Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add instructions/aeyeops/mcp-imagemagick/claude-mdgit clone --depth 1 https://github.com/AeyeOps/mcp-imagemagickWrote 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/aeyeops/mcp-imagemagick/claude-md)<a href="https://agentmods.dev/instructions/aeyeops/mcp-imagemagick/claude-md"><img src="https://agentmods.dev/badge/instructions/aeyeops/mcp-imagemagick/claude-md.svg" alt="Measured on agentmods" 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 | $0.00863 | $0.00863 |
| Opus 5 | $0.00432 | $0.00432 |
| Sonnet 5 | $0.00173 | $0.00173 |
| Haiku 4.5 | $0.00086 | $0.00086 |
Grade A, and why
mcp-imagemagick 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 6d 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 — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CLAUDE.md
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
Project Overview
This is an MCP (Model Context Protocol) server for image conversion, primarily focused on converting DNG (Digital Negative) files to WebP format using ImageMagick and darktable. The server implements the MCP protocol with stdio transport and provides tools for image conversion.
Common Development Commands
Building
# Development build
cargo build
# Release build (optimized)
cargo build --release
Testing
# Run unit tests
cargo test
# Run integration tests
python3 test_mcp.py
# Test MCP protocol manually
./test_protocol.py
# Quick server test
./test_server.sh
Running and Debugging
# Run with debug logging
RUST_LOG=debug cargo run
# Run release binary
./target/release/mcp-imagemagick
# Test with MCP Inspector (if installed)
mcp-inspector ./target/release/mcp-imagemagick
Code Quality
# Format code
cargo fmt
# Run linter
cargo clippy
# Check for issues without building
cargo check
High-Level Architecture
The codebase follows a modular architecture with clear separation of concerns:
Core Components
-
MCP Server Layer (
src/server.rs)- Handles JSON-RPC protocol implementation
- Routes requests to appropriate handlers
- Manages the message loop and response formatting
- Implements MCP methods:
initialize,tools/list,tools/call
-
Transport Layer (
src/transport.rs)- Manages stdio communication (stdin/stdout)
- Handles async message passing between threads
- Buffers and parses JSON-RPC messages
-
Converter System (
src/converters/)- Trait-based Design: All converters implement the
ImageConvertertrait - Auto-selection:
AutoConverterautomatically selects the best available converter based on priority - ImageMagick Converter: Priority 60, uses
convert7ormagickcommand - Darktable Converter: Priority 40, uses
darktable-clifor better RAW processing - Converters are checked for availability using the
whichcrate
- Trait-based Design: All converters implement the
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.
- 6d ago First seen · 121 lines · 863 tokens per session scan A cac7eea46dee
mcp-imagemagick CLAUDE.md is an instructions file published in the GitHub repository AeyeOps/mcp-imagemagick (15 stars, last pushed 1y ago), licensed MIT. It adds 863 tokens to every session, about $0.0043 per session on Opus 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 2026-08-30.
Other instructions, from other repositories
vscode buildNext.instructions.md
Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).
codex AGENTS.md
AGENTS.md instructions for openai/codex, covering rust/codex-rs, the codex-core crate, code review rules, crate api surface and model visible context.
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, applying the notice (cutover) and fallback chain (never fail the build).
spec-kit AGENTS.md
AGENTS.md instructions for github/spec-kit, covering agents.md, about spec kit and specify, quickstart — add a new integration in 5 steps, integration architecture and integrationmanifest — file tracking.
next.js AGENTS.md
AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.
langchain AGENTS.md
AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.