Skill7
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., "@Skill7find a skill for Docker security best practices"
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.
๐ก Why This Exists
Stop searching. Start building.
You spend hours searching for best practices, design patterns, and solutions. This MCP server gives your AI instant access to 634+ curated skills covering:
๐ Security โ Penetration testing, vulnerability scanning, secure coding
โ๏ธ Frontend โ React, Next.js, Vue, Angular, modern CSS
๐ Backend โ Python, Node.js, Go, Rust, API design
โ๏ธ Infrastructure โ Docker, Kubernetes, AWS, Terraform
๐ง AI/ML โ LangChain, RAG, prompt engineering
๐ Architecture โ System design, microservices, DDD
Related MCP server: AgentSkillsHub MCP Server
โก Quick Start
One-Liner Setup
# Clone and install
git clone https://github.com/Abderraouf-yt/skill7.git && cd skill7
npm install && npm run build
# Configure your AI client (pick one)
npm run setup:gemini # Gemini CLI
npm run setup:claude # Claude Desktop / Code
npm run setup:cursor # Cursor
npm run setup:all # All clientsRestart your AI client. Done.
๐ฏ Features
๐ Universal Compatibility
Works with any MCP client:
Gemini CLI
Claude Desktop & Code
Cursor
VS Code Copilot
Custom integrations
๏ฟฝ Premium UI Experience
Immersive, modern interface:
Interactive 3D Orb background (OGL)
Glassmorphism design system
Stable Grid Layout with load-more
Mobile-first responsive design
๐ 634+ Skills
Curated, production-tested guidance:
Agentic Systems & Orchestration
Generative AI Engineering
Platform Engineering practices
Web3 & Security protocols
๐ง 6 Tools + 2 Prompts
Everything you need:
Semantic Skill Matcher
Workflow Generator
Deep Skill Documentation
Smart Categorization
๏ฟฝ Modern Skill Taxonomy (2026 Standard)
Category | Skills | Highlights |
๐ค Agentic Systems | 98 | Autonomous Agents, Swarms, Memory, Tool Use |
๐ง Generative AI | 85 | LLMs, Inference, Transformers, Prompt Eng |
โ๏ธ Platform Eng | 72 | Cloud, Kubernetes, Terraform, Microservices |
๐จ Product Exp | 95 | Next.js, React, Mobile, Accessibility, UI/UX |
๏ฟฝ๏ธ Security | 107 | DevSecOps, Red Teaming, Auth, Encryption |
๐ฎ Game & 3D | 45 | Unity, Three.js, OGL, WebGL, Shaders |
๏ฟฝ Knowledge | 60 | RAG, Vector DBs, Semantic Search |
โก Automation | 40 | Playwright, Crawlers, ETL Pipelines |
๏ฟฝ Web3 Core | 35 | Smart Contracts, DeFi, Solidity |
๐ API Endpoints (HTTP Mode)
When running with MCP_TRANSPORT=http:
Endpoint | Method | Description |
| GET | Server info & health |
| GET | Health check |
| GET | MCP SSE connection |
Environment Variables:
PORTโ Server port (default: 3000)MCP_TRANSPORTโstdioorhttp
๐ฆ Project Structure
skill7/
โโโ src/
โ โโโ index.ts # MCP server (dual transport)
โโโ data/
โ โโโ skills_index.json # 634+ skills catalog
โโโ scripts/
โ โโโ setup.js # One-click configurator
โโโ Dockerfile # Production container
โโโ docker-compose.yml # Easy deployment
โโโ package.json๐ค Contributing
We welcome contributions! See CONTRIBUTING.md.
# Development
npm install
npm run dev # Hot reload with tsx๐ License
MIT ยฉ Skill7
Available Tools
6 toolsget_categoriesA
Get all skill categories with counts
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It indicates a read operation ('Get all') and mentions return content ('with counts'), but does not disclose behavior such as ordering, pagination, or whether empty categories are included.
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, front-loaded with the action and resource, and contains no unnecessary words. It earns its place without redundancy.
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 tool with no parameters and no output schema, the description adequately communicates the purpose and return value. However, it leaves minor ambiguities like sorting order or whether counts include categories with zero skills, which could be clarified.
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 zero parameters, giving a baseline of 4. The description correctly omits parameter details since there are none, and the schema already indicates an empty object.
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 it retrieves all skill categories with counts, using a specific verb and resource. This distinguishes it from sibling tools like list_skills or get_skill, which operate on skills rather than categories.
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 does not mention suitable contexts, exclusions, or why one might choose get_categories over list_skills or search_skills.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_skillB
Get full details of a skill
| Name | Required | Description | Default |
|---|---|---|---|
| skillId | Yes | Skill ID |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations provided, so the description carries the full burden of behavioral disclosure. The description only says 'Get full details' which is read-only by implication, but it does not state this explicitly, nor does it mention any potential errors, permissions, or what 'full details' entails. It offers no behavioral insight beyond what the tool name and schema already convey.
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: 'Get full details of a skill.' It is front-loaded with the verb and resource, contains no filler, and every word contributes to clarity.
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 simple get-by-ID tool with one parameter and no output schema, the description is minimally adequate but leaves ambiguity. It says 'full details' but does not specify what those details are, nor does it describe the response shape. Given the lack of an output schema, more detail would be helpful for a complete understanding.
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 schema description coverage is 100% (skillId is described as 'Skill ID'). The description does not add any additional meaning to the parameter beyond what the schema already provides. This meets the baseline for schema-heavy parameter documentation.
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 purpose: to get full details of a skill. It uses a specific verb ('Get') and resource ('skill'), which makes the core action unambiguous. However, it does not distinguish itself from sibling tool get_skill_content, which could be seen as overlapping.
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 full details of a skill identified by an ID. It does not explicitly state when to use this over list_skills or search_skills, nor does it mention alternatives or when not to use it. Usage guidance is implied but not explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_skill_contentC
Read full SKILL.md content
| Name | Required | Description | Default |
|---|---|---|---|
| skillId | Yes | Skill ID |
TDQS
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 does not disclose return format, error behavior, whether raw content is returned, or any side effectsโleaving significant behavioral ambiguity.
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 short sentence with no redundant words. It is appropriately front-loaded and concise, though its brevity contributes to under-specification.
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?
Despite being a simple one-parameter tool with no output schema, the description does not explain return values, usage context, or how it differs from siblings. The lack of annotations increases the need for more contextual detail.
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 already covers the only parameter (skillId) with 100% description coverage. The description adds no additional meaning beyond the schema, so the baseline of 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 a specific verb ('Read') and a specific resource ('full SKILL.md content'). It distinguishes the tool from list_skills and search_skills, though it does not explicitly clarify how it differs from get_skill.
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 its siblings, no preconditions, and no exclusions. It is a bare statement of function without usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_skillsB
List skills with optional category filter
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| offset | No | ||
| category | No | Filter by category |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It only says 'list skills with optional category filter' and does not mention pagination, sorting, return format, or whether the list is complete or partial. The schema includes limit/offset but their presence is not explained.
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, front-loaded sentence with no unnecessary words, making it easy for an agent to quickly parse its purpose.
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 simple listing tool, the description is minimal but adequate for understanding the core action. However, it lacks details about pagination controls (limit/offset) and doesn't clarify how it differs from search_skills, leaving gaps in completeness given the absence of annotations and output schema.
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 coverage is only 33% (category alone has a description); limit and offset have no descriptions. The tool description adds no new meaning for these parameters and merely echoes 'optional category filter', which is already present in the schema. It does not compensate for the undocumented parameters.
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 function with the verb 'List' and resource 'skills', and mentions the optional 'category filter', which differentiates it from sibling tools like get_skill (specific skill retrieval) and search_skills (keyword 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 a general listing use case with optional filtering, but it does not explicitly state when to prefer this over search_skills or get_categories, nor does it mention any exclusions or alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_skillsC
Search skills by keyword
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | Yes | Search query |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It only says 'Search skills by keyword' and reveals nothing about return format, pagination, ordering, default limit behavior, or safety (e.g., read-only). This is a minimal explanation that fails to convey any behavioral trail beyond the tool's name.
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: 'Search skills by keyword'. It is front-loaded with the verb and resource, and every word earns its place. No unnecessary information is included.
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 search tool with no output schema and no annotations, the description is too sparse. It does not explain what the search returns, how results are ordered, or how the limit parameter affects results. It also does not clarify when to select this tool over list_skills or get_skill, making it incomplete for an AI agent to use reliably.
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 only 50% (query has a description, limit does not). The description maps to the query parameter but adds no meaning for the limit parameter. Since schema coverage is not high, the description should compensate, but it does not, leaving limit's purpose and default unexplained.
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') and resource ('skills') with a specific method ('by keyword'). This distinguishes it from sibling tools like list_skills and get_skill, making its purpose unmistakable.
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 such as list_skills or get_skill. It does not mention exclusions or preferred contexts, leaving the agent without explicit decision criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
suggest_workflowB
Get a workflow suggestion for a goal
| Name | Required | Description | Default |
|---|---|---|---|
| goal | Yes | What you want to accomplish |
TDQS
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 only states 'Get a workflow suggestion', which suggests a read-only operation, but does not disclose what a workflow suggestion is, what the output looks like, or whether any processing is involved.
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?
One short, front-loaded sentence with no wasted words. It is concise 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?
The tool is simple, but the description is too sparse. Without an output schema or annotations, the agent has no idea what a workflow suggestion consists of, how it relates to the sibling skill tools, or what to expect in the response.
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 coverage is 100% and the single parameter 'goal' is documented. The description adds no new meaning beyond 'for a goal', so it does not improve on 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 uses a specific verb ('Get') and identifies the resource ('a workflow suggestion') plus the intent context ('for a goal'). This clearly distinguishes it from sibling skill-related tools like list_skills and search_skills.
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 is given on when to use this tool versus the sibling tools. 'For a goal' implies a vague context, but there is no explicit mention of scenarios, prerequisites, or alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
6 tool updates
v1.1.0- First observed
get_categories - First observed
get_skill - First observed
get_skill_content - First observed
list_skills - First observed
search_skills - First observed
suggest_workflow
TDQS
Most tools are distinct, but get_skill and get_skill_content have overlapping purposes since full details likely include content. list_skills and search_skills also have similar retrieval functions, though with different filters.
All tools follow a consistent verb_noun pattern with underscores (list, get, search, suggest), making actions predictable. Minor inconsistencies like singular versus plural nouns are acceptable.
Six tools is well-scoped for a skills management and workflow suggestion server, fitting comfortably in the optimal range.
The skill browsing surface is well covered with list, get, search, and content retrieval, plus categories. However, there are no tools for creating, updating, or deleting skills, and workflow functionality is limited to suggestion only, leaving some potential dead ends.
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Related MCP Connectors
A registry of 5,900+ peer-authored skills any MCP agent can search and load on demand.
Search & install 6,500+ AI agent skills from skills-hub.ai inside any MCP tool.
Governed AI agent skills โ one library, distributed to devs and exposed to remote agents over MCP.
Sharebench โ search & pull AI skills, agents, prompts & playbooks (SKILL.md) into any MCP client
Related MCP Servers
- AlicenseAqualityCmaintenanceEnables AI assistants to search, discover, and get recommendations from 20,000+ skills, tools, agents, rules, and MCP servers.5261MIT
- AlicenseAqualityCmaintenanceEnables AI agents to search, audit, and install open-source AI skills and MCP servers with security grading and quality scoring, directly from MCP clients.3231MIT
- AlicenseAqualityDmaintenanceDelivers on-demand knowledge from a built-in library of 728 agents, skills, patterns, and examples across 26 domains to any MCP-compatible AI tool.6141MIT
- AlicenseNot gradedqualityCmaintenanceEnables AI agents to discover and execute over 1,000 developer skills and tools via MCP and WebMCP, with progressive disclosure for efficient token usage.MIT
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/Abderraouf-yt/skill7'
If you have feedback or need assistance with the MCP directory API, please join our Discord server