Tiger Skills MCP Server
Emulates Claude Skills with any LLM via MCP, enabling agents to use modular skill packages for specialized workflows and tool integrations.
README
Tiger Skills MCP Server
Emulate Claude Skills with any LLM via a Model Context Protocol (MCP) server.
What are Skills?
Skills are modular components that enhance the capabilities of an MCP-compatible agent by providing specific functionalities, workflows, and domain expertise. They transform a general-purpose agent into a specialized agent equipped with procedural knowledge that no model can fully possess.
The goal is to be fully compatible with Anthropic's skill format. See their Agent Skills Spec and related documentation for more details.
<details> <summary><strong>An overview of the Skills spec</strong></summary>
Skill Structure
Skills are modular, self-contained packages that extend agent capabilities by providing specialized knowledge, workflows, and tools. Think of them as "onboarding guides" for specific domains or tasks—they transform the agent from a general-purpose agent into a specialized agent equipped with procedural knowledge that no model can fully possess.
What Skills Provide
- Specialized workflows - Multi-step procedures for specific domains
- Tool integrations - Instructions for working with specific file formats or APIs
- Domain expertise - Company-specific knowledge, schemas, business logic
- Bundled resources - Scripts, references, and assets for complex and repetitive tasks
Anatomy of a Skill
Every skill consists of a required SKILL.md file and optional bundled resources:
skill-name/
├── SKILL.md (required)
│ ├── YAML frontmatter metadata (required)
│ │ ├── name: (required)
│ │ └── description: (required)
│ └── Markdown instructions (required)
└── Bundled Resources (optional)
├── scripts/ - Executable code (Python/Bash/etc.)
├── references/ - Documentation intended to be loaded into context as needed
└── assets/ - Files used in output (templates, icons, fonts, etc.)
SKILL.md (required)
Metadata Quality: The name and description in YAML frontmatter determine when the agent will use the skill. Be specific about what the skill does and when to use it. Use the third-person (e.g. "This skill should be used when..." instead of "Use this skill when...").
Bundled Resources (optional)
Scripts (scripts/)
Executable code (Python/Bash/etc.) for tasks that require deterministic reliability or are repeatedly rewritten.
- When to include: When the same code is being rewritten repeatedly or deterministic reliability is needed
- Example:
scripts/rotate_pdf.pyfor PDF rotation tasks - Benefits: Token efficient, deterministic, may be executed without loading into context
- Note: Scripts may still need to be read by the agent for patching or environment-specific adjustments
References (references/)
Documentation and reference material intended to be loaded as needed into context to inform the agent's process and thinking.
- When to include: For documentation that the agent should reference while working
- Examples:
references/finance.mdfor financial schemas,references/mnda.mdfor company NDA template,references/policies.mdfor company policies,references/api_docs.mdfor API specifications - Use cases: Database schemas, API documentation, domain knowledge, company policies, detailed workflow guides
- Benefits: Keeps SKILL.md lean, loaded only when the agent determines it's needed
- Best practice: If files are large (>10k words), include grep search patterns in SKILL.md
- Avoid duplication: Information should live in either SKILL.md or references files, not both. Prefer references files for detailed information unless it's truly core to the skill—this keeps SKILL.md lean while making information discoverable without hogging the context window. Keep only essential procedural instructions and workflow guidance in SKILL.md; move detailed reference material, schemas, and examples to references files.
Assets (assets/)
Files not intended to be loaded into context, but rather used within the output the agent produces.
- When to include: When the skill needs files that will be used in the final output
- Examples:
assets/logo.pngfor brand assets,assets/slides.pptxfor PowerPoint templates,assets/frontend-template/for HTML/React boilerplate,assets/font.ttffor typography - Use cases: Templates, images, icons, boilerplate code, fonts, sample documents that get copied or modified
- Benefits: Separates output resources from documentation, enables the agent to use files without loading them into context
Progressive Disclosure Design Principle
Skills use a three-level loading system to manage context efficiently:
- Metadata (name + description) - Always in context (~100 words)
- SKILL.md body - When skill triggers (<5k words)
- Bundled resources - As needed by the agent (Unlimited*)
*Unlimited because scripts can be executed without reading into context window.
</details>
Configuration
Environment Variables
SKILLS_FILE: Path to the YAML file configuring the set of skills. Default:./skills.yamlSKILLS_TTL: Time (in milliseconds) to cache loaded skills. Default: 5 minutes
Skills Configuration File
The set of skills is configured via a YAML file. Both local directories and GitHub repositories are supported. Config can point to individual skills or collections of skills.
local-directory-collection:
# A collection of local skills stored in the `./skills` directory.
# Each skill should be in its own subdirectory with a `SKILL.md` file.
type: local_collection
path: ./skills
local-individual-skill:
# An individual local skill stored in the `./skills/skill-name` directory.
type: local
path: ./path-to/individual/skill-name
anthropic-github-collection:
# A GitHub repo containing a collection of skills.
# Each skill should be in its own subdirectory with a `SKILL.md` file.
type: github_collection
repo: anthropics/skills
# path: ./ # not needed for this example since skills are at the root of the repo
# Optionally specify skills/paths to ignore in this collection
ignored_paths:
- .claude-plugin
- document-skills
disabled_skills:
- canvas-design
# Setting enabled_skills will _only_ load the specified skills from the collection
# enabled_skills:
# - frontend-design
# - webapp-testing
single-github-skill-example:
# A GitHub repo containing an individual skill.
type: github
repo: anthropics/claude-cookbooks
path: ./skills/custom_skills/creating-financial-models
Skill names must be unique across all configured skills. Any duplicates will be ignored with a warning.
Connection string parameters
Individual clients can control the set of skills that are enabled, as well as the protocol(s) used, via parameters in the connection string.
enabled_skills: Comma-separated list of skill keys to enable. If not provided, all configured skills are enabled.disabled_skills: Comma-separated list of skill keys to disable. If not provided, no skills are disabled.tools=0: Disable all tools (for resource-only integration).resources=0: Disable all resources (for tool-only integration).
Example
http://tiger-skills-mcp-server/mcp?disabled_skills=foo,bar&resources=0
Subagent Task Execution
This MCP server (optionally) provides a subagent tool that can be used to break up complex tasks into smaller subtasks, each handled by its own agent instance. This is useful for tasks that require multiple steps, especially when those steps may require consuming large amounts of data in LLM context.
The subagent will automatically have access to the same set of skills as configured for the view skill tool, as well as the ability to (recursively) invoke further subagents. In addition, a mcp.yaml configuration file can be provided to specify additional tooling to be made available to the subagent.
If you do not wish to use subagents, you can set SUBAGENT_DISABLED=true in the environment to disable the tool.
MCP Configuration for Subagents
Create a mcp.yaml file to specify additional tools for the subagent to have accessible. This file is read from the root directory by default, or you can specify a different path via the MCP_PATH environment variable.
Only the streamable HTTP transport is supported at this time.
pg_aiguide:
type: http
url: https://mcp.tigerdata.com/docs
Development
Cloning and running the server locally.
git clone git@github.com:timescale/tiger-skills-mcp-server.git
Building
Run ./bun i to install dependencies and build the project. Use ./bun watch to rebuild on changes.
You will need a GitHub token with the correct scopes. Here is a direct link to create such a new token.
Create a .env file based on the .env.sample file.
cp .env.sample .env
Then update the GITHUB_TOKEN value in .env.
Testing
The MCP Inspector is a very handy to exercise the MCP server from a web-based UI.
./bun inspector
Test via HTTP
./bun watch http
| Field | Value |
|---|---|
| Transport Type | Streamable HTTP |
| URL | http://localhost:3001/mcp |
Test via stdio
./bun watch stdio
| Field | Value |
|---|---|
| Transport Type | STDIO |
| Command | node |
| Arguments | dist/index.js |
Testing in Claude Desktop
Create/edit the file ~/Library/Application Support/Claude/claude_desktop_config.json to add an entry like the following, making sure to use the absolute path to your local tiger-skills-mcp-server project, and use a valid GitHub token.
{
"mcpServers": {
"tiger-skills": {
"command": "node",
"args": [
"/absolute/path/to/tiger-skills-mcp-server/dist/index.js",
"stdio"
],
"env": {
"GITHUB_TOKEN": "ghp_whatever"
}
}
}
}
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。