Auto Pop-Sci

Generate broadcast-quality science popularization videos from a topic prompt. Stack: Manim + edge-tts + ffmpeg.

Parameters

Ask the user (defaults in parentheses):

Param	Values	Default
topic	any subject	(required)
language	`zh-CN`, `en-US`, `ja-JP`, ...	`zh-CN`
duration	`5min`, `10min`, `15min`	`10min`
audience	`general`, `cs-student`, `advanced`	`cs-student`
voice	edge-tts voice name	`zh-CN-YunxiNeural`

Prerequisites Check

Before starting, verify the user has these installed:

uv --version          # Package manager
ffmpeg -version       # Audio/video processing
latex --version       # For Manim's MathTex (or xelatex)

Workflow

Phase 0 -- Scaffold the project

Important: Create the project directory OUTSIDE any existing uv workspace, otherwise uv init adds it as a workspace member instead of standalone.

mkdir <topic> && cd <topic>
uv init --name <topic>-animation --python ">=3.11"
uv add manim==0.20.1 edge-tts
mkdir animation
rm -f main.py   # uv init creates a default main.py -- delete it

Create animation/base.py by reading the scaffold file from this skill's directory. The file is at: <this_skill_directory>/scaffold/animation/base.py To find the skill directory, resolve the path of this SKILL.md file.

If you cannot find the scaffold file, copy animation/base.py from any working example (e.g. examples/euler-identity/animation/base.py).

NEVER regenerate base.py from scratch. It contains the sync-critical SubtitleMixin class. Always copy it verbatim.

Phase 1 -- Write the narrative plan (plan.md)

Structure using the 起承转合 framework:

Part 1: 起 (Hook)     ~15-20%  -- Real-life scenario, paradox, title reveal
Part 2: 承 (Build)    ~30-35%  -- Intuitive analogy, step-by-step concept building
Part 3: 转 (Deepen)   ~30-35%  -- Math/formalism, derivation, key insight
Part 4: 合 (Conclude) ~15-20%  -- Applications, return to opening, closing line

List Scene class names (e.g. S01_HookScene) with one-line descriptions.

Phase 2 -- Write the narration script (script.md)

Write the COMPLETE narration text (~250 chars/min for Chinese, ~150 words/min English).

Every paragraph = exactly one show_sub() call. This mapping is critical for sync.

### S01 SceneName (start_time - end_time)

Narration text here.

[ANIM: what happens on screen]

Next paragraph.

[ANIM: next animation]

Phase 2.5 -- Review Harness (MANDATORY)

Before implementing animations, review the plan and script against BOTH checklists below. This is the most impactful quality gate -- a boring or confusing script cannot be saved by good animations. If any item fails, revise plan.md and script.md before proceeding to Phase 3.

Pop-sci communication checklist

Score each dimension. For any score below "good", rewrite the offending segments before moving on.

Hook strength: Does the opening create genuine curiosity within 30s? Is there a question, paradox, or surprising fact -- not a textbook intro?
Analogy quality: Are analogies vivid, accurate, and grounded in everyday experience? Do they illuminate without oversimplifying?
Jargon management: Is every technical term explained before or upon first use? Could a simpler word work? Are there terms that never get used after being introduced?
Cognitive scaffolding: Does each concept build on the previous one? Is there a clear ladder from intuition to formalism, with no logical leaps?
Narrative tension: Is there an emotional arc? Does the script create "why?" questions and deliver satisfying "aha!" moments?
Information density: Is each segment digestible in one breath? More than one new concept per show_sub() call is a red flag -- split it.
Transition flow: Do segments connect with natural bridges ("But wait...", "So now the question becomes..."), not just topic jumps?
Closing resonance: Does the ending tie back to the opening and leave the viewer with a sense of wonder or a new mental model?

Animation clarity checklist

Concrete before abstract: Every formula must be preceded by a concrete numerical example. Never show a·s + e = b without first showing 2×3 + 5×1 + 1 = 12 with step-by-step highlighting.
One idea per segment: Each show_sub() should introduce exactly one concept. If you need two sentences to explain, split into two segments.
Visual anchors: Every abstract statement needs a visual counterpart. "Noise destroys solvability" → show the Gaussian elimination failing with actual numbers side-by-side.
Step-by-step derivation: For any math derivation, show each algebraic step on its own line, animating with Write() progressively. Use TransformMatchingTex for equation transitions (like 3Blue1Brown).
Spatial intuition: For geometric/algebraic topics, include at least one scene with 3D-like visualization (see "3D Visualization" section below).

If any item fails, revise the script and plan before proceeding to Phase 3.

Phase 3 -- Implement Manim scenes (animation/main.py)

from base import SubtitleMixin, BG, ACCENT, OK_CLR, FAIL_CLR, MUTED, CARD_BG, FONT

class S01_HookScene(SubtitleMixin, Scene):
    def construct(self):
        self.camera.background_color = BG

        self.show_sub("First narration sentence.")  # NON-BLOCKING
        self.play(Write(title))                      # plays DURING narration
        self.play(FadeIn(diagram))
        self.pad_segment()                           # fill remaining TTS time

        self.show_sub("Second narration sentence.")
        self.play(Transform(a, b))
        self.pad_segment()

        self.clear_all()

Key pattern: show_sub → animations → pad_segment

show_sub() is NON-BLOCKING. It shows the subtitle, then returns immediately. Animations after it play WHILE the narration audio plays.
pad_segment() waits for any remaining TTS time after animations finish. If animations took longer than the narration, it does nothing.
clear_all() auto-calls pad_segment() and saves timestamps.
For the LAST scene: if it doesn't use clear_all(), call self._save_stamps()

Phase 4 -- Implement the build pipeline (animation/build_video.py)

Read the template at <this_skill_directory>/scaffold/animation/build_video.py.tmpl and fill in these values:

VOICE: the edge-tts voice name (e.g. "zh-CN-YunxiNeural")
SCENE_ORDER: list of scene class names from main.py
SCENE_SEGMENTS: dict mapping each scene to its TTS text list
SRT_DISPLAY: dict mapping each scene to its subtitle display text

CRITICAL RULE: len(SCENE_SEGMENTS[scene]) MUST equal the number of show_sub() calls in that scene class. The template includes verify_segments() which checks this automatically before building.

Phase 5 -- Build and verify

uv run python animation/build_video.py
open media/final/*_full.mp4

The pipeline runs 5 phases automatically:

Phase 1: Generate TTS per segment → timing.json
Phase 2: Render Manim scenes (silent video + timestamps.json)
Phase 3: Concat silent videos into one full video (video-only, -an)
Phase 4: ONE-PASS audio merge → place ALL segments at absolute timestamps
         on the full timeline using ffmpeg adelay + amix
Phase 5: Generate SRT from timestamps

Why one-pass merge matters: The old approach (per-scene audio merge then concat) caused AAC frame discontinuities at scene boundaries, producing audible pops and silence gaps. The new approach creates a single continuous audio track in one ffmpeg call -- zero boundary artifacts.

Build pipeline rules

Render all scenes in ONE manim process -- _STAMPS dict accumulates across scenes; per-scene rendering breaks timestamp continuity.
Add TTS retry logic -- edge-tts can hit transient network errors; retry up to 3 times with backoff.
Set PYTHONUNBUFFERED=1 -- so build progress is visible in real-time.
Add timeout=600 to subprocess calls -- prevents hung renders from blocking the pipeline forever.
Never use capture_output=True alone -- always use text=True and log stderr on failure for debugging.

Audio-Video Sync Rules (MANDATORY)

NEVER use Manim's add_sound() -- it drops audio clips silently
TTS per segment -- one .mp3 per show_sub() call, not per scene
Timestamp logging -- SubtitleMixin._log_timestamp() records Manim render time
One-pass audio merge -- concat silent videos first, then place ALL audio at absolute timestamps in a single ffmpeg call. NEVER merge per-scene then concat.
Segment count must match -- mismatches = desync. verify_segments() checks this

Style Guide (3Blue1Brown)

Subtitles

Subtitles are off by default (SHOW_SUBTITLES = False in base.py). The SRT file is always generated for external subtitle use. To enable on-screen subtitle bars, set SHOW_SUBTITLES = True in base.py.

Colors

Background: #1a1a2e, Card bg: #16213e, Subtitle bg: #0d0d1a
Accent: #f1c40f (gold), Success: #2ecc71, Fail: #e74c3c
Primary: #3498db (blue), Secondary: #e67e22 (orange), Muted: #7f8c8d
Assign each key concept its own color and use consistently across scenes

Math formulas

Use MathTex with separate strings per sub-expression for color targeting: MathTex("e", "^{", "i", r"\pi", "}", font_size=72) then eq[0].set_color(...)
Use TransformMatchingTex for derivation steps (animates matching parts)
Frame key results with SurroundingRectangle(corner_radius=0.12)

Layout patterns

Card grid: RoundedRectangle with field tag + symbol + description + separator line
Two-tier: constants/icons on top row, operations/arrows below, result at bottom
Section titles: smaller muted text at top, FadeIn(shift=DOWN*0.15) entrance
Decorative: soft glow circles behind hero formulas (low opacity fill + stroke)

Animation patterns

Staggered entrance: FadeIn(item, shift=UP*0.4) with lag_ratio or loop
Emphasis: scale=0.7 on FadeIn for a "pop" effect
Step labels: show before derivation, fade out after
Unit circle: TracedPath + always_redraw for cos/sin projection lines
Final reveal: double frame (inner solid + outer faint)

Step-by-step math derivations (3Blue1Brown style)

The single most important quality signal. Every derivation must be animated progressively, never shown as a complete block.

# GOOD: step-by-step with progressive reveal
step1 = MathTex(r"2x + 5y", "=", "11", font_size=30)
self.play(Write(step1, run_time=0.5))

step2 = MathTex(r"y = 13 - 4x", font_size=26)
step2.next_to(step1, DOWN, aligned_edge=LEFT, buff=0.3)
self.play(Write(step2, run_time=0.4))

step3 = MathTex(r"x = 3", font_size=30, color=OK_CLR)
step3.next_to(step2, DOWN, buff=0.3)
box = SurroundingRectangle(step3, corner_radius=0.08, color=OK_CLR)
self.play(Write(step3), Create(box))

# BAD: showing complete derivation at once
full = MathTex(r"2x+5y=11 \Rightarrow y=13-4x \Rightarrow x=3")
self.play(Write(full))  # student has no time to follow!

For equation transformations, use TransformMatchingTex:

eq1 = MathTex(r"e^{i\pi}", "=", r"\cos\pi", "+", r"i\sin\pi")
eq2 = MathTex(r"e^{i\pi}", "=", "(-1)", "+", r"i \cdot 0")
self.play(TransformMatchingTex(eq1, eq2), run_time=1)

Concrete before abstract

For any abstract formula, always precede it with a concrete worked example: show specific numbers first, animate each operation, then generalize.

# FIRST: concrete example with specific numbers
self.show_sub("Let's see how cosine similarity works")
comp = MathTex("3", r"\times", "4", "+", "1", r"\times", "2", "=", "14")
self.play(Write(comp))  # animate step by step

# THEN: general formula
self.show_sub("In general, we compute the dot product")
formula = MathTex(r"\vec{a} \cdot \vec{b} = \sum a_i b_i")

3D visualization (spatial topics only)

NEVER use ThreeDScene with Dot3D/Sphere -- Cairo renders each sphere as a parametric surface; 50+ spheres can OOM or hang for minutes.

Instead, use oblique projection with regular Dot in a normal Scene. See examples/lattice-crypto/ S04_Lattice3DScene for a working _proj() implementation with camera rotation.

Things to AVOID

letter_spacing parameter in Text() -- Manim does not support it
Raw \text{} in MathTex for Chinese -- use separate Text() objects
Overly long run_time on Write() -- keep under 2.5s for readability
Plain text-only scenes -- always add at least one visual element
ThreeDScene + Dot3D/Sphere for many points -- use _proj() instead
Showing formulas without a preceding concrete numerical example
Dumping a complete derivation on screen at once without progressive reveal

Reference

Working example: examples/zero-knowledge-proof/
Sync architecture: docs/sync-architecture.md
Contributing new topics: docs/contributing.md

auto-pop-sci