eyeball
Document analysis with inline source screenshots. When you ask Copilot to analyze a document, Eyeball generates a Word doc where every factual claim includes a highlighted screenshot from the source material so you can verify it with your own eyes.
Install / Use
npx skills add github/awesome-copilot --skill eyeballInstalls into whichever agent you are using.
SKILL.md
Installable skill definition
Quality Score
Category
Content & MediaSupported Platforms
Tags
Our assessment of eyeball
eyeball scores 90/100 on our quality scale, 74th of 305 Content & Media skills we index (top 25%).
Its SKILL.md is 6.4 KB long, well organised into 13 sections with 11 code examples: a thorough specification that gives an agent plenty to work with.
With 39,348 GitHub stars, it is one of the more widely adopted skills in the catalogue.
Maintenance, license and trust
- The repository was last updated yesterday, so eyeball is actively maintained.
- It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
- Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.
eyeball compared with similar skills
All 4 of these similar skills score higher than eyeball; compare them before choosing.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| eyeball (this skill)by github | 90 | 39.3k | 1d ago | SKILL.md |
| siyuanby siyuan-note | 100 | 46.5k | today | MCP Server |
| algorithmic-artby anthropics | 100 | 177.9k | 2d ago | SKILL.md |
| pptxby anthropics | 100 | 177.9k | 2d ago | SKILL.md |
| designby nextlevelbuilder | 100 | 130.2k | 3d ago | SKILL.md |
Frequently asked questions
- How do I install eyeball?
- Run
npx skills add github/awesome-copilot --skill eyeball. The install tabs above show the steps for each supported agent. - Which AI agents does eyeball work with?
- It is written for GitHub Copilot, as a SKILL.md file. Other agents that read the same format can often use it too.
- Is eyeball safe to use?
- It is MIT-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
- Is eyeball still maintained?
- The repository was last updated yesterday, so eyeball is actively maintained.
Skill content
View source on GitHubname: eyeball description: 'Document analysis with inline source screenshots. When you ask Copilot to analyze a document, Eyeball generates a Word doc where every factual claim includes a highlighted screenshot from the source material so you can verify it with your own eyes.'
Eyeball
Analyze documents with visual proof. When activated, Eyeball produces a Word document on the user's Desktop where every factual assertion includes an inline screenshot from the source material with the cited text highlighted in yellow.
Activation
When the user invokes this skill (e.g., "use eyeball", "run eyeball on this", "eyeball this document"), respond with:
Eyeball is active. I'll analyze the document and produce a Word doc with inline source screenshots so you can verify every claim with your own eyes.
Then follow the workflow below.
Supported Sources
- Local files: Word documents (.docx, .doc), PDFs (.pdf), RTF files
- Web URLs: Any publicly accessible web page
Tool Location
The Eyeball Python utility is located at:
<plugin_dir>/skills/eyeball/tools/eyeball.py
To find the actual path, run:
find ~/.copilot/installed-plugins -name "eyeball.py" -path "*/eyeball/*" 2>/dev/null
If not found there, check the project directory or the user's home directory for the eyeball repo.
First-Run Setup
Before first use, check that dependencies are installed:
python3 <path-to>/eyeball.py setup-check
If anything is missing, install the required dependencies:
pip3 install pymupdf pillow python-docx playwright
python3 -m playwright install chromium
On Windows, also install pywin32 for Word automation:
pip install pywin32
Workflow
Follow these steps exactly. The order matters.
Step 1: Read the source text
Before writing any analysis, extract and read the full text of the source document:
python3 <path-to>/eyeball.py extract-text --source "<path-or-url>"
Read the output carefully. Identify actual section numbers, headings, page numbers, and key language.
CRITICAL: Do not skip this step. Do not write analysis based on assumptions about how the document is structured. Read the actual text.
Step 2: Write analysis with exact citations
For each point in your analysis, you must:
- Reference the correct section number as it appears in the document (e.g., "Section 9" not "Section 8" because you assumed the numbering).
- Reference the correct page number where the section appears in the extracted text.
- Select anchors that are verbatim phrases from the source that directly support your claim.
Step 3: Select anchors correctly
This is the most important step. Anchors determine what gets highlighted in the screenshots.
DO:
- Use verbatim phrases from the source text that directly support your assertion
- Use multiple anchors to span the full range of text the reader should see
- Use specific, uncommon phrases that appear only where you intend
DO NOT:
- Use generic topic labels (e.g., "Confidentiality") that appear throughout the document
- Use section titles alone when they appear as cross-references elsewhere
- Use single common words that match in many places
Examples:
WRONG -- uses a generic topic label that matches everywhere:
{"anchors": ["User-Generated Content"], "target_page": 8}
RIGHT -- uses the specific language that supports the claim:
{"anchors": ["retain ownership", "Ownership of Content, Right to Post"], "target_page": 8}
WRONG -- section title appears as a cross-reference on earlier pages:
{"anchors": ["LIMITATION OF LIABILITY"]}
RIGHT -- includes the section number for precision, targets the correct page:
{"anchors": ["12. LIMITATION OF LIABILITY", "INDIRECT", "CONSEQUENTIAL"], "target_page": 13}
Step 4: Build the analysis document
Construct a JSON array of sections and call the build command:
python3 <path-to>/eyeball.py build \
--source "<path-or-url>" \
--output ~/Desktop/<title>.docx \
--title "Analysis Title" \
--subtitle "Source description" \
--sections '[
{
"heading": "1. Section Title",
"analysis": "Your analysis text here. Reference Section X on page Y...",
"anchors": ["verbatim phrase 1", "verbatim phrase 2"],
"target_page": 5,
"context_padding": 40
},
{
"heading": "2. Another Section",
"analysis": "More analysis...",
"anchors": ["exact quote from source"],
"target_pages": [10, 11],
"context_padding": 50
}
]'
Section object fields:
heading(required): Section heading in the output documentanalysis(required): Your analysis textanchors(required): List of verbatim phrases from the source to search for and highlighttarget_page(optional): Single page number (1-indexed) to search ontarget_pages(optional): List of page numbers to search across (screenshots stitched vertically)context_padding(optional): Padding in PDF points above/below the anchor region (default: 40). Increase for more context.
Step 5: Deliver the output
Save the output to the user's Desktop. Tell the user the filename and that they can open it to verify each claim against the highlighted source screenshots.
Self-Check Before Delivery
Before saving the final document, mentally verify:
- Does each section's analysis text reference the correct section number from the source?
- Are the anchors verbatim phrases that appear on the target page?
- Does each anchor directly support the claim in the analysis, not just relate to the same topic?
- If the screenshot doesn't match the analysis, is the analysis wrong or is the anchor wrong? Fix whichever is incorrect.
Notes
- The output document includes highlighted screenshots that are dynamically sized. If you provide multiple anchors, the screenshot expands to cover all of them.
- When a search term is not found, the output document will note this. If this happens, the anchor was likely not verbatim enough. Adjust and rebuild.
- For web pages, Playwright renders the page to PDF first. The resulting page numbers may differ from what you see in a browser. Use the extracted text output (step 1) to determine correct page numbers.
- If the user has already provided the source text or you have already read it in the current conversation, you can skip step 1. But always verify section numbers and page references against the actual text before writing analysis.
Related Skills
siyuan
46.5kAn open-source, privacy-first, self-hosted knowledge workspace where humans and AI agents work together 开源、隐私优先、自托管的知识工作空间,让人与智能体在此协作
algorithmic-art
177.9kCreating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems.
pptx
177.9kUse this skill any time a .pptx or .potx file is involved in any way — as input, output, or both. This includes: creating slide decks, pitch decks, or presentations; reading, parsing, or extracting text from any .pptx or .potx file (even if the extracted content will be used elsewhere, like in an em…
design
130.2kComprehensive design skill: brand identity, design tokens, UI styling, logo generation (55 styles, Gemini, Atlas Cloud, or MuAPI AI), corporate identity program (50 deliverables, CIP mockups), HTML presentations (Chart.js), banner design (22 styles, social/ads/web/print), icon design (15 styles, SVG…
Languages
Trust signals
From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.
