fix: lint more READMEs

Author: fiam

.markdownlint.yaml

@@ -3,19 +3,19 @@
 # MD001: Heading levels should only increment by one level at a time
 MD001: false
 
+# MD004: Unordered list style - allow different styles in sublists
+MD004:
+  style: sublist
+
 # MD013: Line length
 MD013:
   line_length: 120
   code_blocks: false
   tables: false
 
-# MD033: Inline HTML
-MD033:
-  allowed_elements: [p, img]
-
 # MD025: Multiple top-level headings allowed
 MD025: false
 
-# MD004: Unordered list style - allow different styles in sublists
-MD004:
-  style: sublist
+# MD033: Inline HTML
+MD033:
+  allowed_elements: [p, img]

adk/README.md

@@ -148,4 +148,7 @@ docker compose down -v
 [DuckDuckGo]: https://duckduckgo.com
 [Docker Compose]: https://github.com/docker/compose
 [Docker Desktop]: https://www.docker.com/products/docker-desktop/
+[Docker Engine]: https://docs.docker.com/engine/
 [Docker Model Runner]: https://docs.docker.com/ai/model-runner/
+[Docker Model Runner requirements]: https://docs.docker.com/ai/model-runner/
+[Docker Offload]: https://www.docker.com/products/docker-offload/

agno/README.md

@@ -1,6 +1,9 @@
 # 🧠 Agno GitHub Issue Analyzer
 
-This project demonstrates a **collaborative multi-agent system** built with [Agno], where specialized agents work together to analyze GitHub repositories. The **Coordinator** orchestrates the workflow between a **GitHub Issue Retriever** agent that fetches open issues via the **GitHub MCP Server**, and a **Writer** agent that summarizes and categorizes them into a comprehensive markdown report.
+This project demonstrates a **collaborative multi-agent system** built with [Agno], where specialized
+agents work together to analyze GitHub repositories. The **Coordinator** orchestrates the workflow
+between a **GitHub Issue Retriever** agent that fetches open issues via the **GitHub MCP Server**, and
+a **Writer** agent that summarizes and categorizes them into a comprehensive markdown report.
 
 > [!Tip]
 > ✨ No complex configuration needed — just add your GitHub token and run with a single command.
@@ -9,24 +12,28 @@ This project demonstrates a **collaborative multi-agent system** built with [Agn
 
 ### Requirements
 
-+ **[Docker Desktop](https://www.docker.com/products/docker-desktop/) 4.43.0+ or [Docker Engine](https://docs.docker.com/engine/)** installed
-+ **A laptop or workstation with a GPU** (e.g., a MacBook) for running open models locally. If you don't have a GPU, you can alternatively use [**Docker Offload**](https://www.docker.com/products/docker-offload).
-+ If you're using Docker Engine on Linux or Docker Desktop on Windows, ensure that the [Docker Model Runner requirements](https://docs.docker.com/ai/model-runner/) are met (specifically that GPU support is enabled) and the necessary drivers are installed
-+ If you're using Docker Engine on Linux, ensure you have Compose 2.38.1 or later installed
++ **[Docker Desktop] 4.43.0+ or [Docker Engine]** installed.
++ **A laptop or workstation with a GPU** (e.g., a MacBook) for running open models locally. If you
+  don't have a GPU, you can alternatively use **[Docker Offload]**.
++ If you're using [Docker Engine] on Linux or [Docker Desktop] on Windows, ensure that the
+  [Docker Model Runner requirements] are met (specifically that GPU
+  support is enabled) and the necessary drivers are installed.
++ If you're using Docker Engine on Linux, ensure you have [Docker Compose] 2.38.1 or later installed.
 + 🔑 GitHub Personal Access Token (for public repositories)
 
 ### Setup
 
 1. **Create a GitHub Personal Access Token:**
-   - Navigate to https://github.com/settings/personal-access-tokens
+   - Navigate to <https://github.com/settings/personal-access-tokens>
    - Create a fine-grained token with **read access to public repositories**
 
    ![GitHub token permissions](./img/github-perms.png)
 
 2. **Configure MCP secrets:**
    - Copy `.mcp.env.example` to `.mcp.env`
    - Add your GitHub token to the `.mcp.env` file:
-   ```bash
+  
+   ```sh
    GITHUB_PERSONAL_ACCESS_TOKEN=your_token_here
    ```
 
@@ -36,44 +43,49 @@ This project demonstrates a **collaborative multi-agent system** built with [Agn
 docker compose up --build
 ```
 
-Using Docker Offload with GPU support, you can run the same demo with a larger model that takes advantage of a more powerful GPU on the remote instance:
+Using Docker Offload with GPU support, you can run the same demo with a larger model that takes advantage
+of a more powerful GPU on the remote instance:
+
 ```sh
 docker compose -f compose.yaml -f compose.offload.yaml up --build
 ```
 
-That's all! The agents will spin up automatically. Open **http://localhost:3000** in your browser to interact with the multi-agent system.
+That's all! The agents will spin up automatically. Open **<http://localhost:3000>** in your browser to
+interact with the multi-agent system.
 
 # 🧠 Inference Options
 
-By default, this project uses [Docker Model Runner] to handle LLM inference locally — no internet connection or external API key is required.
+By default, this project uses [Docker Model Runner] to handle LLM inference locally — no internet
+connection or external API key is required.
 
 If you’d prefer to use OpenAI instead:
 
 1. Create a `secret.openai-api-key` file with your OpenAI API key:
 
-```
-sk-...
-```
+      ```plaintext
+      sk-...
+      ```
 
 2. Restart the project with the OpenAI configuration:
 
-```
-docker compose down -v
-docker compose -f compose.yaml -f compose.openai.yaml up
-```
+      ```sh
+      docker compose down -v
+      docker compose -f compose.yaml -f compose.openai.yaml up
+      ```
 
 # ❓ What Can It Do?
 
 Give it any public GitHub repository and watch the agents collaborate to deliver a comprehensive analysis:
 
-- **Fetch Issues**: The GitHub agent retrieves all open issues with their details
-- **Analyze & Categorize**: The Writer agent classifies issues into categories (bugs, features, documentation)
-- **Generate Report**: Creates a structured markdown summary with issue links and descriptions
++ **Fetch Issues**: The GitHub agent retrieves all open issues with their details
++ **Analyze & Categorize**: The Writer agent classifies issues into categories (bugs, features, documentation)
++ **Generate Report**: Creates a structured markdown summary with issue links and descriptions
 
 **Example queries:**
-- `summarize the issues in the repo microsoft/vscode`
-- `analyze issues in facebook/react`
-- `categorize the problems in tensorflow/tensorflow`
+
++ `summarize the issues in the repo microsoft/vscode`
++ `analyze issues in facebook/react`
++ `categorize the problems in tensorflow/tensorflow`
 
 The **Coordinator** orchestrates the entire workflow, ensuring each agent performs its specialized task efficiently.
 
@@ -122,21 +134,22 @@ flowchart TD
     end
 ```
 
-- The **Coordinator** orchestrates the multi-agent workflow using Agno's team coordination
-- **GitHub Issue Retriever** connects to GitHub via the secure MCP Gateway
-- **Writer** processes and categorizes the retrieved data into structured reports
-- All agents use **Docker Model Runner** with Qwen 3 for local LLM inference
-- The **Next.js UI** provides an intuitive chat interface for repository analysis
++ The **Coordinator** orchestrates the multi-agent workflow using Agno's team coordination
++ **GitHub Issue Retriever** connects to GitHub via the secure MCP Gateway
++ **Writer** processes and categorizes the retrieved data into structured reports
++ All agents use **[Docker Model Runner]** with Qwen 3 for local LLM inference
++ The **Next.js UI** provides an intuitive chat interface for repository analysis
 
 # 🛠️ Agent Configuration
 
 The agents are configured in `agents.yaml` with specific roles and instructions:
 
-- **GitHub Agent**: Specialized in retrieving GitHub issues with precise API calls
-- **Writer Agent**: Expert in summarization and categorization with markdown formatting
-- **Coordinator Team**: Orchestrates the workflow between specialized agents
++ **GitHub Agent**: Specialized in retrieving GitHub issues with precise API calls
++ **Writer Agent**: Expert in summarization and categorization with markdown formatting
++ **Coordinator Team**: Orchestrates the workflow between specialized agents
 
-Each agent uses the **Docker Model Runner** for inference, ensuring consistent performance without external API dependencies.
+Each agent uses the **[Docker Model Runner]** for inference, ensuring consistent performance without
+external API dependencies.
 
 # 🧹 Cleanup
 
@@ -148,12 +161,15 @@ docker compose down -v
 
 # 📎 Credits
 
-- [Agno] - Multi-agent framework
-- [GitHub MCP Server] - Model Context Protocol integration
-- [Docker Compose] - Container orchestration
++ [Agno] - Multi-agent framework
++ [GitHub MCP Server] - Model Context Protocol integration
++ [Docker Compose] - Container orchestration
 
 [Agno]: https://github.com/agno-agi/agno
 [GitHub MCP Server]: https://github.com/modelcontextprotocol/servers
 [Docker Compose]: https://github.com/docker/compose
 [Docker Desktop]: https://www.docker.com/products/docker-desktop/
+[Docker Engine]: https://docs.docker.com/engine/
 [Docker Model Runner]: https://docs.docker.com/ai/model-runner/
+[Docker Model Runner requirements]: https://docs.docker.com/ai/model-runner/
+[Docker Offload]: https://www.docker.com/products/docker-offload/

agno/agent-ui/README.md

@@ -1,8 +1,10 @@
 # Agent UI
 
-A modern chat interface for AI agents built with Next.js, Tailwind CSS, and TypeScript. This template provides a ready-to-use UI for interacting with Agno agents.
+A modern chat interface for AI agents built with Next.js, Tailwind CSS, and TypeScript. This template
+provides a ready-to-use UI for interacting with Agno agents.
 
-<img src="https://github.com/user-attachments/assets/7765fae5-a813-46cb-993b-904af9bc1672" alt="agent-ui" style="border-radius: 10px; width: 100%; max-width: 800px;" />
+<img src="https://github.com/user-attachments/assets/7765fae5-a813-46cb-993b-904af9bc1672" alt="agent-ui"
+style="border-radius: 10px; width: 100%; max-width: 800px;" />
 
 ## Features
 
@@ -18,7 +20,9 @@ A modern chat interface for AI agents built with Next.js, Tailwind CSS, and Type
 
 ### Prerequisites
 
-Before setting up Agent UI, you may want to have an Agno Playground running. If you haven't set up the Agno Playground yet, follow the [official guide](https://agno.link/agent-ui#connect-to-local-agents) to run the Playground locally.
+Before setting up Agent UI, you may want to have an Agno Playground running. If you haven't set up the
+Agno Playground yet, follow the [official guide](https://agno.link/agent-ui#connect-to-local-agents)
+to run the Playground locally.
 
 ### Installation
 
@@ -32,28 +36,29 @@ npx create-agent-ui@latest
 
 1. Clone the repository:
 
-```bash
-git clone https://github.com/agno-agi/agent-ui.git
-cd agent-ui
-```
+    ```bash
+    git clone https://github.com/agno-agi/agent-ui.git
+    cd agent-ui
+    ```
 
 2. Install dependencies:
 
-```bash
-pnpm install
-```
+    ```bash
+    pnpm install
+    ```
 
 3. Start the development server:
 
-```bash
-pnpm dev
-```
+    ```bash
+    pnpm dev
+    ```
 
 4. Open [http://localhost:3000](http://localhost:3000) with your browser to see the result.
 
 ## Connecting to an Agent Backend
 
-By default Agent UI connects to `http://localhost:7777`. You can easily change this by hovering over the endpoint URL and clicking the edit option.
+By default Agent UI connects to `http://localhost:7777`. You can easily change this by hovering over the
+endpoint URL and clicking the edit option.
 
 The default endpoint works with the standard Agno Playground setup described in the [official documentation](https://agno.link/agent-ui#connect-to-local-agents).