mcp-atlassian은 AI 어시스턴트를 Atlassian Jira 및 Confluence에 연결하는 Model Context Protocol 서버입니다. MCP 호환 클라이언트를 통해 자연어로 Jira 이슈 검색 및 관리, Confluence 페이지 읽기 및 편집, 프로젝트 관리 작업을 수행할 수 있습니다.
이 스킬은 AI 어시스턴트(예: Claude Code, Cursor)가 Jira 이슈와 Confluence 페이지를 직접 조작할 수 있게 해주는 MCP 서버입니다. 사용자는 자연어 명령만으로 이슈 상태 변경, 프로젝트 조회, Confluence 문서 편집 등 복잡한 프로젝트 관리 작업을 수행할 수 있습니다. uvx, Docker, pip 등 다양한 설치 방식을 지원하며, Atlassian API 토큰만 발급받으면 즉시 사용 가능합니다. AI 에이전트가 실시간으로 Jira/Confluence 데이터를 읽고 쓸 수 있어, 개발 워크플로우를 크게 간소화합니다.
npx skills add https://github.com/agentskillexchange/skills/tree/main/skills/atlassian-mcp-server-jira-confluence스킬 README에서
mcp-atlassian은 AI 어시스턴트를 Atlassian Jira 및 Confluence에 연결하는 Model Context Protocol 서버입니다. MCP 호환 클라이언트를 통해 자연어로 Jira 이슈 검색 및 관리, Confluence 페이지 읽기 및 편집, 프로젝트 관리 작업을 수행할 수 있습니다.
업스트림의 요구 사항 및 주의사항:
기본 사용법 또는 시작하기 노트:
https://id.atlassian.com/manage-profile/security/api-tokens 에 접속하여 토큰을 생성합니다.
업스트림 문서에서 발췌: https://raw.githubusercontent.com/sooperset/mcp-atlassian/HEAD/README.md
This repo contains 20 individual skills — each has its own dedicated page.
Builds citation networks from Semantic Scholar API and CrossRef DOI metadata. Visualizes paper influence graphs using NetworkX, identifies seminal works, and tracks research lineage across fields.
Exposes Advanced Custom Fields data through the WordPress REST API using register_rest_field and acf_format_value. Handles repeater fields, flexible content layouts, and gallery fields with proper serialization.
An ASE skill built around ACF Extended, the WordPress enhancement suite for Advanced Custom Fields that adds field types, admin improvements, front-end forms, options pages, and developer tooling. It is a practical fit for agents working inside complex WordPress content models and custom field workflows.
Converts Advanced Custom Fields field groups into native Gutenberg blocks using the ACF Block API v2 and @wordpress/scripts build pipeline. Maps ACF repeaters, groups, and flexible content to InnerBlocks and block attributes with server-side rendering via acf_register_block_type().
Activepieces is an open-source, self-hostable workflow automation platform with 200+ integrations. It provides a visual builder for creating automated workflows and exposes all its connectors as MCP servers for AI agent use.
ActivityWatch is a privacy-first, open-source automated time tracker that records application usage, browser activity, and AFK status across Windows, macOS, and Linux. With 16k+ GitHub stars, it provides detailed productivity analytics without sending data to external servers.
act is an open-source CLI tool that runs GitHub Actions workflows locally using Docker, enabling fast feedback on workflow changes without pushing to GitHub. It is a standard tool for local Actions development and testing.
Use AgentClick when an agent should pause before risky commands, plans, drafts, or code changes so a human can inspect, edit, approve, or reject them in a purpose-built browser UI.
Use UX/UI Agent Skills when Claude should generate tokens, component specs, accessibility audits, and framework-specific UI code from a repeatable design workflow.
Use Bats-core when an agent needs to turn fragile shell scripts or command-line workflows into something it can verify repeatedly after edits. The agent writes focused Bash tests for success paths, failure paths, and output contracts, then runs them locally or in CI before a refactor, release, or incident fix goes out.
Use Cognee to ingest project knowledge into graph and vector memory so agents can retrieve durable context across sessions and workflows.
Give a coding agent symbol-aware lookup, cross-file rename, and structural edit tools before it starts making brittle text-only changes.
Give LangGraph agents memory management and search tools so they can store, retrieve, and update durable facts across sessions.
Use SimpleMem to store, compress, index, and retrieve text or multimodal memories for agents through MCP or Python integrations.
Keep Claude Code sessions grounded in prior decisions, project context, and daily handoff notes instead of starting from zero every time.
Store embeddings beside application data in Postgres, create vector indexes, and query nearest neighbors for semantic search, RAG, recommendations, or agent memory retrieval.
Find the open PR for the current branch, gather unresolved review comments, and drive a focused comment-resolution workflow with gh-authenticated context.
Use VoltAgent to intercept, validate, and enforce input/output policies in TypeScript agent workflows.
Use Zep as an external context layer for agents that need to store events, assemble temporal graph context, and retrieve relevant memory before model calls.
Calls Adobe Firefly
Extract frames or short clips from videos using ffmpeg.
Summarize or transcribe URLs, YouTube/videos, podcasts, articles, transcripts, PDFs, and local files.
Use when testing, fixing, or extending the OpenClaw Control UI GUI with Vitest + Playwright end-to-end checks, mocked Gateway WebSocket flows, mocked dashboard runs, screenshots/videos, or agent-verifiable browser proof.
Obra superpowers agent skills.
See, Understand, Act on video and audio. See- ingest from local files, URLs, RTSP/live feeds, or live record desktop; return realtime context and playable stream links. Understand- extract frames, build visual/semantic/temporal indexes, and search moments with timestamps and auto-clips. Act- transcode and normalize (codec, fps, resolution, aspect ratio), perform timeline edits (subtitles, text/image overlays, branding, audio overlays, dubbing, translation), generate media assets (image, audio...
End-to-end marketing campaign planning and execution. Covers audience research, positioning, campaign angle definition, landing page copy, email sequences, social posts, ad copy, short-form video scripts, and content calendars. Use as the orchestration layer for multi-channel product launches. Use when planning or executing a multi-channel product launch, or producing landing page, email, social, or ad copy.