-demand reference skill for Chinese-narration recaps from supported video files: local Python/ffmpeg; remote Xiaomi MiMo ASR, VLM, and default TTS; optional Fish Audio TTS with a separate API key or a self-hosted HTTP TTS endpoint; final playback review required.
Python, 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.