social-media-slideshow-video skill
PIL + ffmpeg slideshow videos: product reviews, promos, TikTok/Reels/Shorts.
Is the social-media-slideshow-video skill safe?
Clean: nothing in its files matched our rules. We read 1 file in the folder on 2026-09-28.
No findings.
Install the social-media-slideshow-video skill
A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description.
git clone --depth 1 https://github.com/kevinnft/ai-agent-skills.git /tmp/ai-agent-skills mkdir -p ~/.claude/skills cp -r /tmp/ai-agent-skills/skills/creative/social-media-slideshow-video ~/.claude/skills/social-media-slideshow-video
In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub
The instructions your agent would load
SKILL.md as published, without the frontmatter. Read it on GitHub
Social Media Slideshow Video Generator
When to use
Use when users request: product review videos, promotional slideshow videos, TikTok/Reels/Shorts content from static images, photo-based video with text overlays, hijab/fashion/beauty review videos, unboxing recap videos, or any image-to-video social media content with designed slides.
Stack
No GPU, no moviepy, no heavy dependencies needed.
Architecture: File-Based Frame Pipeline
Critical: Do NOT store all frames as numpy arrays in memory. A 15-second 1080×1920 video at 24fps = 360 frames × ~6MB each = 2.1GB RAM → OOM kill.
Correct approach:
1. Render each SLIDE as a static PIL Image (5-10 slides in memory is fine)
2. For each slide, generate per-frame variations (fade, zoom) and SAVE AS PNG to tmpdir
3. Feed the PNG sequence to ffmpeg via -i pattern
4. Clean up temp filesWhy not pipe to ffmpeg stdin?
Piping raw RGB frames to ffmpeg stdin causes deadlocks and broken pipe errors in many environments. The file-based approach is robust and debuggable.
Resolution Presets
Slide Types & Design Patterns
1. Title Slide
- Gradient background (soft, matching product color)
- Hero photo in rounded-rect or circular frame with colored border
- Product name + subtitle text centered below
- Tag/category text above photo
2. Detail/Review Slide
- Blurred photo as background (GaussianBlur(radius=20) + dark blend at 0.55)
- Photo at top with gradient fade mask at bottom edge
- Semi-transparent card overlay (RGBA with alpha ~220) with rounded corners
- Bullet points + rating text
3. Verdict/Score Slide
- Gradient background
- Circular photo with colored border
- Large score text (e.g., "9.5/10")
- Verdict text + CTA
Key Techniques
Gradient Background
def gradient_bg(w, h, c1, c2):
img = Image.new("RGB", (w, h))
px = img.load()
for y in range(h):
r = y / h
for x in range(w):
px[x, y] = (int(c1[0]*(1-r)+c2[0]*r), ...)
return imgPhoto with Rounded/Circular Mask
mask = Image.new("L", (size, size), 0)
ImageDraw.Draw(mask).rounded_rectangle([0, 0, size, size], radius=35, fill=255)
# or .ellipse([0, 0, size, size], fill=255) for circular
frame.paste(photo, (x, y), mask)Colored Border Around Photo
bdr = 8
border_img = Image.new("RGB", (size+bdr*2, size+bdr*2), border_color)
border_mask = Image.new("L", border_img.size, 0)
ImageDraw.Draw(border_mask).rounded_rectangle([0,0,...], radius=40, fill=255)
frame.paste(border_img, (x-bdr, y-bdr), border_mask)
frame.paste(photo, (x, y), photo_mask) # photo on topSemi-Transparent Card Overlay
card = Image.new("RGBA", (cw, ch), (255, 248, 240, 220))
cmask = Image.new("L", (cw, ch), 0)
ImageDraw.Draw(cmask).rounded_rectangle([0,0,cw,ch], radius=25, fill=220)
frame_rgba = frame.convert("RGBA")
frame_rgba.paste(card, (x, y), cmask)
frame = frame_rgba.convert("RGB")Blurred Photo Background
bg = prepare_photo(WIDTH, HEIGHT, zoom=1.5)
bg = bg.filter(ImageFilter.GaussianBlur(radius=20))
dark = Image.new("RGB", (WIDTH, HEIGHT), (40, 30, 30))
frame = Image.blend(bg, dark, 0.55)Gradient Fade Mask (photo fading to transparent at bottom)
gmask = Image.new("L", (w, h), 255)
gd = ImageDraw.Draw(gmask)
for y in range(h - fade_height, h):
alpha = int(255 * (1 - (y - (h - fade_height)) / fade_height))
gd.rectangle([(0, y), (w, y)], fill=alpha)
frame.paste(photo, (0, 0), gmask)Fade Transitions Between Slides
black = Image.new("RGB", (WIDTH, HEIGHT), (0, 0, 0))
if frame_idx < TRANSITION_FRAMES:
alpha = frame_idx / TRANSITION_FRAMES
out = Image.blend(black, slide_img, alpha)
elif frame_idx > total - TRANSITION_FRAMES:
alpha = (total - frame_idx) / TRANSITION_FRAMES
out = Image.blend(black, slide_img, alpha)ffmpeg Encoding Command
cmd = [
"ffmpeg", "-y",
"-framerate", str(FPS),
"-i", os.path.join(tmpdir, "frame_%05d.png"),
"-c:v", "libx264",
"-preset", "fast",
"-crf", "23",
"-pix_fmt", "yuv420p",
"-movflags", "+faststart",
output_path,
]
subprocess.run(cmd, capture_output=True, text=True, timeout=300)Font Handling
def get_font(size, bold=False):
fp = "/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf" if bold \
else "/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf"
if os.path.exists(fp):
return ImageFont.truetype(fp, size)
return ImageFont.load_default()Center text: bbox = draw.textbbox((0,0), text, font=f); x = (WIDTH - (bbox[2]-bbox[0])) // 2
Typical Duration & Pacing
Pitfalls
- OOM Kill: Never accumulate all frames as numpy arrays. Use file-based pipeline.
- ffmpeg stdin pipe: Deadlocks on long videos. Use PNG sequence input instead.
- pip on Ubuntu 24.04+: Needs --break-system-packages flag.
- Emoji/Unicode in text: DejaVu fonts don't render emoji. Use text descriptions or install emoji fonts.
- Color matching: Extract dominant color from product photo to build cohesive palette.
- Text centering: Always use textbbox() for accurate width measurement before centering.
Verification
After generating, extract mid-slide frames to verify (not transition frames):
# Extract frame from middle of each slide (not transitions which are black)
ffmpeg -i output.mp4 -vf "select='eq(n\,36)+eq(n\,108)...'" -vsync vfr preview_%d.jpg
ffprobe -v quiet -print_format json -show_format -show_streams output.mp4More skills from kevinnft/ai-agent-skills
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