Guides
By AnyCap Team
How to add vision capabilities
to an AI agent
Most AI agents work with text and code, but they cannot see unless you give them a visual capability surface. AnyCap adds image understanding and video analysis so the agent can inspect screenshots, review designs, summarize demos, and reason about visual evidence inside the same workflow.
This guide covers both image read and video read setup for agents such as Claude Code, Cursor, and Codex. The setup is straightforward, but the value comes from what happens after installation: the agent can move from text-only reasoning to visual inspection, extraction, and QA tasks.
Once configured, your agent can treat a screenshot, a UI mockup, or a recorded demo as structured input. That opens up new workflows for bug triage, accessibility review, competitor research, release-note drafting, and design validation.
What you need
- An AI agent that can run shell commands, such as Claude Code, Cursor, or Codex
- Node.js 18+ for skills.sh and npm install support
- A browser for the one-time login flow
- Images or videos to analyze, either as URLs or local files that can be uploaded first
Vision capabilities usually show up as two commands: image read for still images and video read for temporal analysis. Both return structured text that an agent can reason over, summarize, or feed into follow-up actions.
Install the AnyCap skill
# For Claude Code
npx -y skills add anycap-ai/anycap -a claude-code -y
# For Cursor
npx -y skills add anycap-ai/anycap -a cursor -y
This installs the AnyCap skill so your agent can discover image and video analysis without improvising the workflow from scratch. The skill explains commands, setup, and the situations where vision should be used.
Install the AnyCap CLI
curl -fsSL https://anycap.ai/install.sh | sh
Or use npm install -g @anycap/cli. The CLI is the runtime surface that executes image and video reads after the skill tells the agent how to call them.
Log in
anycap login
This authenticates the CLI once so the agent can use visual understanding together with other AnyCap capabilities in the same session.
Use image understanding
# Analyze an image from a URL
anycap image read --url https://example.com/screenshot.png
# Analyze with a specific question
anycap image read --url https://example.com/ui.png --prompt "What accessibility issues do you see?"
The command returns structured details about visible text, objects, layout, and context. Focused prompts usually make the output much more useful for real product work.
Use video analysis
# Analyze a video
anycap video read --url https://example.com/demo.mp4
# Analyze with a focused prompt
anycap video read --url https://example.com/demo.mp4 --prompt "List each feature shown in order"
Video analysis returns scene-level structure, key moments, and temporal relationships, which makes it useful for demos, user recordings, and competitive analysis.
Combine vision in agent workflows
With vision installed, your agent can combine visual input with writing, coding, and planning tasks. That is where the capability becomes more than a captioning tool.
# UI review workflow
"Look at this screenshot and identify any UI issues"
# Video summary workflow
"Watch this demo video and write release notes"
# Combined generation plus vision
"Generate a hero image, then analyze it for brand consistency"
The agent can orchestrate upload, analysis, interpretation, and follow-up actions without forcing the user to manage each step manually.
Where vision adds the most value
UI and QA review
Have the agent inspect screenshots for layout regressions, accessibility problems, text overflow, or visual bugs before a release.
Design and brand review
Ask the agent to compare a mockup against brand guidelines, extract visible text, or summarize the hierarchy and composition of a layout.
Video understanding
Feed a product demo, user recording, or ad creative into the agent so it can summarize scenes, extract key moments, and turn the analysis into notes or tickets.
How agents use vision output well
Vision features are most useful when the analysis becomes part of a larger workflow rather than a one-off caption. For example, an agent can read a screenshot, identify accessibility issues, and then open code files to propose a fix based on what it found.
The same applies to video. A scene-by-scene summary becomes more valuable when the agent turns it into release notes, a QA checklist, or a list of missing product explanations. The capability is not just about describing visuals. It is about helping the agent make decisions with visual evidence.
In practice, focused prompts produce better results than generic ones. Asking 'What is in this image?' is useful, but asking 'What onboarding issues would block a first-time user in this screenshot?' gives the model a sharper evaluation frame.
Common setup and usage mistakes
Forgetting the upload step for local files
If the input is not already reachable by URL, the agent needs to upload it first and then pass the resulting URL to the read command.
Using generic prompts for complex reviews
Broad prompts get broad answers. A targeted question about accessibility, information hierarchy, or feature order produces more actionable output.
Treating vision as a standalone task
The highest leverage comes when the agent uses the visual analysis to drive the next step, such as drafting notes, filing bugs, or making code changes.
FAQ
What is the difference between image read and video read?
Image read analyzes a single visual frame and returns structured details such as objects, visible text, layout, and context. Video read adds temporal understanding, so the output includes scenes, actions, sequence, and key moments over time.
What image and video formats are supported?
Image workflows commonly support formats such as JPEG, PNG, WebP, and GIF, while video workflows commonly support MP4, WebM, and MOV. The easiest pattern is to provide a stable URL or let the agent upload the local file first.
Can vision capabilities work with locally stored files?
Yes. If the file is local, the agent can upload it first and then pass the resulting hosted URL to the image read or video read command. That upload-then-analyze pattern is exactly the kind of operational detail a skill helps automate.
What are good first use cases for vision in an agent?
Strong early use cases include screenshot QA, accessibility review, extracting information from UI mockups, summarizing product demos, and comparing visuals against design or brand expectations.