
Tiger Skills
io.github.timescale由 GitHub 账号 timescale 发布v0.1.4更新于 Oct 24, 2026
Provider agnostic skills implementation, with skills sourced from local paths or GitHub repositories
概览
从本地目录或 GitHub 仓库加载模块化技能包,并将其提供给任何兼容 MCP 的智能体使用。
- 可以做什么
- Tiger Skills 实现了一套与提供商无关的技能系统,兼容 Anthropic 的技能格式。它读取 YAML 配置,其中列出本地技能目录或 GitHub 仓库,每个技能是一个包含 SKILL.md 的文件夹,并可附带脚本、参考资料和资源文件。技能通过渐进式披露提供给智能体,可选的 subagent 工具可将复杂任务拆分为多个较小的智能体实例。客户端可通过连接字符串参数启用或禁用单个技能。
- 适用场景
- 适合希望在不同 LLM 智能体之间共享同一套精选流程知识与工作流的场景,也适合把技能维护在 GitHub 仓库和本地目录中并按需加载。对于已经采用 Anthropic 技能目录结构的团队,它提供了一种可移植的提供方式。
- 运行要求
- 以 stdio 进程在本地运行,可从 npm 包或 OCI 镜像安装。需要 skills.yaml 配置文件(路径由 SKILLS_FILE 指定,默认 ./skills.yaml)。读取私有 GitHub 仓库需要 GITHUB_TOKEN。可选设置包括 SKILLS_TTL、SUBAGENT_DISABLED 和 MCP_PATH。
安装
在 SourceWeft 中
- 打开 控制台中的 Tiger Skills,将其添加到工作区。
- 为需要使用其工具的对话启用该服务。
Desktop only,通过 STDIO。 STDIO 服务会启动本地进程,因此需要 SourceWeft 桌面宿主。
其他 MCP 客户端
参照 仓库 中的启动说明。
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.
An overview of the Skills spec
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.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.
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.
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
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.
Development
Cloning and running the server locally.
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.
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.
Test via HTTP
Test via stdio
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.
来源:README.md,提交 f767fb4
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0版本历史
1- v0.1.4最新Sep 16, 2026
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