agentskill.sh용 MCP 서버 -- 최대 규모의 스킬 디렉토리(11만 개 이상의 스킬)에서 AI 에이전트 스킬을 검색, 발견 및 설치하세요.
도구 | 설명 |
| 플랫폼, 카테고리 및 보안 점수 필터를 사용하여 키워드별로 스킬 검색 |
| SKILL.md, 보안 분석 및 품질 검토를 포함한 전체 세부 정보 가져오기 |
| 보안 검사를 거쳐 로컬 스킬 디렉토리에 스킬 설치 |
| 인기(24시간), 트렌드(7일), 최고(전체 기간) 또는 최신 스킬 가져오기 |
| 워크플로우나 역할별로 묶인 큐레이션된 스킬 컬렉션 탐색 |
| 스킬셋의 모든 스킬을 한 번에 설치 |
| 다른 사용자가 최고의 스킬을 찾을 수 있도록 스킬을 1~5점으로 평가 |
| 설치된 스킬에 더 최신 버전이 있는지 확인 |
Related MCP server: aiskillstore
claude mcp add agentskill -- npx -y agentskill-mcp~/Library/Application Support/Claude/claude_desktop_config.json에 추가하세요:
{
"mcpServers": {
"agentskill": {
"command": "npx",
"args": ["-y", "agentskill-mcp"]
}
}
}{
"mcpServers": {
"agentskill": {
"command": "npx",
"args": ["-y", "agentskill-mcp"]
}
}
}연결되면 AI 에이전트가 다음을 수행할 수 있습니다:
검색: "SEO 최적화를 위한 스킬 찾아줘"
필터: "보안 점수 80점 이상인 마케팅 스킬 검색해줘"
탐색: "Claude Code용 인기 스킬 보여줘"
스킬셋: "프론트엔드 개발을 위한 큐레이션된 스킬셋 탐색해줘"
설치: "compound-engineering/frontend-design 스킬 설치해줘"
컬렉션 설치: "seo-starter 스킬셋 설치해줘"
평가: "compound-engineering/frontend-design 스킬을 5/5로 평가해줘"
업데이트: "설치된 스킬의 업데이트 확인해줘"
Claude Code, Cursor, GitHub Copilot, Windsurf, Codex, Gemini CLI, Hermes, ChatGPT, Cline, VS Code, OpenCode, Aider, AMP, Goose, Roo Code, Trae 등 20개 이상의 플랫폼에서 스킬을 사용할 수 있습니다.
MIT
Get full details for a specific skill including its SKILL.md content, security info, and metadata.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Skill slug (e.g. 'seo-optimizer', 'react-best-practices') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It mentions retrieving 'full details' including specific components, but doesn't disclose behavioral traits such as error handling (e.g., what happens if the slug doesn't exist), authentication needs, rate limits, or response format. This leaves gaps for an agent to understand operational nuances.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the purpose and lists key details retrieved. It avoids redundancy and wastes no words, though it could be slightly more structured by separating usage context.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (retrieving detailed skill info), no annotations, and no output schema, the description is minimally adequate. It specifies what details are included, but lacks information on return values, error cases, or behavioral constraints, leaving the agent with incomplete context for reliable invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the 'slug' parameter well-documented in the schema. The description adds no additional parameter semantics beyond implying the slug identifies a 'specific skill', which is already clear from the schema. Baseline 3 is appropriate as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get' and resource 'full details for a specific skill', specifying what information is retrieved (SKILL.md content, security info, metadata). It distinguishes from siblings like 'search_skills' (searching multiple) and 'install_skill' (installing), though not explicitly. However, it doesn't fully differentiate from 'get_trending' which might also retrieve skill details, keeping it at 4.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when you need detailed information for a specific skill, but provides no explicit guidance on when to use this tool versus alternatives like 'search_skills' for broader queries or 'get_trending' for trending skills. It lacks exclusions or prerequisites, relying on context from the tool name and parameters.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get trending and popular skills on agentskill.sh.
| Name | Required | Description | Default |
|---|---|---|---|
| period | No | 'hot' for 24h, 'trending' for 7 days, 'top' for all time (default: trending) | |
| platform | No | Filter by platform: claude-code, cursor, copilot, windsurf, etc. | |
| limit | No | Max results (default: 10) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool 'Get[s] trending and popular skills,' implying a read-only operation, but doesn't address other behavioral aspects such as authentication requirements, rate limits, error handling, or response format. For a tool with no annotation coverage, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It is front-loaded and wastes no space, making it highly concise and well-structured for quick understanding.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a tool with three parameters and no annotations or output schema, the description is incomplete. It lacks details on behavioral traits, usage guidelines, and expected outputs, which are crucial for effective tool invocation. The high schema coverage doesn't compensate for these gaps in contextual information.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with clear documentation for all three parameters (period, platform, limit), including enum values and defaults. The description adds no additional parameter semantics beyond what the schema provides, so it meets the baseline score of 3 for adequate coverage without extra value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get trending and popular skills on agentskill.sh.' It specifies the verb 'Get' and the resource 'trending and popular skills,' making the action and target explicit. However, it doesn't differentiate this from sibling tools like 'get_skill' or 'search_skills,' which might also retrieve skill-related data, so it doesn't reach the highest score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'get_skill' or 'search_skills,' nor does it specify contexts or exclusions for usage. This lack of comparative information leaves the agent without clear direction for tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Install a skill from agentskill.sh to the local skills directory. Downloads the SKILL.md file and any associated files.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Skill slug to install | |
| targetDir | No | Target directory (auto-detected if not provided) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It mentions downloading files, which implies network activity and local file system changes, but doesn't disclose permissions needed, whether it overwrites existing skills, error handling, or rate limits. More behavioral context is needed for a mutation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences with no wasted words, front-loading the main action. It could be slightly improved by integrating parameter hints, but it's efficiently structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a mutation tool with no annotations and no output schema, the description is incomplete. It lacks details on success/failure outcomes, side effects, and error conditions. Given the complexity of installing skills, more context is needed to guide the agent effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents both parameters. The description adds no additional meaning about parameters beyond what the schema provides, such as format examples for 'slug' or default behavior for 'targetDir'. Baseline 3 is appropriate when schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Install a skill') and resource ('from agentskill.sh to the local skills directory'), including downloading specific files. It distinguishes from siblings like get_skill (retrieval) and search_skills (searching), but could be more explicit about the difference from get_trending.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives like get_skill or search_skills. The description implies installation from a specific source (agentskill.sh), but doesn't state prerequisites, when not to use it, or compare with sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search for AI agent skills on agentskill.sh. Returns matching skills with name, description, rating, and install count.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query (e.g. 'seo', 'react', 'testing') | |
| platform | No | Filter by platform: claude-code, cursor, copilot, windsurf, codex, etc. | |
| limit | No | Max results (default: 5, max: 20) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the return format ('Returns matching skills with name, description, rating, and install count'), which is helpful, but lacks critical details like pagination behavior, rate limits, authentication needs, error handling, or whether it's a read-only operation. For a search tool with zero annotation coverage, this leaves significant gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two concise sentences with zero waste, front-loading the core purpose. Every word earns its place, though it could be slightly more informative without losing efficiency. It's appropriately sized for a search tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, no output schema, and three parameters, the description is incomplete. It covers the basic purpose and return fields but misses behavioral traits (e.g., safety, limits), usage context versus siblings, and deeper parameter insights. For a tool with this complexity, it should provide more guidance to the agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all three parameters thoroughly. The description adds no parameter-specific information beyond what's in the schema. Baseline 3 is appropriate when the schema does the heavy lifting, but the description doesn't compensate with additional context like example queries or platform nuances.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Search for AI agent skills') and resource ('on agentskill.sh'), with specific verb+resource pairing. It distinguishes from sibling 'get_skill' (which likely retrieves a specific skill) and 'get_trending' (which likely shows popular skills), but doesn't explicitly contrast with 'install_skill' beyond the different action. The purpose is specific but could be more differentiated.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'get_skill' or 'get_trending'. It doesn't mention prerequisites, use cases, or exclusions. The agent must infer usage from tool names alone, which is insufficient for optimal selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v0.1.0get_skillget_trendinginstall_skillsearch_skillsScored across 4 tools
Each tool has a clearly distinct purpose with no overlap: get_skill retrieves detailed information for a specific skill, get_trending lists popular skills, install_skill handles installation, and search_skills performs keyword-based searches. The descriptions reinforce these distinct roles, making misselection unlikely.
All tool names follow a consistent verb_noun pattern using snake_case (e.g., get_skill, install_skill). The verbs (get, get, install, search) are appropriate and predictable, creating a coherent naming convention throughout the set.
With 4 tools, this server is well-scoped for its purpose of managing AI agent skills. Each tool serves a clear and necessary function (discovery, retrieval, installation), and none feel redundant or missing, making the count appropriate for the domain.
The tool set covers core workflows for skill discovery and installation effectively, including search, trending, detailed retrieval, and installation. A minor gap exists in lifecycle management, such as updating or uninstalling skills, but agents can work around this with the provided tools for most use cases.
Search & install 13,000+ AI agent skills from skills-hub.ai inside any MCP tool.
Marketplace of MCP servers and agent skills, free and paid, where developers publish and monetise.
Search and install 4,000+ security-scanned MCP servers from inside any MCP-aware AI client.
Find MCP servers, skills, plugins, agents, prompts and rules for any task.
