| name | wjs-overlaying-video |
| description | Use when the user has one or more video clips and wants to add post-production on top — AI-generated cover as first frame, HTML/CSS captions synced to SRT, kinetic illustration overlays at hook moments, chapter chips, end-card CTA, or any other timed motion graphics. Most often used as the downstream of `/wjs-segmenting-video` — pick up where that skill stopped (raw cropped clip + per-clip SRT) and produce the upload-ready MP4. Backed by HyperFrames so everything compiles to ONE final encode — no cascade of re-encodes. Triggers — "加封面", "加字幕", "加动画", "加 CTA", "做后期", "post-production", "title card", "kinetic captions", "end card". |
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
| Is | Is not |
|---|
| Everything that goes ON TOP of a video clip: cover, caption, chapter, illustration, CTA | Cutting / cropping a video (that's /wjs-segmenting-video + /wjs-reframing-video) |
| One HyperFrames composition per clip = ONE final encode | A multi-step decode/encode cascade |
cover is the literal first frame of the output (platforms auto-pick it as thumbnail) | A separate thumbnail file the user uploads alongside |
Captions are HTML/CSS — -webkit-text-stroke for white-on-anything readability | libass burn-in (deprecated) |
Illustrations: re-usable stack / hammer patterns + custom escape hatch | One bespoke HTML/CSS per illustration without re-use |
| AI covers regenerated at native target aspect (1024×1792 for vertical, 1536×1024 for horizontal) | Single 1024×1536 default that letterboxes or crops on the platform |
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:
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")
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):
make_cover.py --segments S.json --out output/ --size 1024x1792 [--single N]
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 {
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|％|%))")
JS (one bubble per cue + GSAP fade in/out, all centered at container midpoint):
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;
b.style.opacity = "0";
captionEl.appendChild(b);
return b;
});
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
python3 ~/.claude/skills/wjs-segmenting-video/scripts/make_cover.py \
--segments segments.json --out output/ --size 1024x1792 --single 1
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.mp4cover.png — copied from output/cover_NN_slug.pngcaptions.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: [
{"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},
]},
{"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"},
],
}
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
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]}
]
}
| Field | Required | Notes |
|---|
source_video | Yes | Path to source MP4. Symlinked into the project as source.mp4. |
duration | Yes | Total composition length in seconds — match the source video. |
size | No | WIDTHxHEIGHT (default 1920x1080). |
overlays[].type | Yes | quote, slogan, callout, or custom. |
overlays[].start | Yes | Start time in seconds. |
overlays[].duration | Yes | How long the overlay is on screen. |
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:
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
- 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 substitutionsreferences/build_hf_clips.py — Workflow A multi-clip builder.
Reads segments.json + per-clip illustrations dict, scaffolds and
populates one project per clipreferences/illustration_patterns.md — canonical CSS / GSAP for
the stack and hammer illustration patternsreferences/custom_overlay_recipes.md — reusable custom overlay
recipes (terminal demo, layer-stack diagram, callout with arrow)references/example_spec.json — Workflow B example
