ghost-font-decoder

Ghost Font Decoder

https://playground.likec4.dev/share/uPAKaklH79/

Give AI agents motion vision.

Decode text hidden inside random-dot “ghost font” videos with Codex, Claude Code, Claude.ai, or plain Python.

No more ghosting. The message is the motion—and this decoder reads it.

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The problem

Ghost-font videos conceal text inside a moving random-dot field. Pause the video and every frame appears to be uniform noise. The letters emerge only when their dots move differently from the background, so image-only readers miss the message entirely.

Ghost Font Decoder analyzes that motion with dense optical flow, reconstructs the hidden glyphs, and gives the agent both a clean reveal and a heatmap it can verify.

Paused frame — looks like noise	Recovered motion mask

Why it stands out

• Agent-native — one ghost-decode skill across Codex, Claude Code, and Claude.ai.
• Actually motion-aware — analyzes movement across frame pairs instead of guessing from a still image.
• Verifiable output — produces both a cleaned binary mask and the raw motion heatmap.
• Works without an agent — the same decoder runs directly from Python.
• OCR is optional — visual recovery still works when Tesseract is unavailable.
• Local by design — decoding runs on the machine executing the skill.

Quick start

Claude Code

Install the plugin, then pass it a video:

/plugin marketplace add haroontrailblazer/ghost-font-decoder
/plugin install ghost-font-decoder@ghost-font-tools
/ghost-decode path/to/ghost-video.mp4

You can also ask naturally:

What does ghost-video.mp4 say?

Codex

Add this repository as a plugin source, install Ghost Font Decoder, start a new task, and ask:

Use $ghost-decode to tell me what ghost-video.mp4 says.

Codex runs the bundled decoder, inspects the generated mask, displays the result in the task, and reports the recovered text.

Claude.ai

Build the uploadable skill package:

python claude-ai-skill/build-zip.py

Upload ghost-decode.zip under Settings → Capabilities → Skills, attach a video, and ask what it says. See the Claude.ai installation guide for the complete setup.

Plain Python

pip install -r requirements.txt
python decode.py examples/ghost-message.mp4

The included example decodes to:

Text in the video: HELLO HUMAN

No plugin installation is required. You can also paste prompts/decode-in-chat.md into a supported chat, attach the video, and run the same optical-flow workflow.

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Proof: decoded by real agents

The screenshots below document the same hidden message—I LOVE YOU—being recovered in Claude Code, Claude.ai, and Codex. They show the agent invocation, the reported text, and the generated visual evidence.

1. Claude Code — successful decode

Claude Code ran the decoder across 185 frame pairs, compensated for drift, and identified the full three-line message.

2. Claude.ai — skill invoked from chat

The uploaded ghost-decode skill accepted the video and returned the hidden message directly in the conversation.

3. Claude.ai — generated evidence files

Claude.ai returned both outputs used to verify the answer: the cleaned reveal and the raw motion heatmap.

4. Claude.ai — recovered mask preview

Opening the generated reveal makes the hidden I LOVE YOU message directly visible.

5. Codex — plugin result rendered in the task

Codex invoked the installed skill, reported the decoded text, rendered the reveal, and linked both generated outputs.

The screenshots are execution evidence, not mockups. The decoded masks are generated from motion in the supplied video.

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How it works

Video
  ↓
Dense optical flow between consecutive frames
  ↓
Median background-motion subtraction
  ↓
Counter-motion scoring and drift registration
  ↓
Temporal score accumulation
  ↓
Thresholding and mask cleanup
  ↓
Revealed image + heatmap + optional OCR

Under the hood, the decoder:

1. Computes dense optical flow between consecutive frames.
2. Estimates and subtracts the dominant background movement.
3. Scores pixels moving against that background.
4. Registers the drifting glyph region with phase correlation.
5. Accumulates evidence across time, thresholds it, and cleans the mask.
6. Runs optional Tesseract OCR and lets the agent verify the visual result.

Output

Each run writes:

File	Purpose
revealed.png	Clean binary mask for direct visual inspection and OCR
revealed_heatmap.png	Raw opposition score for faint or partially recovered glyphs

Useful command-line options:

python decode.py VIDEO \
  -o OUT_DIR \
  --method farneback \
  --stride 2 \
  --max-frames 200 \
  --no-ocr

Plugin structure

This repository ships native skill packaging for each supported agent:

Surface	Integration
Claude Code	.claude-plugin/plugin.json, marketplace metadata, and claude-skills/ghost-decode/SKILL.md
Claude.ai	Uploadable skill built from claude-ai-skill/ghost-decode/SKILL.md
Codex	.codex-plugin/plugin.json, codex-skills/ghost-decode/SKILL.md, and agents/openai.yaml
Any supported chat	Portable workflow in prompts/decode-in-chat.md
Standalone	decode.py and requirements.txt

Requirements

• Python 3.8 or newer
• OpenCV
• NumPy
• pytesseract for optional OCR

Install Python dependencies:

pip install -r requirements.txt

On Windows, Tesseract can be installed with:

winget install UB-Mannheim.TesseractOCR

The reveal and heatmap are still generated when Tesseract is not installed.

Repository map

ghost-font-decoder/
├── decode.py                     # Optical-flow decoder
├── examples/                     # Reproducible sample video and outputs
├── claude-skills/                # Claude Code skill
├── claude-ai-skill/              # Claude.ai uploadable skill source
├── codex-skills/                 # Codex skill and UI metadata
├── commands/                     # Claude Code slash command
├── prompts/                      # Portable in-chat workflow
└── docs/                         # Project page and proof assets

The core insight

Ghost fonts are often presented as text that AI cannot read. The harder truth is simply that the information is encoded in motion, not in any single frame. Dense optical flow is designed to measure exactly that signal.

This project packages a proven computer-vision technique as an agent skill, so the same assistant that receives the video can run the analysis, inspect the evidence, and answer with the recovered message.

License

Released under the MIT License.

Author

Built by Haroon K M (@haroontrailblazer).