Building a News Roundup with Docker Agent, Docker Model Runner, and Skill

Hello, I’m Philippe, and I am a Principal Solutions Architect helping customers with their usage of Docker. I wanted a lightweight way to automate my IT news roundups without burning through AI credits. So I built a Docker Agent skill that uses the Brave Search API to fetch recent articles on a topic, then hands the results to a local model running with Docker Model Runner to analyze the stories and generate a Markdown report. In this setup, Qwen3.5-4B handles the reasoning and skill invocation, while the skill itself does the retrieval work. The result is a simple local workflow for turning a prompt like “use news roundup skill with tiny language models” into a structured news brief I can save, review, and reuse. It is a bit slower than doing the same thing with Claude Code, but that tradeoff works for me: I keep the workflow local, I save my Claude credits, and I get a practical example of how skills make Docker Agent more useful for repeatable tasks. Prerequisites for building the news roundup: - Docker and Docker Compose, obviously. - A Brave Search account with an API key (you can get one here). (There’s a free plan.) - A local model that supports a large context window and knows how to do function calling. I chose to use qwen3.5-4b from Qwen (I went with the Unsloth version), a 4-billion-parameter model optimized for text understanding and generation, with native support for up to 262144 context tokens. I started my tests with qwen3.5-9b, but on my MacBook Air, it’s a bit slow and qwen3.5-4b does the job just fine. Let’s get into the setup. Step-by-step guide to building the news roundup Step 1: Creating the Dockerfile I used an ubuntu:22.04 base image and installed curl to make requests to the Brave Search API. I also copied the docker-agent binary from the docker/docker-agent:1.32.5 image to run the agents. FROM --platform=$BUILDPLATFORM docker/docker-agent:1.32.5 AS coding-agent FROM --platform=$BUILDPLATFORM ubuntu:22.04 AS base LABEL maintainer="@k33g_org" ARG TARGETOS ARG TARGETARCH ARG USER_NAME=docker-agent-user ARG DEBIAN_FRONTEND=noninteractive ENV LANG=en_US.UTF-8 ENV LANGUAGE=en_US.UTF-8 ENV LC_COLLATE=C ENV LC_CTYPE=en_US.UTF-8 # ------------------------------------ # Install Tools #------------------------------------ RUN <<EOF apt-get update apt-get install -y wget curl apt-get clean autoclean apt-get autoremove --yes rm -rf /var/lib/{apt,dpkg,cache,log}/ EOF # ------------------------------------ # Install docker-agent # ------------------------------------ COPY --from=coding-agent /docker-agent /usr/local/bin/docker-agent # ------------------------------------ # Create a new user # ------------------------------------ RUN adduser ${USER_NAME} # Set the working directory WORKDIR /home/${USER_NAME} # Set the user as the owner of the working directory RUN chown -R ${USER_NAME}:${USER_NAME} /home/${USER_NAME} # Switch to the regular user USER ${USER_NAME} Let’s move on to the agent configuration. Step 2: Creating the Docker Agent configuration file For the Docker Agent configuration, I defined a root agent using the brain model, which is an alias for qwen3.5-4b. I also enabled skills support (skills: true) and provided detailed instructions so the agent behaves like an expert IT journalist, capable of searching, analyzing, and summarizing the latest tech news. For the toolsets, Docker Agent ships with some ready-to-use ones, but I preferred a script-type toolset with an execute_command that can run any shell command and capture its output. This gives me the flexibility to interact with the Brave Search API directly from shell commands, without having to implement specific tools for it — and most importantly, it keeps the agent’s instructions lightweight. agents: root: model: brain description: News Roundup Expert skills: true instruction: | You are an expert IT journalist with deep knowledge of software engineering, cloud infrastructure, artificial intelligence, cybersecurity, and the open-source ecosystem. Your role is to gather, analyze, and summarize the latest technology news in a clear, accurate, and engaging way. You write for a technical audience and always provide context, highlight trends, and explain the impact of each piece of news. toolsets: - type: script shell: execute_command: description: Execute a shell command and return its stdout and stderr output. args: command: description: The shell command to execute. cmd: | bash -c "$command" 2>&1 models: brain: provider: dmr model: huggingface.co/unsloth/qwen3.5-4b-gguf:Q4_K_M temperature: 0.0 top_p: 0.95 presence_penalty: 1.5 provider_opts: # llama.cpp flags runtime_flags: ["--context_size=65536"] Now let’s look at the skill. Step 3: Building the news roundup skill I created a news-roundup skill that uses the Brave Search API to search for the latest news on a given topic, enriches each article with additional web searches, and generates a structured Markdown report. Inside the .agents/skills folder, I created a news-roundup directory with a SKILL.md file that describes the skill in detail, with the steps to follow and the commands to execute at each step. ├── .agents │ └── skills │ └── news-roundup │ └── SKILL.md Here’s the content of SKILL.md: --- name: news-roundup description: search the news using Brave News Search API with a query as argument. Use this skill when the user asks to search for recent news or current events. --- # News Roundup ## Purpose Generate a comprehensive Markdown news report on a given topic (default: "small ai local models"). ## Steps to follow ### Step 1 — Search for recent news #### Command to execute ```bash curl -s "https://api.search.brave.com/res/v1/news/search?q=$(echo "$ARGUMENTS_REST" | sed 's/ /+/g')&count=3&freshness=pw" \ -H "X-Subscription-Token: ${BRAVE}" \ -H "Accept: application/json" ``` ### Step 2 — Enrich each article For each article returned in Step 1, use the below command with the article URL to retrieve additional context and details. #### Command to execute ```bash curl -s "https://api.search.brave.com/res/v1/web/search?q=$(echo "$ARTICLE_URL" | sed 's/ /+/g')&count=10" \ -H "X-Subscription-Token: ${BRAVE}" \ -H "Accept: application/json" ``` ### Step 3 — Generate the Markdown report Using all the collected information, write a well-structured Markdown report saved to `/workspace/news-report.md`. The report must follow this structure: ```markdown # IT News Report — {topic} > Generated on {date} ## Summary A short paragraph summarizing the main trends found across all articles. ## Articles ### {Article Title} - **Source**: {source name} - **URL**: {url} - **Published**: {date} {2-3 sentence summary of the article content and its significance for IT professionals} --- (repeat for each article) ## Key Trends A bullet list of the main technology trends identified across all articles. ``` Save the final report to `/workspace/data/news-report-{YYYYMMDD-HHMMSS}.md` using the `write_file` tool, where `{YYYYMMDD-HHMMSS}` is the current date and time (e.g. `news-report-20260318-143012.md`). To get the current timestamp, run: ```bash date +"%Y%m%d-%H%M%S" ``` All that’s left is to create the compose.yml file to launch the agent. Step 4: Updating the compose.yml file Here’s the content of compose.yml. Note: you’ll need a .env file with your Brave Search API key (e.g. BRAVE=abcdef1234567890). services: news-roundup: build: context: . dockerfile: Dockerfile stdin_open: true tty: true command: docker-agent run /workspace/config.yaml volumes: - ./config.yaml:/workspace/config.yaml:ro - ./.agents:/workspace/.agents:ro - ./data:/workspace/data working_dir: /workspace env_file: - .env models: qwen3.5-4b: models: qwen3.5-4b: model: huggingface.co/unsloth/qwen3.5-4b-gguf:Q4_K_M context_size: 65536 And that’s it — everything we need to run our IT news roundup agent. Step 5: Let’s test it out! Just run the following command in your terminal: docker compose run --rm --build news-roundup And ask the agent: use news roundup skill with tiny language models The agent will then execute the news-roundup skill, query the Brave Search API, analyze the articles, and generate a Markdown report in the data folder. Note: this can take a little while, so feel free to grab a coffee (or get some work done). The agent will detect that it needs to run tools (the curl commands from the news-roundup skill) — you can validate each command manually or let the agent run them automatically: Your agent will work for a few minutes… …and at the end, it will give you the path of the generated report, which you can open to read your personalized IT news roundup: You can find examples of generated reports in the data folder of the project on this repository: https://codeberg.org/docker-agents/news-roundup/src/branch/main/data. Final Thoughts That’s the full setup: a Docker Agent skill for news retrieval, the Brave Search API for fresh articles, and Docker Model Runner with Qwen3.5-4B for local analysis and report generation. You now have a fully local IT news roundup agent. I have written a lot of content on use cases for local models, including context packaging and making small LLMs smarter. See you soon for more Docker Agent use cases with local language models!