Automated summary
Drives a headless Chrome browser through Midscene, operating purely from screenshots with no DOM or accessibility labels required.
Strengths
- Vision-only interaction works on canvas, shadow DOM and non-standard tech stacks
- The browser session persists across CLI calls, so multi-step workflows keep their state
- Strict one-action-at-a-time loop: the agent reads the screenshot before choosing the next step
- Simple npx invocation — only a vision model's credentials need to be supplied
Limitations
- Requires a configured vision model (Gemini, Qwen3-VL, Doubao…) through environment variables or a .env file, with no zero-config fallback
- Each command takes roughly a minute, so long flows are slow and token-heavy
- Background execution and command chaining are explicitly forbidden, so no parallelism
- Does not preserve logged-in sessions or existing browser tabs (Chrome Bridge is suggested instead)
Use it when a page must be opened, data scraped, a form filled, or a UI verified in a fresh headless browser without writing DOM selectors.
Avoid it when the task needs the user's logged-in session or existing tabs, or when speed matters more than vision-based robustness.
Security analysis
CautionThe skill is a legitimate browser-automation wrapper around Midscene.js/Puppeteer. It uses powerful tooling (Bash, npx, headless Chrome, outbound network) but for its stated purpose, with explicit, readable commands and no destructive, exfiltrating, or obfuscated instructions. The main residual risks are ordinary for this class of tool: runtime npm dependency trust, screenshots and page content flowing to a third-party vision model, and an API key stored in env/.env. Hence 'caution' rather than 'safe' or 'risky'.
- •Declares Bash and executes `npx @midscene/web@1` — invokes an npm package downloaded at runtime (supply-chain trust in @midscene/web and its dependencies) with full shell access.
- •Automation is driven by a third-party vision model: page screenshots (potentially containing logged-in content, PII, tokens visible on screen) are sent to the configured external model API, and every `act` prompt is executed by that model.
- •Requires storing `MIDSCENE_MODEL_API_KEY` in environment or a `.env` file in the working directory, which the skill encourages; risk of key exposure in cwd artifacts/logs.
- •Launches a persistent background headless Chrome session across CLI calls; combined with 'click/type anything visible', it can perform authenticated actions (form submits, purchases) on the user's behalf.
- •No destructive filesystem commands, no curl|sh piping, no obfuscated/base64 payloads, no attempt to disable agent safety checks — behavior is transparent and purpose-scoped to browsing.
Examples
Open https://news.ycombinator.com, take a screenshot, and extract the titles and point counts of the top 10 stories as a table.Go to our staging contact page, fill in the name, email and message fields with test data, submit the form, and take a screenshot of the confirmation.Open the checkout page, verify that the promo code field and the total price are visible, and screenshot the result so I can confirm the fix.name: Browser Automation description: | Vision-driven browser automation using Midscene. Operates entirely from screenshots — no DOM or accessibility labels required. Can interact with all visible elements on screen regardless of technology stack.
Opens a new browser tab for each target URL via Puppeteer (headless Chrome).
Use this skill when the user wants to:
- Browse, navigate, or open web pages
- Scrape, extract, or collect data from websites
- Fill out forms, click buttons, or interact with web elements
- Verify, validate, or test frontend UI behavior
- Take screenshots of web pages
- Automate multi-step web workflows
- Run browser automation or check website content
Powered by Midscene.js (https://midscenejs.com) allowed-tools:
- Bash
Browser Automation
CRITICAL RULES — VIOLATIONS WILL BREAK THE WORKFLOW:
- Never run midscene commands in the background. Each command must run synchronously so you can read its output (especially screenshots) before deciding the next action. Background execution breaks the screenshot-analyze-act loop.
- Run only one midscene command at a time. Wait for the previous command to finish, read the screenshot, then decide the next action. Never chain multiple commands together.
- Allow enough time for each command to complete. Midscene commands involve AI inference and screen interaction, which can take longer than typical shell commands. A typical command needs about 1 minute; complex
actcommands may need even longer.- Always report task results before finishing. After completing the automation task, you MUST proactively summarize the results to the user — including key data found, actions completed, screenshots taken, and any relevant findings. Never silently end after the last automation step; the user expects a complete response in a single interaction.
Automate web browsing using npx @midscene/web@1. Launches a headless Chrome via Puppeteer that persists across CLI calls — no session loss between commands. Each CLI command maps directly to an MCP tool — you (the AI agent) act as the brain, deciding which actions to take based on screenshots.
When to Use
Use this skill when:
- The user wants to browse or navigate to a specific URL
- You need to scrape, extract, or collect data from websites
- You want to verify or test frontend UI behavior
- The user wants screenshots of web pages
If you need to preserve login sessions or work with the user's existing browser tabs, use the Chrome Bridge Automation skill instead.
Prerequisites
Midscene requires models with strong visual grounding capabilities. The following environment variables must be configured — either as system environment variables or in a .env file in the current working directory (Midscene loads .env automatically):
MIDSCENE_MODEL_API_KEY="your-api-key"
MIDSCENE_MODEL_NAME="model-name"
MIDSCENE_MODEL_BASE_URL="https://..."
MIDSCENE_MODEL_FAMILY="family-identifier"
Example: Gemini (Gemini-3-Flash)
MIDSCENE_MODEL_API_KEY="your-google-api-key"
MIDSCENE_MODEL_NAME="gemini-3-flash"
MIDSCENE_MODEL_BASE_URL="https://generativelanguage.googleapis.com/v1beta/openai/"
MIDSCENE_MODEL_FAMILY="gemini"
Example: Qwen3-VL
MIDSCENE_MODEL_API_KEY="your-openrouter-api-key"
MIDSCENE_MODEL_NAME="qwen/qwen3-vl-235b-a22b-instruct"
MIDSCENE_MODEL_BASE_URL="https://openrouter.ai/api/v1"
MIDSCENE_MODEL_FAMILY="qwen3-vl"
Example: Doubao Seed 1.6
MIDSCENE_MODEL_API_KEY="your-doubao-api-key"
MIDSCENE_MODEL_NAME="doubao-seed-1-6-250615"
MIDSCENE_MODEL_BASE_URL="https://ark.cn-beijing.volces.com/api/v3"
MIDSCENE_MODEL_FAMILY="doubao-vision"
Commonly used models: Doubao Seed 1.6, Qwen3-VL, Zhipu GLM-4.6V, Gemini-3-Pro, Gemini-3-Flash.
If the model is not configured, ask the user to set it up. See Model Configuration for supported providers.
Commands
Connect to a Web Page
npx @midscene/web@1 connect --url https://example.com
Take Screenshot
npx @midscene/web@1 take_screenshot
After taking a screenshot, read the saved image file to understand the current page state before deciding the next action.
Perform Action
Use act to interact with the page and get the result. It autonomously handles all UI interactions internally — clicking, typing, scrolling, hovering, waiting, and navigating — so you should give it complex, high-level tasks as a whole rather than breaking them into small steps. Describe what you want to do and the desired effect in natural language:
# specific instructions
npx @midscene/web@1 act --prompt "click the Login button and fill in the email field with 'user@example.com'"
npx @midscene/web@1 act --prompt "scroll down and click the Submit button"
# or target-driven instructions
npx @midscene/web@1 act --prompt "click the country dropdown and select Japan"
Disconnect
Disconnect from the page but keep the browser running:
npx @midscene/web@1 disconnect
Close Browser
Close the browser completely when finished:
npx @midscene/web@1 close
Workflow Pattern
The browser persists across CLI calls via a background Chrome process. Follow this pattern:
- Connect to a URL to open a new tab
- Take screenshot to see the current state, make sure the page is loaded.
- Execute action using
actto perform the desired action or target-driven instructions. - Close the browser when done (or disconnect to keep it for later)
- Report results — summarize what was accomplished, present key findings and data extracted during the task, and list any generated files (screenshots, logs, etc.) with their paths
Best Practices
- Always connect first: Navigate to the target URL with
connect --urlbefore any interaction. - Be specific about UI elements: Instead of
"the button", say"the blue Submit button in the contact form". - Use natural language: Describe what you see on the page, not CSS selectors. Say
"the red Buy Now button"instead of"#buy-btn". - Handle loading states: After navigation or actions that trigger page loads, take a screenshot to verify the page has loaded.
- Close when done: Use
closeto shut down the browser and free resources. - Never run in background: Every midscene command must run synchronously — background execution breaks the screenshot-analyze-act loop.
- Batch related operations into a single
actcommand: When performing consecutive operations within the same page, combine them into oneactprompt instead of splitting them into separate commands. For example, "fill in the email and password fields, then click the Login button" should be a singleactcall, not three. This reduces round-trips, avoids unnecessary screenshot-analyze cycles, and is significantly faster. - Always report results after completion: After finishing the automation task, you MUST proactively present the results to the user without waiting for them to ask. This includes: (1) the answer to the user's original question or the outcome of the requested task, (2) key data extracted or observed during execution, (3) screenshots and other generated files with their paths, (4) a brief summary of steps taken. Do NOT silently finish after the last automation command — the user expects complete results in a single interaction.
Example — Dropdown selection:
npx @midscene/web@1 act --prompt "click the country dropdown and select Japan"
npx @midscene/web@1 take_screenshot
Example — Form interaction:
npx @midscene/web@1 act --prompt "fill in the email field with 'user@example.com' and the password field with 'pass123', then click the Log In button"
npx @midscene/web@1 take_screenshot
Troubleshooting
Connection Failures
- Ensure Chrome/Chromium is installed on the system (Puppeteer downloads its own by default).
- Check that no firewall blocks local Chrome debugging ports.
API Key Errors
- Check
.envfile containsMIDSCENE_MODEL_API_KEY=<your-key>. - Verify the key is valid for the configured model provider.
Timeouts
- Web pages may take time to load. After connecting, take a screenshot to verify readiness before interacting.
- For slow pages, wait briefly between steps.
Screenshots Not Displaying
- The screenshot path is an absolute path to a local file. Use the Read tool to view it.
From the file to a first result
The download contains exactly the content displayed on this page. This copy may differ from the latest version in the source repository.
View the source and supporting filesThis download contains SKILL.md only. Any scripts, references, templates and dependencies are not included. Get the required files from the source, preserving their directory structure, before continuing.
1. Download the file
Download SKILL.mdFrom the root of a test project, save the file as .skill-downloads/browser-automation-mm5t8baj/SKILL.md, or use the command below. This folder is for inspecting the skill before installation.
Command for macOS/Linux or WSL: requires a POSIX shell and curl. On Windows without WSL, use the download button and create the folder manually.
mkdir -p '.skill-downloads/browser-automation-mm5t8baj' && curl --fail --show-error --location 'https://skills-guides.com/api/skills/browser-automation-mm5t8baj/download' --output '.skill-downloads/browser-automation-mm5t8baj/SKILL.md.part' && mv '.skill-downloads/browser-automation-mm5t8baj/SKILL.md.part' '.skill-downloads/browser-automation-mm5t8baj/SKILL.md'2. Check prerequisites
Read SKILL.md before using it. If it references scripts or external services, inspect them and prepare the required access in your test environment.
Original excerpt: Prerequisites
Midscene requires models with strong visual grounding capabilities. The following environment variables must be configured — either as system environment variables or in a .env file in the current working directory (Midscene loads .env automatically):
MIDSCENE_MODEL_API_KEY="your-api-key"
MIDSCENE_MODEL_NAME="model-name"
MIDSCENE_MODEL_BASE_URL="https://..."
MIDSCENE_MODEL_FAMILY="family-identifier"
Example: Gemini (Gemini-3-Flash)
MIDSCENE_MODEL_API_KEY="your-google-api-key"
MIDSCENE_MODEL_NAME="gemini-3-flash"
MIDSCENE_MODEL_BASE_URL="https://generativelanguage.googleapis.com/v1beta/openai/"
MIDSCENE_MODEL_FAMILY="gemini"
Example: Qwen3-VL
MIDSCENE_MODEL_API_KEY="your-openrouter-api-key"
MIDSCENE_MODEL_NAME="qwen/qwen3-vl-235b-a22b-instruct"
MIDSCENE_MODEL_BASE_URL="https://openrouter.ai/api/v1"
MIDSCENE_MODEL_FAMILY="qwen3-vl"
Example: Doubao Seed 1.6
MIDSCENE_MODEL_API_KEY="your-doubao-api-key"
MIDSCENE_MODEL_NAME="doubao-seed-1-6-250615"
MIDSCENE_MODEL_BASE_URL="https://ark.cn-beijing.volces.com/api/v3"
MIDSCENE_MODEL_FAMILY="doubao-vision"
Commonly used models: Doubao Seed 1.6, Qwen3-VL, Zhipu GLM-4.6V, Gemini-3-Pro, Gemini-3-Flash.
If the model is not configured, ask the user to set it up. See Model Configuration for supported providers.
The platforms on this page are catalog indications. The paths below follow tool documentation; they do not prove this skill has been run in those tools. No minimum tool version is confirmed for this listing.
3. Load the skill
This file does not provide a usable name/description header for this native procedure. It remains available to download and inspect; use the manual method below.
Manual method or another tool: attach SKILL.md to a conversation, or open it in an agent that can read files. Include any required supporting resources. No universal folder is recognized by every tool.
4. Try a first task
Use a short, synthetic input: an API endpoint, a small document or an interface brief. In a new conversation, attach the file or give the agent its path, then adapt this request:
Read .skill-downloads/browser-automation-mm5t8baj/SKILL.md and apply its instructions to this example: [describe a test input]. Identify any missing files, dependencies or access. Produce the result requested by the skill without inventing data or running unreviewed scripts.Expected result: output matching the skill instructions, or a precise list of missing prerequisites. A read confirmation alone does not validate a first use. If an external resource is unavailable, that path remains untested.
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Santa Method
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Multi-agent adversarial verification with two independent reviewers. Output is checked twice and iteratively fixed until both pass before shipping.