Add eyeball plugin: document analysis with inline source screenshots 🤖🤖🤖 (#1294)

* Add eyeball plugin: document analysis with inline source screenshots

Eyeball generates Word documents where every factual claim includes a
highlighted screenshot from the source material. Supports PDFs, Word
docs, and web URLs. Designed for verifying AI-generated document
analysis against the original source.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* Address review feedback: soft imports, Windows Word support, doc fixes

- Make PyMuPDF/Pillow/python-docx imports soft so setup-check runs
  without dependencies installed
- Add _check_core_deps() guard at CLI command entry points
- Add Windows Word detection in setup-check
- Remove references to setup.sh (not included in plugin)
- Fix usage docstring to show inline JSON instead of filename
- Use consistent <path-to>/eyeball.py paths in SKILL.md

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* Update eyeball plugin with bug fixes and Windows support

- Fix resource leaks (pdf_doc closed in finally blocks)
- Fix Windows Word COM automation (DispatchEx, proper cleanup)
- Fix converter order (Word before LibreOffice on Windows)
- Add source file existence checks with clear errors
- Fix Playwright cache detection for all platforms
- Fix setup.sh error handling (pipefail)
- Fix AppleScript path injection
- Fix highlight padding scaling with DPI
- Add pywin32 as Windows dependency
- Update README with Windows setup instructions

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* Address Copilot code review feedback

- Fix browser leak in render_url_to_pdf (try/finally around Playwright)
- Remove setup.sh references from SKILL.md (not bundled in plugin)
- Use consistent <path-to>/eyeball.py paths in SKILL.md
- Update plugin README install instructions for awesome-copilot
- Add Windows pywin32 install step to SKILL.md

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* fix: update _img_to_bytes docstring to reflect BytesIO return type

Agent-Logs-Url: https://github.com/github/awesome-copilot/sessions/048d704a-d78a-4a46-80d9-a155ff1e0d85

Co-authored-by: dvelton <48307985+dvelton@users.noreply.github.com>

---------

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>

docs/README.skills.md

@@ -146,6 +146,7 @@ See [CONTRIBUTING.md](../CONTRIBUTING.md#adding-skills) for guidelines on how to
 | [eval-driven-dev](../skills/eval-driven-dev/SKILL.md)<br />`gh skills install github/awesome-copilot eval-driven-dev` | Improve AI application with evaluation-driven development. Define eval criteria, instrument the application, build golden datasets, observe and evaluate application runs, analyze results, and produce a concrete action plan for improvements. ALWAYS USE THIS SKILL when the user asks to set up QA, add tests, add evals, evaluate, benchmark, fix wrong behaviors, improve quality, or do quality assurance for any Python project that calls an LLM model. | `references/1-a-project-analysis.md`<br />`references/1-b-entry-point.md`<br />`references/1-c-eval-criteria.md`<br />`references/2a-instrumentation.md`<br />`references/2b-implement-runnable.md`<br />`references/2c-capture-and-verify-trace.md`<br />`references/3-define-evaluators.md`<br />`references/4-build-dataset.md`<br />`references/5-run-tests.md`<br />`references/6-analyze-outcomes.md`<br />`references/evaluators.md`<br />`references/runnable-examples`<br />`references/testing-api.md`<br />`references/wrap-api.md`<br />`resources` |
 | [exam-ready](../skills/exam-ready/SKILL.md)<br />`gh skills install github/awesome-copilot exam-ready` | Activate this skill when a student provides study material (PDF or pasted notes) and a syllabus, and wants to prepare for an exam. Extracts key definitions, points, keywords, diagrams, exam-ready sentences, and practice questions strictly from the provided material. | None |
 | [excalidraw-diagram-generator](../skills/excalidraw-diagram-generator/SKILL.md)<br />`gh skills install github/awesome-copilot excalidraw-diagram-generator` | Generate Excalidraw diagrams from natural language descriptions. Use when asked to "create a diagram", "make a flowchart", "visualize a process", "draw a system architecture", "create a mind map", or "generate an Excalidraw file". Supports flowcharts, relationship diagrams, mind maps, and system architecture diagrams. Outputs .excalidraw JSON files that can be opened directly in Excalidraw. | `references/element-types.md`<br />`references/excalidraw-schema.md`<br />`scripts/.gitignore`<br />`scripts/README.md`<br />`scripts/add-arrow.py`<br />`scripts/add-icon-to-diagram.py`<br />`scripts/split-excalidraw-library.py`<br />`templates` |
+| [eyeball](../skills/eyeball/SKILL.md)<br />`gh skills install github/awesome-copilot 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. | `tools` |
 | [fabric-lakehouse](../skills/fabric-lakehouse/SKILL.md)<br />`gh skills install github/awesome-copilot fabric-lakehouse` | Use this skill to get context about Fabric Lakehouse and its features for software systems and AI-powered functions. It offers descriptions of Lakehouse data components, organization with schemas and shortcuts, access control, and code examples. This skill supports users in designing, building, and optimizing Lakehouse solutions using best practices. | `references/getdata.md`<br />`references/pyspark.md` |
 | [fedora-linux-triage](../skills/fedora-linux-triage/SKILL.md)<br />`gh skills install github/awesome-copilot fedora-linux-triage` | Triage and resolve Fedora issues with dnf, systemd, and SELinux-aware guidance. | None |
 | [finalize-agent-prompt](../skills/finalize-agent-prompt/SKILL.md)<br />`gh skills install github/awesome-copilot finalize-agent-prompt` | Finalize prompt file using the role of an AI agent to polish the prompt for the end user. | None |