wjs-overlaying-video

Post-production for a video clip: cover, captions, illustrations, CTA, custom motion graphics — all composed in ONE HyperFrames project and rendered in a SINGLE final encode. No cascade of decodes/re-encodes (each cascade pass degrades quality and burns time).

When to use

• Downstream of `/wjs-segmenting-video` — the segmentation skill

hands you cropped clips + per-clip SRTs; this skill turns them into upload-ready MP4s with cover/captions/illustrations/CTA.

• User has a finished video and wants to dress it up with motion

graphics: opening hook, key-quote callout, closing slogan, chapter cards, AI-generated cover as first frame.

• User wants HTML/CSS-quality captions on a video (kinetic word-by-word

highlighting, custom fonts, large outlined text, seekable per cue).

• User wants illustration overlays at specific hook moments — diagrams,

big text emphasis, flow charts.

Don't use for:

• Splitting one long video into clips → use /wjs-segmenting-video.
• Creating the source SRT → use /wjs-transcribing-audio (then /wjs-translating-subtitles if you need a different language).
• Full HyperFrames productions where the source isn't a fixed video →

use hyperframes directly.

• 微信视频号 / 抖音 upload (no public API for those) → this skill

produces the MP4; upload is manual.

What this skill IS — and IS NOT

The pipeline

clip.mp4 + clip.zh-CN.burn.srt   (from /wjs-segmenting-video hand-off)
   ↓
1. (Optional) Generate AI cover via gpt-image-2
   make_cover.py --segments S.json --out output/ --size 1024x1792
   cover_NN_slug.png

2. Scaffold a HyperFrames project per clip
   hf_clip_NN/1080/{index.html, clip.mp4, cover.png, captions.json}

3. Compose: cover scene + body video + caption track + chapter chip
            + 1-2 illustrations at hook moments + CTA scene

4. npm run check (lint + validate + visual inspect)
   npm run render → upload-ready MP4

A 2-minute vertical 1080×1920 composition renders in ~2-3 min on M-series Mac.

Color: tone-map HLG/HDR source → SDR BEFORE compositing

Only tone-map genuinely HLG/HDR sources. If the body clip is ALREADY Rec.709 SDR — e.g. a graded multicam render, or polysync output where an S-Log3→709 LUT was already applied — running the HLG tone-map recipe on it washes/darkens the already-correct color. build_hf_clips.py's tonemap_to_sdr now probes color_transfer (_is_hlg_hdr): HLG/PQ → tone-map; otherwise a straight re-encode with dense keyframes (no tone-map). Either way you still get the -g 30 dense-keyframe encode HyperFrames needs.

iPhone / modern-camera footage is often HLG HDR (bt2020 / arib-std-b67). If you feed that straight into HyperFrames it either renders washed-out ("发白") or, with a naive --sdr, too dark ("发黑"); and the HDR x265 path can hang the renderer. Pre-convert the body clip to SDR (bt709) 30fps h264 with a locked zscale tone-map, then composite the SDR clip.

The verified recipe (tonemap_to_sdr() in build_hf_clips.py). npl=203 matches macOS-native (qlmanage) reference brightness; hable keeps contrast; this preserves the ORIGINAL look (natural skin / foliage / brick), no wash, no darkening:

# zscale-capable ffmpeg — Homebrew's lacks zscale/tonemap.
# imageio-ffmpeg ships one: .../imageio_ffmpeg/binaries/ffmpeg-macos-aarch64-v7.1
TONEMAP_VF = ("zscale=tin=arib-std-b67:min=bt2020nc:pin=bt2020:t=linear:npl=203,"
              "format=gbrpf32le,tonemap=tonemap=hable:desat=0,"
              "zscale=t=bt709:m=bt709:p=bt709:r=tv,format=yuv420p,fps=30")
# encode: libx264 -crf 18 -color_primaries/-trc/-colorspace bt709
#         -g 30 -keyint_min 30 -movflags +faststart   ← see gotcha below

Dense-keyframe gotcha. HyperFrames seeks the body video frame-by-frame. A clip with sparse keyframes (long GOP) makes it freeze on stale frames — the render log warns Video "video" has sparse keyframes. Always encode the SDR clip with -g 30 -keyint_min 30 (one keyframe per frame-second) so every seek lands clean.

Verify the render log says No HDR sources detected — rendering SDR. If it says HDR detected, your clip wasn't tone-mapped — fix that first.

Version stamp (every output)

Stamp 「skill名字 + 版本号」 bottom-right, shown during the END/CTA scene, so every render is traceable to the pipeline version that made it. Bump VERSION in build_hf_clips.py on each pipeline change.

#ver-stamp { position: absolute; right: 28px; bottom: 28px; z-index: 30;
  font-size: 20px; color: rgba(150,150,156,0.55); letter-spacing: 0.06em; }

<div id="ver-stamp" class="clip" data-start="{cta_start}" data-duration="{cta_dur}"
     data-track-index="2">wjs-overlaying-video v1.3</div>

Standard overlay types (the 6 building blocks)

Every clip's final composition is built from some combination of these. The agent picks the right ones per clip — typically all 6 for a podcast highlight, or just 1-2 for a single annotation overlay.

1. cover — full-frame AI image as first frame

The cover IS the first frame (no animation, no zoom) so platforms that auto-pick the first frame as the thumbnail get your designed cover by default. Always verify with `ffmpeg -ss 0 -vframes 1` — frame 0 must NOT be black or platform thumbnails will be black.

HTML:

<div id="cover" class="clip" data-start="0" data-duration="1.6"
     data-track-index="1" data-layout-allow-overflow>
  <img src="cover.png" alt="" data-layout-allow-overflow />
</div>

CSS:

#cover { position: absolute; inset: 0; background: #0c0d10; overflow: hidden; }
#cover img { position: absolute; inset: 0; width: 100%; height: 100%; object-fit: cover; }

Generation: use /wjs-segmenting-video/scripts/make_cover.py (wraps gpt-image-2 images edit with the midpoint frame as ref):

# For 1080×1920 vertical output (视频号 / 抖音):
make_cover.py --segments S.json --out output/ --size 1024x1792 [--single N]

# For 1920×1080 horizontal output (YouTube / B站):
make_cover.py --segments S.json --out output/ --size 1536x1024

Aspect must match output frame. --size 1024x1536 (2:3, the script default) gets letterboxed or cropped on 9:16 output — always pass 1024x1792 for vertical. The cover image's aspect is what the viewer sees full-frame, so mismatch is visible. Re-roll one with --single N; codex provider can transient-fail mid-batch.

Codex auth required: the script calls codex CLI via gpt-image-2-skill. If ~/.codex/auth.json is missing, the script errors. See gpt-image-2-skill for setup.

Reference frame must match the OUTPUT orientation. make_cover reads output/frame_NN_slug.jpg as the photographic background it keeps. For a vertical clip that came from a horizontal two-person source, the default frame_NN is the horizontal two-shot — feeding that to a 1024x1792 cover crams both people into portrait awkwardly. Replace frame_NN_slug.jpg with a vertical single-speaker frame pulled from the already-cropped body clip first (ffmpeg -ss <t> -i clip_vert.mp4 -frames:v 1 frame_NN_slug.jpg), then run make_cover. The cover then matches the body framing.

Baked-title cover ⇒ drop the animated `#hook` opener. make_cover stamps the segment title into the cover image (white fill + heavy black stroke, placed clear of faces). That cover IS the title card. Do NOT also run the animated #hook opener over it (overlay type below) — you'd double-stamp the title. Pick one: either a make_cover baked-title cover (then leave HOOK empty), or a plain video-frame cover + animated hook. The house default the user approved is the make_cover baked-title cover (a clean video frame with the title burned in, no AI painting).

2. caption — 关键词高亮 captions (字幕风格 03) synced to SRT

Chosen style for 王建硕 (user-approved): 字幕风格 03「关键词高亮」+ 思源宋体 Noto Serif SC. Serif white text with a black stroke, and punchy QUANTITATIVE keywords (倍数 / 大数量级 / 百分比) wrapped in a small gold gradient block. Captions are vertically centered in a fixed zone (so 1-line vs 2-line cues don't make the visual center jump up and down).

There were 4 candidate styles (描边白字 / 质感底条 / 关键词高亮 / 逐字点亮); the user picked 03 关键词高亮 with serif sc font. Use that. The plain-stroke style (-webkit-text-stroke: 5px #000, no gold block, sans font) is the fallback if a clip has no quantitative keywords to highlight.

Font — load Noto Serif SC from Google Fonts in `<head>` (the HyperFrames compiler fetches & inlines requested Google font families automatically; verify the render log says Fetched … Noto Serif SC):

<link rel="preconnect" href="https://fonts.googleapis.com">
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
<link href="https://fonts.googleapis.com/css2?family=Noto+Serif+SC:wght@600;700;900&display=swap" rel="stylesheet">

HTML:

<div id="caption" class="clip" data-start="{body_start}"
     data-duration="{body_dur}" data-track-index="4"></div>

CSS (vertical 1080×1920) — 字幕风格 03:

#caption {
  position: absolute; left: 0; right: 0; bottom: 240px;
  height: 240px; z-index: 10; overflow: visible;
}
#caption .bubble {
  position: absolute; top: 50%; left: 50%;
  display: inline-block; padding: 0 24px;
  font-family: "Noto Serif SC", "Songti SC", "STSong", serif;
  font-size: 52px; line-height: 1.32; font-weight: 700;
  color: #fff; max-width: 980px; text-align: center;
  -webkit-text-stroke: 2.5px rgba(0,0,0,0.9);
  paint-order: stroke fill;
  text-shadow: 0 2px 8px rgba(0,0,0,0.7), 0 0 2px rgba(0,0,0,0.9);
  letter-spacing: 0.01em;
}
#caption .bubble .hot {        /* gold keyword block */
  color: #1a1206; -webkit-text-stroke: 0;
  background: linear-gradient(180deg, #f3c877, #c79655);
  padding: 2px 12px; border-radius: 9px; margin: 0 3px;
  box-shadow: 0 3px 10px -3px rgba(232,176,99,0.6);
}

Keyword auto-selection (sparse on purpose). Wrap only genuinely emphatic magnitudes so the gold block stays meaningful, not noisy. Deliberately EXCLUDE generic 个/年 ("一个", "20年"). Handles thousands-commas ("1,000万"). build_hf_clips.py does this in mark_keywords():

_NUM = r"[0-9０-９,，一二三四五六七八九十百千两零几]+"
_HOT_RE = re.compile(rf"(?:翻了?{_NUM}?[倍番]|{_NUM}\s*(?:[倍番]|万亿?|亿|％|%))")
# → highlights: 一倍 五六倍 十倍 10倍 50万 800万 1,000万 50% 翻一倍
# render the cue with b.innerHTML = g.html (HTML-escape the non-keyword text)

JS (one bubble per cue + GSAP fade in/out, all centered at container midpoint):

// SRT cues are loaded as inline JSON. Each cue's start/end is offset
// by the cover-scene duration (e.g., 1.5s) so the timing aligns with
// the composition timeline (not the body's own t=0).
const captionEl = document.getElementById("caption");
const groups = JSON.parse(document.getElementById("captions-data").textContent);
const bubbles = groups.map((g, i) => {
  const b = document.createElement("span");
  b.className = "bubble"; b.id = "cap-" + i;
  b.innerHTML = g.html || g.text;   // g.html has <span class="hot"> keyword blocks
  b.style.opacity = "0";
  captionEl.appendChild(b);
  return b;
});
// GSAP xPercent/yPercent for centering (CSS transform would get
// overwritten the moment we tween y).
gsap.set(bubbles, { xPercent: -50, yPercent: -50 });
groups.forEach((g, i) => {
  const el = bubbles[i];
  tl.fromTo(el, { opacity: 0, y: 12 }, { opacity: 1, y: 0, duration: 0.18, ease: "power2.out" }, g.start);
  const exitStart = Math.max(g.start + 0.18, g.end - 0.12);
  tl.to(el, { opacity: 0, duration: 0.12, ease: "power2.in" }, exitStart);
  tl.set(el, { opacity: 0 }, g.end);
});

Source SRT — slice + shift before inlining. Prefer the word-timed `.asr.srt` built by /wjs-transcribing-audio (火山 streaming ASR → build_srt_from_asr.py) — its per-word timing means cues sit exactly on the spoken audio with no drift. Parse each cue, add the cover duration to every start/end, run mark_keywords() to produce the html field, and inline as JSON in a <script id="captions-data" type="application/json"> block.

MarginV / position notes:

• Vertical (1080×1920): bottom: 240px keeps captions clear of the

视频号/抖音 bottom UI overlay (likes/comments/share buttons).

• Horizontal (1920×1080): bottom: 100px, font-size: 48px,

-webkit-text-stroke: 4px is a reasonable default.

Caption length cap. If a single cue exceeds ~18 Chinese chars on 1080-wide at 56px, it wraps to 2 lines awkwardly. This is upstream discipline — /wjs-translating-subtitles should cap cues at ~18 chars using word-gap split + punctuation split. If you receive longer cues, either reduce font-size to 48px or accept the wrap.

3. chapter — top-left chapter chip (4s reveal then fade)

A subtle badge identifying the segment. Enters at body start, fades after a few seconds so it doesn't compete with the rest of the composition.

HTML:

<div id="chapter" class="clip" data-start="{body_start}"
     data-duration="{body_dur}" data-track-index="3">
  <span class="dot"></span>
  <span class="text">第一段 · 自然语言才是新代码</span>
</div>

CSS:

#chapter {
  position: absolute; top: 80px; left: 60px; z-index: 9;
  display: inline-flex; align-items: center; gap: 12px;
  padding: 12px 20px;
  background: rgba(12,13,16,0.78);
  border: 1px solid rgba(199,150,85,0.4);
  border-radius: 999px;
}
#chapter .dot { width: 10px; height: 10px; border-radius: 999px; background: #e8b063; }
#chapter .text {
  font-size: 24px; color: #f4f4f5; letter-spacing: 0.04em; font-weight: 600;
}

GSAP:

tl.from("#chapter", { x: -40, opacity: 0, duration: 0.5, ease: "expo.out" }, body_start + 0.4);
tl.to("#chapter", { opacity: 0, duration: 0.4, ease: "power2.in" }, body_start + 4.0);

4. stack illustration — top-right vertical list card

A list of items (e.g., language hierarchy, workflow steps, levels) in a dark card at the top-right. One item can be accented in amber to highlight the relevant level/step.

Use for: showing a hierarchy or list while the speaker explains it. Card stays visible 8-50s.

HTML:

<div id="ill-stack" class="clip" data-start="{start}" data-duration="{dur}" data-track-index="5">
  <div class="ill-card">
    <div class="ill-card-label">我们写的层级</div>
    <div class="ill-row"><span class="ill-tag accent">自然语言</span></div>
    <div class="ill-row"><span class="ill-tag">Python</span></div>
    <div class="ill-row"><span class="ill-tag">C</span></div>
    <div class="ill-row"><span class="ill-tag">Assembly</span></div>
  </div>
</div>

CSS: (see references/illustration_patterns.md for the full canonical CSS — copy verbatim)

GSAP — slide in from right + stagger rows:

tl.fromTo("#ill-stack", { x: 360, opacity: 0 }, { x: 0, opacity: 1, duration: 0.6, ease: "expo.out" }, start + 0.2);
tl.from("#ill-stack .ill-row", { y: 20, opacity: 0, duration: 0.4, stagger: 0.12, ease: "power2.out" }, start + 0.4);
tl.to("#ill-stack", { x: 360, opacity: 0, duration: 0.5, ease: "power2.in" }, end - 0.5);

5. hammer illustration — center-frame big equation/text overlay

A BIG center-frame text/equation that visually "hammers" a key claim. Best for the single most quotable moment in a clip (e.g., "LLM = 编译器", "Token = 新 GDP", "AI ≠ 更快的轿子"). Visible 4–8s.

HTML:

<div id="ill-hammer" class="clip" data-start="{start}" data-duration="{dur}" data-track-index="6">
  <div class="ill-h-content">
    <div class="ill-h-eq">
      <span class="ill-h-left">LLM</span>
      <span class="ill-h-equals">=</span>
      <span class="ill-h-right">新编译器</span>
    </div>
    <div class="ill-h-foot">自然语言 → Python → 汇编</div>
  </div>
</div>

GSAP — scale-pop entrance + stagger each piece + scale-fade exit:

tl.fromTo("#ill-hammer", { scale: 0.85, opacity: 0 },
  { scale: 1.0, opacity: 1, duration: 0.45, ease: "back.out(1.6)" }, start);
tl.from("#ill-hammer .ill-h-left", { x: -40, opacity: 0, duration: 0.4, ease: "expo.out" }, start + 0.2);
tl.from("#ill-hammer .ill-h-equals", { scale: 0, opacity: 0, duration: 0.4, ease: "back.out(2)" }, start + 0.4);
tl.from("#ill-hammer .ill-h-right", { x: 40, opacity: 0, duration: 0.4, ease: "expo.out" }, start + 0.6);
tl.from("#ill-hammer .ill-h-foot", { y: 20, opacity: 0, duration: 0.4, ease: "power2.out" }, start + 0.8);
tl.to("#ill-hammer", { scale: 1.05, opacity: 0, duration: 0.45, ease: "power2.in" }, end - 0.45);

(see references/illustration_patterns.md for full canonical CSS)

6. cta — end-card with channel CTA

A branded outro for the final 3 seconds. Use 王建硕 as the channel name (per global instructions) — never put a guest's name in the CTA slot.

HTML:

<div id="cta" class="clip" data-start="{cta_start}" data-duration="3.24" data-track-index="1">
  <div class="cta-line-1">关注王建硕</div>
  <div class="arrow">↓</div>
  <div class="cta-line-2">微信公众号 · 视频号</div>
  <div class="cta-foot">聊 AI · 聊创业 · 持续更新</div>
</div>

CSS / GSAP: see references/illustration_patterns.md.

Legacy types (for one-off overlays on a single video)

The spec.json + scaffold.py workflow also supports these older overlay types — useful when you want to dress up ONE existing video without going through the full post-production workflow above:

• `quote` — full-width kinetic typography, top or bottom gradient.

Best for opening hooks and key-quote callouts.

• `slogan` — alias for quote with position: bottom and larger

type. Best for closing slogans.

• `callout` — small annotation panel in a corner. Best for chapter

labels, lower-thirds, "as seen in" notes.

• `custom` — escape hatch. Claude writes the overlay's HTML/CSS/GSAP

inside an overlays/<name>.html fragment file. See references/custom_overlay_recipes.md.

Workflow A — Post-segmentation preset (most common)

Use this when you're coming directly from /wjs-segmenting-video and want the standard cover + caption + chapter + illustrations + CTA treatment for each clip.

Step 1 — Generate AI covers at the right aspect

# For vertical 9:16 output (视频号 / 抖音):
python3 ~/.claude/skills/wjs-segmenting-video/scripts/make_cover.py \
    --segments segments.json --out output/ --size 1024x1792 --single 1
# Verify segment 1's cover; then batch:
python3 ~/.claude/skills/wjs-segmenting-video/scripts/make_cover.py \
    --segments segments.json --out output/ --size 1024x1792

Step 2 — For each clip, scaffold a HyperFrames project

hf_clip_NN/1080/ with:

• index.html — the composition (from template; see

references/post_segmentation_template.html)

• clip.mp4 — copied from output/clip_NN_slug.mp4
• cover.png — copied from output/cover_NN_slug.png
• captions.json — generated from output/clip_NN_slug.zh-CN.burn.srt

with every cue's start/end shifted by +cover_duration (so cues align with the composition timeline, not the body's own clock)

The build script at references/build_hf_clips.py does this for all segments in one pass. It reads segments.json + an ILLUSTRATIONS dict (illustrations per clip, see Step 3) + the template, and emits 5 ready-to-render projects.

Step 3 — Define illustrations per clip

For each clip, identify 1-2 hook moments and pick stack or hammer:

ILLUSTRATIONS = {
    1: [
        # The language hierarchy as a stack card during the opening
        {"key": "stack", "pattern": "stack", "body_start": 0.3, "body_end": 9.0,
         "label": "我们写的层级",
         "rows": [
             {"text": "自然语言", "accent": True},
             {"text": "Python",   "accent": False},
             {"text": "C",        "accent": False},
             {"text": "Assembly", "accent": False},
         ]},
        # The hammer at the most quotable moment
        {"key": "hammer", "pattern": "hammer", "body_start": 10.8, "body_end": 14.6,
         "left": "LLM", "equals": "=", "right": "新编译器",
         "foot": "自然语言 → Python → 汇编"},
    ],
    # ... clips 2-5
}

Timestamps are body-relative (after the cover-scene duration); the build script adds the cover offset when emitting GSAP positions.

Step 4 — Build + render

python3 references/build_hf_clips.py    # scaffolds all projects
for n in 01 02 03 04 05; do
  cd "hf_clip_$n/1080"
  npx hyperframes lint
  npx hyperframes validate
  npx hyperframes render
  cd ../..
done

A 2:30 clip renders in ~3 min. Output: hf_clip_NN/1080/renders/*.mp4.

Workflow B — Custom overlays on a single video (legacy spec.json)

Use this when you have ONE existing video and want to add a few ad-hoc overlays (title cards, annotations, lower-thirds).

spec.json schema

{
  "source_video": "../path/to/source.mp4",
  "duration": 135.4,
  "size": "1920x1080",
  "name": "clip_01_animated",
  "overlays": [
    {"id": "o1", "type": "quote", "start": 8.0, "duration": 6.0,
     "position": "top", "lines": ["代码不存在错误", "只存在意图错配"],
     "accent": [false, true]},
    {"id": "o2", "type": "callout", "start": 30.0, "duration": 5.0,
     "anchor": "top-right", "text": "FRP 概念"},
    {"id": "o3", "type": "slogan", "start": 122.0, "duration": 13.4,
     "lines": ["改 prompt", "不改 AI 生成的代码"], "accent": [false, true]}
  ]
}

Scaffold + render

python3 ~/.claude/skills/wjs-overlaying-video/scripts/scaffold.py spec.json
cd <name> && npm run check && npm run render

Output checklist

Before considering a clip done:

• [ ] Frame 0 is the cover (not black) — ffmpeg -ss 0 -vframes 1 out.mp4
• [ ] Captions are synced with audio (lint a few seconds with audio playback)
• [ ] All illustrations enter and exit at the speech moments they support
• [ ] CTA renders correctly (关注王建硕, not a guest's name)
• [ ] npx hyperframes lint && npx hyperframes validate both pass
• [ ] npx hyperframes inspect shows no layout overflow
• [ ] Total duration matches the source clip + cover + CTA durations

Common mistakes

• Cover aspect ≠ output aspect. 1024x1536 (the default

make_cover.py size) is 2:3 and gets letterboxed or cropped on 9:16 output. Always pass --size 1024x1792 for vertical.

• Caption alignment jumps with line count. Anchor by CENTER

(translate(-50%, -50%)) inside a fixed-height container so 1-line vs 2-line cues share the same visual midline. NOT anchored from bottom (causes growth-upward).

• GSAP overwrites CSS transform centering. If you set

transform: translate(-50%, -50%) in CSS and then tween y, GSAP replaces the transform and centering breaks. Use gsap.set(el, { xPercent: -50, yPercent: -50 }) instead so xPercent/yPercent compose with subsequent y/x tweens.

• Burning libass subs on top of HTML/CSS captions. Pick ONE caption

system per output video. If you're using this skill's HTML/CSS captions, do NOT also burn subs in /wjs-segmenting-video — request the raw clip via the hand-off package.

• Frame 0 is black. If your cover scene has an opacity fade-in

starting from 0, the literal first frame is black and the platform thumbnail will be black. Place the cover statically (no opacity tween) and verify with ffmpeg -ss 0 -vframes 1.

• Channel name in CTA = guest's name. Always use 王建硕. Guests

belong in description text inside the metadata, not in the on-screen CTA.

• **Cover image cropped because of object-fit: cover on mismatched

aspect.** Either regenerate the cover at the right aspect (see Step 1) or letterbox with object-fit: contain + dark background.

Integration with other skills

• `/wjs-segmenting-video` — the typical upstream. After it cuts

+ crops + slices SRTs, this skill picks up. The hand-off package is clip_NN.mp4 + clip_NN.zh-CN.burn.srt + segments.json.

• `/wjs-transcribing-audio` + `/wjs-translating-subtitles` — if no SRT exists, run them first. The

word-level Whisper or Volcano/豆包 ASR output is preferred for accurate cue timing.

• `hyperframes` — the underlying composition framework. This skill

is a thin wrapper that encodes the proven post-production patterns; everything in the hyperframes skill applies (preview, render, transitions, audio-reactive, etc.). Read it whenever you write custom overlays.

• `hyperframes-cli` — the CLI commands the project uses

(init, lint, validate, inspect, render).

• `gpt-image-2-skill` — the cover generator. make_cover.py

invokes it via the codex CLI; the codex auth in ~/.codex/auth.json is required.

• `/wjs-uploading-video` — the next downstream after this skill

produces an MP4. Uploads the renders to YouTube with title / description / tags from a metadata file.

Files & references

• scripts/scaffold.py — Workflow B scaffolder (legacy spec.json

for ad-hoc overlays)

• references/post_segmentation_template.html — Workflow A template:

the canonical cover + caption + chapter + illustration + CTA composition shape, with placeholder substitutions

• references/build_hf_clips.py — Workflow A multi-clip builder.

Reads segments.json + per-clip illustrations dict, scaffolds and populates one project per clip

• references/illustration_patterns.md — canonical CSS / GSAP for

the stack and hammer illustration patterns

• references/custom_overlay_recipes.md — reusable custom overlay

recipes (terminal demo, layer-stack diagram, callout with arrow)

• references/example_spec.json — Workflow B example