1.9천건 · 관련도순
Guides you through connecting external services to Claude Code plugins using MCP, a protocol for exposing services as tools. Covers connection types, configuration files, authentication, tool permissions, and testing.
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Inspect and manage BB through its command-line interface. Find the relevant commands for threads, projects, environments, models, and plugins, then check actual status and results while handling identifiers, file paths, and structured output appropriately.
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Add comments to specific elements in desktop Browser tabs and include those references in prompts. Edit or delete annotation pins, or update saved comments through the plugin RPC, a command interface for plugins.
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Update the NotFair plugin to its latest version by refreshing the marketplace repository, installing the release in the plugin cache, and updating installation records. Also handle update prompts raised when a skill starts.
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Create or revise Agent Skills, host plugins and subagents, MCP servers and client configurations, hooks, and marketplace catalogs against their actual specifications. Use when building reusable agent capabilities or consolidating creator tooling without inventing a universal host format. Trigger with "create an agent skill", "build a plugin", "create an MCP server", "add a subagent", or "scaffold an agent integration".
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Discover and validate Agent Skills, host plugins and subagents, MCP servers and configurations, hooks, and marketplace catalogs against the correct authority. Use when performing pre-merge review, external submission checks, portability audits, security checks, or validation of mixed agent-system repositories. Trigger with "validate this skill", "audit this plugin", "check this agent", "review this MCP config", or "is this marketplace ready".
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Create and manage Claude Code plugins with proper structure, manifests, and marketplace integration. Use when creating plugins for a marketplace, adding plugin components (commands, agents, hooks), bumping plugin versions, or working with plugin.json/marketplace.json manifests.
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Use when configuring Maven plugins, setting up common plugins like compiler, surefire, jar, or creating custom plugin executions.
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Use whenever the task involves inspecting, modifying, or building plugins for Bethesda games via the forked xEdit automation daemon. Loads first; routes to the right path (MCP intent tool, MCP atomic passthrough, or sub-agent delegation) and prevents the agent from bypassing the harness.
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Manage Jenkins controllers with jk, including jobs, runs, logs, artifacts, credentials, nodes, queues, and plugins.
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Safely validate, publish, and track a local SKILL.md directory across GitHub, Awesome Codex Plugins, HOL Registry, skills.sh, SkillsMP, LobeHub, ClawHub, and Cursor Directory. Use when the user asks to publish, update, register, or synchronize an agent skill across marketplaces.
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Analyze Swift Package Manager dependencies, package plugins, module variants, and CI-oriented build overhead that slow Xcode builds. Use when a developer suspects packages, plugins, or dependency graph shape are hurting clean or incremental build performance, mentions SPM slowness, package resolution time, build plugin overhead, duplicate module builds from configuration drift, circular dependencies between modules, oversized modules needing splitting, or modularization best practices.
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Configures and enforces SwiftLint in Swift projects using build tool plugins, run scripts, and CI. Covers .swiftlint.yml configuration, disabled_rules, opt_in_rules, only_rules, analyzer_rules, baselines, autocorrect, swiftlint:disable suppressions, reporter formats (sarif, json, checkstyle), strict and lenient modes, SwiftLintBuildToolPlugin via SimplyDanny/SwiftLintPlugins, swift package plugin swiftlint, Xcode run script phases, CI integration, multiple configuration files, and rollout strategies for existing codebases. Use when setting up SwiftLint, configuring lint rules, suppressing warnings, creating baselines, choosing between build tool plugin and run script, or integrating SwiftLint into CI.
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Inventory the installed skills and which plugins contribute them, then flag overlap with the user's own skills — read through the Agent Monitor Config Explorer API. Reads /api/cc-config/skills and /api/cc-config/plugins. Use when managing skills: deduping, deciding what to keep, or tracing a skill back to the plugin that ships it.
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Pins a Bevy 0.20 crate, dev-profile opt-level, and plugin boundaries before gameplay systems exist. Use when starting a windowed or headless Bevy app. Keywords: Bevy, Cargo.toml, DefaultPlugins, MinimalPlugins, opt-level.
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Tests Bevy schedule logic with cargo test, MinimalPlugins, and app.update(). Use for unit tests of systems, resources, and .chain() order. Keywords: Bevy, cargo test, MinimalPlugins, world, libtest.
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Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
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Build AI-enabled .NET applications with Semantic Kernel using services, plugins, prompts, and function-calling patterns that remain testable and maintainable. USE FOR: adding AI-driven prompts, plugins, or orchestration to a .NET app; reviewing kernel construction, service registration, or plugin usage; building function-calling. DO NOT USE FOR: unrelated stacks; generic tasks that do not need this specific guidance. INVOKES: inspect the repository context, edit targeted files, and run relevant build, test, lint, or validation commands when changes are made.
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Drive the contracts engine's runtime surface (the stdio MCP server) to verify changes end-to-end. Scoped to public-plugins/plugins/healthcare/skills/contracts/ and ../../servers/documents/.
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Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
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Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
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Python bridge to ImageJ2/Fiji for macros, plugins (Bio-Formats, TrackMate, Analyze Particles), NumPy↔ImagePlus/ImgLib2 exchange, and ImageJ Ops. Automates Fiji headlessly from Python. Use scikit-image for pure Python without Fiji plugins; napari for visualization.
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Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
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Fix Flutter macOS build failure caused by duplicate camera plugins. Use when: (1) macOS build fails with "Ambiguous use of 'CameraMacosPlugin.register(with:)'" error, (2) Both camera_macos and camera_macos_plus are in pubspec.yaml, (3) Build worked before adding a new camera package. The plugins define the same class name causing Swift registration conflicts.
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