Borrowing it
Nothing to install: this file belongs to Cvartel/Face-Recognition-MCP-Server. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Cvartel/Face-Recognition-MCP-Server/main/CLAUDE.mdgit clone --depth 1 https://github.com/Cvartel/Face-Recognition-MCP-ServerWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/instructions/cvartel/face-recognition-mcp-server/claude-md)<a href="https://agentmods.dev/instructions/cvartel/face-recognition-mcp-server/claude-md"><img src="https://agentmods.dev/badge/instructions/cvartel/face-recognition-mcp-server/claude-md/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/instructions/cvartel/face-recognition-mcp-server/claude-md"><img src="https://agentmods.dev/badge/instructions/cvartel/face-recognition-mcp-server/claude-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.03588 | $0.03588 |
| Opus 5.5 | $0.01435 | $0.01435 |
| Sonnet 5.5 | $0.00718 | $0.00718 |
| Haiku 4.5 | $0.00359 | $0.00359 |
Grade A, and why
Face-Recognition-MCP-Server CLAUDE.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 349 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Face SDK — AI Agent Reference
3DiVi Face SDK v3.29 · Processing Block API · Local MCP server
This file is read automatically by Claude Code, Cursor, and Windsurf.
It contains everything an AI agent needs to generate correct SDK integration code.
Face SDK Overview
3DiVi Face SDK v3.29 provides on-premise biometric processing. All computation runs locally — no data leaves the machine. Two API families exist:
- Processing Block API (v3.29) — context-based, use this for all new integrations
- Legacy API — deprecated, do not use
Primary language: Python. C++, Java, C#, Node.js also supported.
Initialisation
import face_sdk_3divi as F3
# REQUIRED: first arg = SDK root directory (contains lib/, conf/)
# REQUIRED: config_dir = path to facerec.conf file
service = F3.create_service(
'./facesdk', # SDK root
config_dir='./conf/facerec.conf' # config file path — not the directory
)
SDK directory layout (must match the path you pass to create_service()):
./facesdk/
├── lib/ # shared libraries (.so / .dll / .dylib)
├── conf/
│ └── facerec.conf # pass this full path to config_dir
└── license/ # licence file (auto-detected from conf/)
Common mistake: passing './conf' instead of './conf/facerec.conf' raises a licence error.
Context container
Every Processing Block reads from and writes to a Context object — a typed key-value store (analogous to JSON). It is the only way to pass data between blocks.
# Create
ctx = service.create_context()
# Populate input — nested key access with [][]
ctx['image']['blob'] = open('photo.jpg', 'rb').read() # raw bytes
ctx['image']['format'] = 'JPEG' # 'JPEG' | 'PNG' | 'BMP' | 'TIFF'
# Call a processing block (modifies ctx in-place)
detector = service.create_processing_block({'unit_type': 'FACE_DETECTOR'})
detector(ctx)
# Read output
faces = ctx['output_data'] # list of face objects
if faces:
bbox = faces[0]['bbox'] # dict: x, y, width, height
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2mo ago First seen · 349 lines · 3,588 tokens per session scan A 8b5c6226e1ab
Face-Recognition-MCP-Server CLAUDE.md is an instructions file published in the GitHub repository Cvartel/Face-Recognition-MCP-Server (0 stars, last pushed 6mo ago), licensed MIT. It adds 3,588 tokens to every session, about $0.0144 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Face-Recognition-MCP-Server CLAUDE.md compared with instructions from other repositories
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|---|---|---|---|---|---|---|---|
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