Automação

This skill should be used when the user asks to "add MCP server", "integrate MCP", "configure MCP in plugin", "use .mcp.json", "set up Model Context Protocol", "connect external service", mentions "${CLAUDE_PLUGIN_ROOT} with MCP", or discusses MCP server types (SSE, stdio, HTTP, WebSocket). Provides comprehensive guidance for integrating Model Context Protocol servers into Claude Code plugins for

Create a new skill or update an existing skill that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations. Use when the user asks to \"create a skill\", \"make a new skill\", \"build a skill\", \"scaffold a skill\", \"write a skill for...\", or \"new skill that does...\".

Create a new skill or update an existing skill that extends Codex's capabilities with specialized knowledge, workflows, or tool integrations. Use when the user asks to \"create a skill\", \"make a new skill\", \"build a skill\", \"scaffold a skill\", \"write a skill for...\", or \"new skill that does...\".

Manage Jira issues and Confluence wiki pages in Atlassian Cloud. Use when: (1) searching/creating/updating Jira issues with JQL, (2) searching/reading/creating Confluence pages with CQL, (3) managing Jira workflows, transitions, and comments, (4) browsing Confluence spaces and page hierarchies. Supports OAuth 2.1 via MCP server (recommended) or API token authentication (fallback).

Evaluates API surface design, consistency, and exemplar alignment. Use when reviewing public API changes or before releasing a new API surface.

Async Python patterns via asyncio and aiohttp for I/O-bound concurrency. Use when adding async APIs, handling concurrent I/O, or debugging async code.

Orchestrates full project lifecycle by auto-detecting state and routing to the correct phase. Use when starting or resuming a project mid-workflow.

Tracks quotas, monitors thresholds, and degrades gracefully for rate-limited APIs. Use when integrating external services that impose rate or cost limits.

Provides standardized pytest config, reusable fixtures, and CI integration patterns. Use when setting up or auditing a Python plugin's test infrastructure.

Computes DORA delivery-performance metrics from git and GitHub API. Use when assessing deployment frequency, lead time, or change failure rate.

Profiles Python code for performance bottlenecks and memory issues. Use when Python code is slow or when profiling for optimization before a release.

Orchestrates Spec Driven Development by coordinating spec, plan, and task skills. Use when running the full speckit workflow from spec to implementation.