Use when long-lived agents, project memories, system prompts, or playbooks are repeatedly summarized, rewritten, or optimized and risk losing domain detail, provenance, or execution learnings.
日本語の概要は準備中です。原文の説明を表示しています。
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.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Attribution: Sourced from github/awesome-copilot by GitHub Community, licensed MIT. Modified for this collection.
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.
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.
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.
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
Follow these steps exactly. The order matters.
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.
For each point in your analysis, you must:
This is the most important step. Anchors determine what gets highlighted in the screenshots.
DO:
DO NOT:
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}
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.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.
Before saving the final document, mentally verify:
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Use when long-lived agents, project memories, system prompts, or playbooks are repeatedly summarized, rewritten, or optimized and risk losing domain detail, provenance, or execution learnings.
日本語の概要は準備中です。原文の説明を表示しています。
Use this skill when the user explicitly asks to map, document, or onboard into an existing codebase. Trigger for prompts like "map this codebase", "document this architecture", "onboard me to this repo", or "create codebase docs". Do not trigger for routine feature implementation, bug fixes, or narrow code edits unless the user asks for repository-level discovery.
日本語の概要は準備中です。原文の説明を表示しています。
Adds educational comments to a code file so it becomes a learning resource, asking for the file (with a list of close matches) if none is given. Use when preparing code for learners, onboarding, or teaching.
日本語の概要は準備中です。原文の説明を表示しています。
Write, debug, and optimize Adobe Illustrator automation scripts using ExtendScript (JavaScript/JSX). Use when creating or modifying scripts that manipulate documents, layers, paths, text frames, colors, symbols, artboards, or any Illustrator DOM objects. Covers the complete JavaScript object model, coordinate system, measurement units, export workflows, and scripting best practices.
日本語の概要は準備中です。原文の説明を表示しています。
Patterns and techniques for adding governance, safety, and trust controls to AI agent systems. Use this skill when: - Building AI agents that call external tools (APIs, databases, file systems) - Implementing policy-based access controls for agent tool usage - Adding semantic intent classification to detect dangerous prompts - Creating trust scoring systems for multi-agent workflows - Building audit trails for agent actions and decisions - Enforcing rate limits, content filters, or tool restrictions on agents - Working with any agent framework (PydanticAI, CrewAI, OpenAI Agents, LangChain, AutoGen)
日本語の概要は準備中です。原文の説明を表示しています。
Check any AI agent codebase against the OWASP Agentic Security Initiative (ASI) Top 10 risks. Use this skill when: - Evaluating an agent system's security posture before production deployment - Running a compliance check against OWASP ASI 2026 standards - Mapping existing security controls to the 10 agentic risks - Generating a compliance report for security review or audit - Comparing agent framework security features against the standard - Any request like "is my agent OWASP compliant?", "check ASI compliance", or "agentic security audit"
日本語の概要は準備中です。原文の説明を表示しています。