>- **WORKFLOW SKILL** — Generate narrated weekly demo videos for azd features. Pulls latest commits, identifies demo-worthy features, researches PRs, and produces MP4 videos with dark-themed slides and neural TTS narration. make demo video, demo for LT, weekly demo video, azd demo. weekly reports (use weekly-report).
npx skills add https://github.com/Azure/azure-dev --skill weekly-demo-video
Generates narrated MP4 demo videos for azd features using Python + Pillow + edge-tts + ffmpeg.
Ensure these tools are installed:
| Tool | Purpose |
|------|---------|
| Python 3 | Script execution |
| Pillow | Slide generation (pip install Pillow) |
| ffmpeg | Video/audio stitch |
| edge-tts | Neural TTS (pip install edge-tts) |
Verify prerequisites (bash):
python3 -c "from PIL import Image; print('ok')"
ffmpeg -version
edge-tts --list-voices > /dev/null
cd <repo-root> # the azure-dev repository root
git checkout main && git pull --rebase
Replace the dates below with the actual week range:
git log --oneline --since="YYYY-MM-DD" --until="YYYY-MM-DD" --no-merges
Use the current week window (7 days). For sprint demos, use a 2-week window.
Group related commits. Skip: typos, CI fixes, test-only, deps bumps, docs-only.
Look for: new commands, UX improvements, perf gains, new flags, agent features.
Use explore agents in parallel to research each feature group (give them specific commit SHAs).
Present a table of proposed demos. Ask user to confirm or adjust before generating.
Follow the conventions strictly:
{{ references/CONVENTIONS.md }}
List generated videos with filenames and durations. Offer short descriptions for docs.
ImageFont.load_default() automatically. Warn the user that slides may look different.All videos go to: <repo-root>/demo-video/
> Note: demo-video/ is listed in .gitignore to prevent accidental commits.
azd_weekly_demo_{date}_{name}.mp4 (e.g. azd_weekly_demo_may_07_exegraph.mp4)azd_sprint_demo_{date}_{name}.mp4 (e.g. azd_sprint_demo_apr_28_agent_sessions.mp4)Create beautiful visual art in .png and .pdf documents using design philosophy. You should use this skill when the user asks to create a poster, piece of art, design, or other static piece. Create original visual designs, never copying existing artists' work to avoid copyright violations.
Creating 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. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.
Improves the quality of images, especially screenshots, by enhancing resolution, sharpness, and clarity. Perfect for preparing images for presentations, documentation, or social media posts.
Downloads videos from YouTube and other platforms for offline viewing, editing, or archival. Handles various formats and quality options.
Lightweight WSI tile extraction and preprocessing. Use for basic slide processing tissue detection, tile extraction, stain normalization for H&E images. Best for simple pipelines, dataset preparation, quick tile-based analysis. For advanced spatial proteomics, multiplexed imaging, or deep learning pipelines use pathml.
Microscopy data management platform. Access images via Python, retrieve datasets, analyze pixels, manage ROIs/annotations, batch processing, for high-content screening and microscopy workflows.
Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when reading, writing, or modifying medical imaging data in DICOM format, extracting pixel data from medical images (CT, MRI, X-ray, ultrasound), anonymizing DICOM files, working with DICOM metadata and tags, converting DICOM images to other formats, handling compressed DICOM data, or processing medical imaging datasets. Applies to tasks involving medical image analysis, PACS systems, radiology workflows, and healthcare imaging applications.
This skill should be used when working with pre-trained transformer models for natural language processing, computer vision, audio, or multimodal tasks. Use for text generation, classification, question answering, translation, summarization, image classification, object detection, speech recognition, and fine-tuning models on custom datasets.
Take azure/weekly-demo-video from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.
The instructions reference pip.
Without those the skill loads but fails at the first command.