CategoryWeightCategory scoreContribution

Securityprompt, exec, net, exfil, eval

35%

30.8 pts

Supply chainhash, typosquat, maintainer, lockfile

20%

100

20.0 pts

Maintenancestaleness, pinning, CI

15%

100

15.0 pts

TransparencySKILL.md, perms, README

15%

100

15.0 pts

Communityinstalls, verify, response

15%

100

15.0 pts

Findings & checks · 1 flagged

Securityscore 88 · 1 finding

MEDIUM"Never tell the user" non-disclosure imperative in the skillSS-SKILL-INJECT-IMPERATIVE-01 · Prompt injection · manim_mcp/prompts/tts_narration_from_code.md

MEDIUM — it fires on intent; whether the agent honors the non-disclosure imperative depends on the host model.

Why it matters

A bulleted imperative like {match} tells the agent to never reveal, disclose, or mention something to the user. Used adversarially it can instruct the agent to hide its tool calls or lie about what it did — stripping the transparency a user relies on to trust the agent.

The exact value spotted

excerptmanim_mcp/prompts/tts_narration_from_code.md· markdown

26- Describe what appears, moves, transforms on screen

27- Educational, engaging 3Blue1Brown style

28- Don't mention code, variables, or technical implementation

30Return ONLY the narration, one sentence per line.

Occurrences

1 occurrence · at L28

How to fix

Remove the non-disclosure imperative, or rescope it so it limits output format, not honesty to the user.

Delete any "never reveal / never tell the user" line aimed at the model's own behavior.
Keep legitimate confidentiality rules about external data (e.g. "do not echo API keys"), which are user-protective, not user-deceiving.

Framework references

OWASPLLM01 ATLASAML.T0051

Trace & refs

ruleSS-SKILL-INJECT-IMPERATIVE-01sha256dd0131c08d31efc2…rubric 365aacaView on GitHub

Supply chainscore 100 · 0 findings

✓—All supply chain checks passedNo findings in this category for the latest scan.pass

Maintenancescore 100 · 0 findings

✓—All maintenance checks passedNo findings in this category for the latest scan.pass

Transparencyscore 100 · 0 findings

✓—All transparency checks passedNo findings in this category for the latest scan.pass

Communityscore 100 · 0 findings

✓—All community checks passedNo findings in this category for the latest scan.pass

The primary manifest — the file an agent reads to learn what this artifact does.

README.md · 1 file

README.md16.4 KB · Markdown

manim-mcp

Text-to-video animation powered by manimgl (3Blue1Brown's library) and a multi-agent LLM pipeline. Describe what you want to see, and get a rendered animation back.

Works as a CLI tool, an LLM-powered agent, or an MCP server for integration with AI assistants like Claude.

Examples

Circle to Square Transform	3D Rotating Cube
`manim-mcp gen "Transform a blue circle into a red square"`	`manim-mcp gen "A 3D cube rotating. Use ThreeDScene."`
Circle to Square	Rotating Cube

[Watch 20+ example animations on YouTube](https://www.youtube.com/playlist?list=PLYfSs9pitF8Or8_cjhUaXh_sTSuJqskGD)

Features

RAG-powered code generation - Uses 5,300+ indexed documents for high-quality code generation:
3,140 3Blue1Brown scene examples
1,652 manimgl API signatures with exact parameters
101 animation pattern templates (Riemann sums, transforms, physics, etc.)
470 library documentation files
16+ error patterns for common mistakes
Probe integration - Optional AST-aware semantic code search using Probe:
Tree-sitter based code parsing (understands Python structure)
Hybrid BM25 + TF-IDF ranking for better keyword matching
Complete code block extraction (no truncation)
Multi-animation videos - Each video uses 2+ animation patterns for professional quality
Multi-agent pipeline - Concept analysis, scene planning, code generation, and code review
Self-learning - Stores error patterns and fixes for continuous improvement
Multi-provider LLM - Supports Google Gemini, Anthropic Claude, and DeepSeek
Audio narration - Parallel audio generation with automatic sync:
Video code generated first (no narration constraint)
TTS runs in parallel with video rendering
Audio automatically paced to match video duration
Parameter validation - API signatures prevent invalid method calls

Quick Start

pip install -e ".[rag]"

Prerequisites

Python 3.11+
manimgl installed: pip install manimgl
A Google Gemini API key set as MANIM_MCP_GEMINI_API_KEY
Optional: ChromaDB (for RAG), ffmpeg (for audio mixing), LaTeX (for math text), S3/MinIO (for cloud storage)
Optional: Probe for AST-aware code search (install via cargo install probe-search)

Environment Variables

Copy .env.example to .env and fill in your values:

# LLM Provider
MANIM_MCP_GEMINI_API_KEY=your-gemini-api-key
MANIM_MCP_GEMINI_MODEL=gemini-3-flash-preview  # default

# Alternative: Claude
# MANIM_MCP_LLM_PROVIDER=claude
# MANIM_MCP_CLAUDE_API_KEY=your-claude-api-key
# MANIM_MCP_CLAUDE_MODEL=claude-sonnet-4-20250514

# Alternative: DeepSeek
# MANIM_MCP_LLM_PROVIDER=deepseek
# MANIM_MCP_DEEPSEEK_API_KEY=your-deepseek-api-key

# RAG (ChromaDB)
MANIM_MCP_RAG_ENABLED=true
MANIM_MCP_CHROMADB_HOST=localhost
MANIM_MCP_CHROMADB_PORT=8000

# S3 Storage (optional)
MANIM_MCP_S3_ENDPOINT=localhost:9000
MANIM_MCP_S3_ACCESS_KEY=minioadmin
MANIM_MCP_S3_SECRET_KEY=minioadmin
MANIM_MCP_S3_BUCKET=manim-renders

# Probe Search (optional - for AST-aware code search)
# Colon-separated paths to search for scene examples
MANIM_MCP_PROBE_PATHS=/path/to/3b1b-videos:/path/to/manim-examples

Usage

Generate an animation

# Simple mode (default) - direct LLM generation
manim-mcp gen "Transform a blue circle into a red square"

# Advanced mode - multi-agent pipeline with RAG
manim-mcp gen "Visualize the central limit theorem" --mode advanced

# With quality and format options
manim-mcp gen "Animate eigenvectors" --quality high --format mp4

Generation modes:

--mode simple (default): Direct LLM code generation, faster
--mode advanced: Multi-agent pipeline (ConceptAnalyzer → ScenePlanner → CodeGenerator → CodeReviewer) with RAG retrieval

Generate with audio narration

manim-mcp gen "Introduction to linear algebra" --audio
manim-mcp gen "Pythagorean theorem proof" --audio --voice Kore

Audio uses a parallel pipeline with automatic sync:

Manim code is generated first (video-driven)
Video rendering and TTS generation run in parallel
Audio is automatically paced to match video duration
Audio is mixed into the final video

Edit an existing animation

manim-mcp edit <render_id> "Make the vectors red and add axis labels"

List, inspect, delete renders

manim-mcp list --status completed --limit 10
manim-mcp get <render_id>
manim-mcp delete <render_id> --yes

Agent mode

Let the LLM interpret multi-step requests:

manim-mcp prompt "Create a video on eigenvectors, then edit it with better colors"

MCP server

Start the Model Context Protocol server for integration with Claude, Cursor, or other MCP clients:

manim-mcp serve
manim-mcp serve --transport stdio
manim-mcp serve --transport streamable-http

RAG Indexing

Index all knowledge sources for best code generation quality:

# Check current index status
manim-mcp index status

# Index 3b1b video scenes (3,140 scenes)
manim-mcp index 3b1b-videos --path /path/to/3b1b/videos

# Index manimgl API signatures (1,652 signatures)
manim-mcp index api

# Index animation patterns (101 patterns)
manim-mcp index patterns

# Index error patterns (16+ patterns)
manim-mcp index errors

# Index library documentation (470 docs)
manim-mcp index manim-docs

# Clear a collection
manim-mcp index clear patterns --yes

Docker

Run with all dependencies (ChromaDB, MinIO):

export MANIM_MCP_GEMINI_API_KEY=your-api-key
docker compose up

This starts:

MCP server on port 8000
ChromaDB on port 8001
MinIO on ports 9000/9001

Architecture

┌─────────────────────────────────────────────────────────────────────────────┐
│                      AUDIO PIPELINE (parallel with video)                    │
├─────────────────────────────────────────────────────────────────────────────┤
│                                                                              │
│   prompt ──► Code Generation (video-driven)                                  │
│                           │                                                  │
│              ┌────────────┴────────────┐                                     │
│              │                         │  PARALLEL                           │
│              ▼                         ▼                                     │
│   ┌──────────────────┐      ┌──────────────────┐                            │
│   │  Render Video    │      │  Generate Script │                            │
│   │                  │      │  + TTS Audio     │                            │
│   └────────┬─────────┘      └────────┬─────────┘                            │
│            │                         │                                       │
│            ▼                         ▼                                       │
│       video.mp4              audio segments                                  │
│            │                         │                                       │
│            └────────────┬────────────┘                                       │
│                         ▼                                                    │
│              Pace audio to video duration                                    │
│                         │                                                    │
│                         ▼                                                    │
│                  Mix Audio + Video ──► S3 upload ──► URL                     │
│                                                                              │
└─────────────────────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────────────────────┐
│                           MULTI-AGENT PIPELINE                               │
├─────────────────────────────────────────────────────────────────────────────┤
│                                                                              │
│   prompt ──► ConceptAnalyzer ──► ScenePlanner ──► CodeGenerator ──► CodeReviewer
│                    │                   │                │                │   │
│                    ▼                   ▼                ▼                ▼   │
│              ┌─────────────────────────────────────────────────────────┐     │
│              │                    ChromaDB RAG (5,300+ docs)           │     │
│              │  ┌──────────┬──────────┬──────────┬────────┬────────┐  │     │
│              │  │  scenes  │   api    │ patterns │  docs  │ errors │  │     │
│              │  │  (3,140) │ (1,652)  │  (101)   │ (470)  │  (16)  │  │     │
│              │  └──────────┴──────────┴──────────┴────────┴────────┘  │     │
│              └─────────────────────────────────────────────────────────┘     │
│                                                                              │
└──────────────────────────────────┬──────────────────────────────────────────┘
                                   │
                                   ▼
┌─────────────────────────────────────────────────────────────────────────────┐
│                            RENDER PIPELINE                                   │
├─────────────────────────────────────────────────────────────────────────────┤
│                                                                              │
│   validated code ──► CodeSandbox ──► manimgl (xvfb) ──► S3 upload ──► URL   │
│         │                                    │                               │
│         ▼                                    ▼                               │
│   ┌───────────┐                       ┌───────────────┐                     │
│   │  SQLite   │                       │    MinIO/S3   │                     │
│   │ (tracker) │                       │   (storage)   │                     │
│   └───────────┘                       └───────────────┘                     │
│                                                                              │
└─────────────────────────────────────────────────────────────────────────────┘

Components

Component	Description
ConceptAnalyzer	Extracts domain, complexity, and key concepts from prompts
ScenePlanner	Designs animation structure, timing, and transitions
CodeGenerator	Generates manimgl code using scenes, API signatures, and animation patterns
CodeReviewer	Validates code quality and applies fixes
ParameterValidator	Validates method parameters against API signatures
GeminiTTSService	Parallel TTS with Gemini voices, generates narration script
ChromaDBService	Vector similarity search across 5,300+ indexed documents
ProbeSearcher	AST-aware semantic code search using Probe (tree-sitter + BM25)
Linter	Pre-generation code validation using ruff
SelfCritique	Multi-pass code generation with self-review
SchemaGenerator	JSON schema-based structured scene generation
TemplateGenerator	Template-first generation (fill-in-the-middle style)
CodeSandbox	AST-based security validation (blocks dangerous code)
ManimRenderer	Executes manimgl with xvfb for headless rendering
S3Storage	Uploads to MinIO/S3 with presigned URLs
RenderTracker	Persists job metadata in SQLite

RAG Collections

Collection	Documents	Description
`manim_scenes`	3,140	Production 3Blue1Brown scene code
`manim_api`	1,652	API signatures with exact parameters
`animation_patterns`	101	Reusable animation templates
`manim_docs`	470	manimgl library documentation
`error_patterns`	16+	Self-learning error/fix patterns

Self-Learning

The system learns from every error:

Validation failures - Stored with fixes when LLM corrects them
Render failures - Stored for future pattern matching
Successful fixes - Stored as error→fix pairs for RAG retrieval

This creates a feedback loop where the system improves over time.

MCP Tools

When running as an MCP server, these tools are available:

Tool	Description
`generate_animation`	Create an animation from a text prompt
`edit_animation`	Edit an existing animation with instructions
`list_renders`	List past renders with pagination and filtering
`get_render`	Get full details and a fresh download URL
`delete_render`	Permanently delete a render and its files
`rag_search`	Search the RAG database for similar scenes
`rag_stats`	Get collection statistics

Recommended Prompts

The system performs best with mathematical and educational topics that have high RAG coverage:

Topic	Indexed Scenes	Example Prompts
Linear Algebra	810+	"Animate a matrix transformation", "Show eigenvectors during transformation"
Geometry	568+	"Visual proof of Pythagorean theorem", "Inscribed angle theorem"
Probability	290+	"Central limit theorem", "Bayes theorem with updating priors"
Calculus	178+	"Derivative as tangent slope", "Riemann sums converging to integral"

Development

pip install -e ".[dev,rag]"
pytest

Testing Scripts

# Test all LLM provider combinations (Gemini/Claude × Simple/Advanced × RAG On/Off)
python scripts/test_providers.py
python scripts/test_providers.py --no-audio  # Skip audio generation
python scripts/test_providers.py --quick     # Only simple mode tests

# Benchmark LLM providers (DeepSeek vs Gemini)
python scripts/benchmark_providers.py
python scripts/benchmark_providers.py --providers gemini,deepseek --categories simple,medium

License

MIT

Manim Mcp— agent skill

Is Manim Mcp safe to install?

manim-mcp

Examples

Features

Quick Start

Prerequisites

Environment Variables

Usage

Generate an animation

Generate with audio narration

Edit an existing animation

List, inspect, delete renders

Agent mode

MCP server

RAG Indexing

Docker

Architecture

Components

RAG Collections

Self-Learning

MCP Tools

Recommended Prompts

Development

Testing Scripts

License

Audit the pieces. Scan the whole. Decide.