mcpField
C
concept-to-video

concept-to-video

github.com/mathews-tomCreative & media337135 installsUpdated 2d ago
Repository

About concept-to-video

concept-to-video is a skill for turning concepts into animated explainers with Manim. It suits creators who want an editable Python scene as an intermediate, with preview-and-refine rendering before export.

Key features

  1. 01Create animations with Manim
  2. 02Templates for flows, comparisons, and timelines
  3. 03Preview at low quality and iterate
  4. 04Export MP4, GIF, or WebM
  5. 05Add audio and subtitles

Use cases

  1. 01Animate algorithm walkthroughs
  2. 02Explain architectures or data flows
  3. 03Make comparisons and timelines
  4. 04Add narration and subtitles to videos

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/mathews-tom/armory --skill concept-to-video

After installing, type/concept-to-videoin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

config.toml
pyrightconfig.json
evals/
cases.yaml
references/
storyboard-schema.md
references/code2video/
coder.md
critic.md
LICENSE
planner.md
references/rules/
agent-interaction.md
algorithm-stepthrough.md
architecture-layers.md
audio-overlay.md
comparison.md
images.md
layout.md
math-concept.md
multi-scene.md
pipeline-flow.md
subtitles.md
text-animation.md
training-loop.md
transitions.md
voiceover-scaffold.md
references/templates/
comparison_template.py
data_flow_template.py
timeline_template.py
scripts/
_config.py
_fixup_client.py
add_audio.py
critic_pass.py

Details

From the project README

Concept to Video

Creates animated explainer videos from concepts using Manim (Python) as a programmatic animation engine.

Reference Files

FilePurpose
references/rules/pipeline-flow.mdRAG, ETL, CI/CD — sequential stage animations with arrows
references/rules/architecture-layers.mdSystem stacks, network layers, abstraction hierarchies
references/rules/algorithm-stepthrough.mdSorting, search, graph traversal — stateful step-by-step animations
references/rules/comparison.mdSide-by-side A vs B, before/after, trade-off visualizations
references/rules/agent-interaction.mdMulti-agent message passing, distributed systems, pub/sub
references/rules/math-concept.mdEquations, formulas, geometric proofs — LaTeX-free by default
references/rules/training-loop.mdGradient descent, RL loops, cyclic iterative processes
references/rules/transitions.mdFade and wipe transitions between scene sections
references/rules/text-animation.mdText replacement, progressive bullet reveal, callouts, emphasis
references/rules/layout.mdCanvas coordinates, VGroup arrangement, spacing guidelines
references/rules/audio-overlay.mdffmpeg audio overlay — background music, voiceover, multi-track mixing
references/rules/voiceover-scaffold.mdTiming script generation, TTS handoff, narration best practices
references/rules/images.mdImageMobject usage, logo/screenshot patterns, scaling and positioning
references/rules/subtitles.mdSRT generation from scene timing, ffmpeg subtitle burning
references/rules/multi-scene.mdMultiple Scene classes, ffmpeg concat, chapter-based composition
references/templates/data_flow_template.pyParametric pipeline/data flow animation (config-driven STAGES list)
references/templates/comparison_template.pyParametric side-by-side comparison (config-driven LEFT/RIGHT items)
references/templates/timeline_template.pyParametric timeline animation (config-driven EVENTS list)
scripts/render_video.pyWrapper around Manim CLI — handles quality, format, output path cleanup
scripts/add_audio.pyffmpeg wrapper — audio overlay, volume, fade-in/out, trim-to-video

Why Manim as the engine

Manim is the "SVG of video" — you write Python code that describes animations declaratively, and it renders to MP4/GIF at any resolution. The Python scene file IS the editable intermediate: the user can see the code, request changes ("make the arrows red", "add a third step", "slow down the transition"), and only do a final high-quality render once satisfied. This makes the workflow iterative and controllable, exactly like concept-to-image uses HTML as an intermediate.

Workflow

Concept → Manim scene (.py) → Preview (low-quality) → Iterate → Final render (MP4/GIF)
  1. Interpret the user's concept — determine the best animation approach
  2. Design a self-contained Manim scene file — one file, one Scene class
  3. Preview by rendering at low quality (-ql) for fast iteration
  4. Iterate on the scene based on user feedback
  5. Export final video at high quality using scripts/render_video.py

Step 0: Ensure dependencies

Before writing any scene, ensure Manim is installed:

# System deps (usually pre-installed)
apt-get install -y libpango1.0-dev libcairo2-dev ffmpeg 2>/dev/null

# Python package
pip install manim --break-system-packages -q

Verify with: python3 -c "import manim; print(manim.__version__)"

Step 1: Interpret the concept

Determine the best animation pattern, then read the matching rule file before writing any code.

User intentRule file to readKey Manim primitives
Explain a pipeline/flowreferences/rules/pipeline-flow.mdArrow, Rectangle, Text, AnimationGroup
Show architecture layersreferences/rules/architecture-layers.mdVGroup, Arrange, FadeIn with shift
Algorithm step-throughreferences/rules/algorithm-stepthrough.mdTransform, ReplacementTransform, Indicate
Compare approachesreferences/rules/comparison.mdSplit screen VGroups, simultaneous animations
Mathematical conceptreferences/rules/math-concept.mdMathTex, geometric shapes, Rotate, Scale
Agent/multi-system interactionreferences/rules/agent-interaction.mdArrows between entities, Create/FadeOut
Training/optimization loopreferences/rules/training-loop.mdLoop with Transform, ValueTracker, plots
Timeline/historyreferences/templates/timeline_template.pyNumberLine, sequential Indicate
Embed images or screenshotsreferences/rules/images.mdImageMobject, SVGMobject
Add subtitles or captionsreferences/rules/subtitles.mdSRT generation, ffmpeg subtitle burn
Multiple distinct chaptersreferences/rules/multi-scene.mdMultiple Scene classes, ffmpeg concat
Add audio or voiceoverreferences/rules/audio-overlay.mdffmpeg, scripts/add_audio.py
Transition between sectionsreferences/rules/transitions.mdFadeOut all, shift off-screen
Text reveal, callouts, emphasisreferences/rules/text-animation.mdReplacementTransform, LaggedStart, Indicate
Positioning, spacing, layoutreferences/rules/layout.mdnext_to, arrange, to_edge, move_to

Step 2: Design the Manim scene

Template-first vs from-scratch

Check whether a parametric template covers the concept before writing a scene from scratch:

If the concept is...Start with template
A linear pipeline (A→B→C→D)references/templates/data_flow_template.py — edit STAGES
A two-option comparisonreferences/templates/comparison_template.py — edit LEFT_ITEMS, RIGHT_ITEMS
A chronological timelinereferences/templates/timeline_template.py — edit EVENTS
Anything elseWrite from scratch using the relevant rule file

When using a template: copy it to the working directory, edit the config constants at the top, do not restructure the class.

Core rules:

  • Single file, single Scene class: Everything in one .py file with one class XxxScene(Scene).
  • Self-contained: No external assets unless absolutely necessary. Use Manim primitives for everything.
  • Readable code: The scene file IS the user's artifact. Use clear variable names, comments for each animation beat.
  • Color with intention: Use Manim's color constants (BLUE, RED, GREEN, YELLOW, etc.) or hex colors. Max 4-5 colors. Every color should encode meaning.
  • Pacing: Include self.wait() calls between logical sections. 0.5s for breathing room, 1-2s for major transitions.
  • Text legibility: Use font_size=36 minimum for body text, font_size=48+ for titles. Test at target resolution.
  • Scene dimensions: Default Manim canvas is 14.2 × 8 units (16:9). Keep content within ±6 horizontal, ±3.5 vertical.
Animation best practices
# DO: Use animation groups for simultaneous effects
self.play(FadeIn(box), Write(label), run_time=1)

# DO: Use .animate syntax for property changes
self.play(box.animate.shift(RIGHT * 2).set_color(GREEN))

# DO: Stagger related elements
self.play(LaggedStart(*[FadeIn(item) for item in items], lag_ratio=0.2))

# DON'T: Add/remove without animation (jarring)
self.add(box)  # Only for setup before first frame

# DON'T: Make animations too fast
self.play(Transform(a, b), run_time=0.3)  # Too fast to read
Structure template
from manim import *

class ConceptScene(Scene):
    def construct(self):
        # === Section 1: Title / Setup ===
        title = Text("Concept Name", font_size=56, weight=BOLD)
        self.play(Write(title))
        self.wait(1)
        self.play(FadeOut(title))

        # === Section 2: Core animation ===
        # ... main content here ...

        # === Section 3: Summary / Conclusion ===
        # ... wrap-up animation ...
        self.wait(2)

Step 3: Preview render

Use low quality for fast iteration:

python3 scripts/render_video.py scene.py ConceptScene --quality low --format mp4

This renders at 480p/15fps — fast enough for previewing timing and layout. Present the video to the user.

Step 4: Iterate

Common refinement requests and how to handle them:

RequestAction
"Slower/faster"Adjust run_time= params and self.wait() durations
"Change colors"Update color constants
"Add a step"Insert new animation block between sections
"Reorder"Move code blocks around
"Different layout"Adjust .shift(), .next_to(), .arrange() calls
"Add labels/annotations"Add Text or MathTex objects with .next_to()
"Make it loop"Add matching intro/outro states

Step 5: Final export

Once the user is satisfied:

python3 scripts/render_video.py scene.py ConceptScene --quality high --format mp4
Quality presets
PresetResolutionFPSFlagUse case
low480p15-qlFast preview
medium720p30-qmDraft review
high1080p60-qhFinal delivery
4k2160p60-qkPresentation quality
Format options
FormatFlagUse case
mp4--format mp4Standard video delivery
gif--format gifEmbeddable in docs, social
webm--format webmWeb-optimized
Delivering the output

Present both:

  1. The .py scene file (for future editing)
  2. The rendered video file (final output)

Copy the final video to /mnt/user-data/outputs/ and present it.

Step 5.5: Optional audio overlay

If the user provides audio (music or voiceover), or requests it:

# Background music at 25% volume with fade-in/out
python3 scripts/add_audio.py final.mp4 music.mp3 \
    --output final_with_audio.mp4 \
    --volume 0.25 --fade-in 2 --fade-out 3 --trim-to-video

# Voiceover at full volume, trimmed to video length
python3 scripts/add_audio.py final.mp4 voiceover.mp3 \
    --output final_narrated.mp4 --trim-to-video

For voiceover scripting before recording, read references/rules/voiceover-scaffold.md. For subtitles/captions, read references/rules/subtitles.md. For advanced multi-track mixing, read references/rules/audio-overlay.md.

Error Handling

ErrorCauseResolution
ModuleNotFoundError: manimManim not installedRun Step 0 setup commands
pangocairo build errorMissing system dev headersapt-get install -y libpango1.0-dev
FileNotFoundError: ffmpegffmpeg not installedapt-get install -y ffmpeg
Scene class not foundClass name mismatchVerify class name matches CLI argument
Overlapping objectsPositions not calculatedUse .next_to(), .arrange(), explicit .shift() calls
Text cut offText too large or positioned near edgeReduce font_size or adjust position within ±6,±3.5
Slow renderToo many objects or complex transformationsReduce object count, simplify paths, use lower quality
LaTeX ErrorLaTeX not installed (for MathTex)Use Text instead, or install texlive-latex-base
LaTeX fallback

If LaTeX is not available, avoid MathTex and Tex. Use Text with Unicode math symbols instead:

# Instead of: MathTex(r"\frac{1}{n} \sum_{i=1}^{n} x_i")
# Use:        Text("(1/n) Σ xᵢ", font_size=36)

Agentic Mode (Opt-In)

Single-shot mode (default) is fast and cheap — the coder writes scene.py directly from a concept. Use agentic mode for production-quality renders where layout correctness and asset resolution matter enough to justify additional LLM and VLM calls.

Pipeline
concept
  └─► plan_storyboard.py ──► storyboard.json
            │
            ▼
      fetch_assets.py (optional)
            │
            ▼
      coder writes scene.py
            │
            ▼
      render_video.py --max-fix-attempts N
            │  ▲
            │  └─ LLM fixup loop (on failure, up to N retries)
            ▼
      critic_pass.py --critic
            │  ▲
            │  └─ VLM layout patch (1 call with M image blocks)
            ▼
       final MP4
Flag Reference
ScriptFlagDefaultHard capEffectCost impact
render_video.py--max-fix-attempts03LLM-assisted auto-fix on render failure; 0 = disabled+1 LLM call per retry
critic_pass.py--criticdisabled—Enable the VLM critic pass; noop without this flag+1 VLM call (N image blocks)
critic_pass.py--critic-budget50000—Token budget for critic call; aborts loudly if exceededSets ceiling; use to prevent runaway spend
critic_pass.py--frames510Frames sampled from the rendered video for the criticMore frames → higher token cost per critic run
fetch_assets.py--adapternone—Asset backend: local, iconfinder, noneiconfinder adds external API calls
fetch_assets.py--asset-dir——Root directory for --adapter=local; required with localNone
Cost Tradeoffs

The fixup loop adds one LLM call per failed render attempt — with --max-fix-attempts 3 you may pay up to 3 extra calls before the loop exhausts or succeeds. The critic pass adds one VLM call containing N PNG image blocks (default 5, max 10); each frame adds roughly 1 token per 800 bytes of base64-encoded PNG, so complex scenes at high resolution are materially more expensive. Setting --critic-budget to a conservative token ceiling (e.g. 20000) causes BudgetExceededError before the API call is made, so you never pay for an accidentally oversized request — the error is loud and non-recoverable by design.

Invocation Example
# 1. Plan
python3 scripts/plan_storyboard.py "explain transformer self-attention" \
    --output storyboard.json

# 2. (Optional) Fetch assets
python3 scripts/fetch_assets.py storyboard.json \
    --adapter local --asset-dir ./assets --output resolved.json

# 3. Coder writes scene.py (Claude writes this from storyboard.json)

# 4. Render with auto-fix
python3 scripts/render_video.py scene.py AttentionScene \
    --quality high --format mp4 --max-fix-attempts 3 \
    --output final.mp4

# 5. Critic pass
python3 scripts/critic_pass.py scene.py final.mp4 \
    --critic --critic-budget 40000 --frames 5

Agentic pipeline design (storyboard planner, auto-fix loop, VLM critic) is adapted from Code2Video (arXiv 2510.01174, MIT). Vendored prompt templates live in references/code2video/ alongside the upstream LICENSE. Full vendoring record, pinned commit, and re-sync policy are tracked in root ATTRIBUTIONS.md.

Limitations

  • Manim + ffmpeg required — cannot render without these dependencies.
  • Audio is post-render only — Manim renders silent MP4s. Use scripts/add_audio.py to overlay audio after export.
  • LaTeX optional — MathTex requires a LaTeX installation. Fall back to Text with Unicode for math.
  • Render time scales with complexity — a 30-second 1080p scene with many objects can take 1-2 minutes to render.
  • 3D scenes require OpenGL — ThreeDScene may not work in headless containers. Stick to 2D Scene class.
  • No interactivity — output is a static video file, not an interactive widget.
  • GIF output is silent — audio overlay only works with MP4/WEBM output formats.

Design anti-patterns to avoid

  • Walls of text on screen — keep to 3-5 words per label, max 2 lines
  • Everything appearing at once — use staged animations with LaggedStart
  • Uniform timing — vary run_time to create rhythm (fast for simple, slow for important)
  • No visual hierarchy — use size, color, and position to guide attention
  • Rainbow colors — 3-4 intentional colors max
  • Ignoring the grid — align objects to consistent positions using arrange/align

Comments

Markdown supported