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 skills/sopaco/cortex-mem/skillnpx skills add sopaco/cortex-mem --skill skillgit clone --depth 1 https://github.com/sopaco/cortex-memWhat 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 | $0.00072 | $0.02911 |
| Opus 5 | $0.00036 | $0.01456 |
| Sonnet 5 | $0.00014 | $0.00582 |
| Haiku 4.5 | $0.00007 | $0.00291 |
Grade A, and why
cortex-mem-mcp scanned grade A with 1 finding 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 2d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl http://localhost:6333 How it starts
The opening of the file, as written. The whole thing — 459 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cortex Memory MCP Skill
This skill enables persistent memory capabilities for AI agents, allowing them to store, search, and recall information across sessions using semantic retrieval.
Prerequisites Check
Before configuring this skill, verify if cortex-mem-mcp is available in your system:
# Check if cortex-mem-mcp is in PATH
which cortex-mem-mcp || where cortex-mem-mcp # Linux/macOS || Windows
If the command returns a path, the binary is already installed. If not, proceed to the installation section below.
Installation
Option 1: Install from crates.io (Recommended)
cargo install cortex-mem-mcp
After installation, verify:
cortex-mem-mcp --version
Option 2: Build from Source
# Clone the repository
git clone https://github.com/sopaco/cortex-mem.git
cd cortex-mem
# Build the release binary
cargo build --release --bin cortex-mem-mcp
# The binary will be at:
# ./target/release/cortex-mem-mcp (Linux/macOS)
# .\target\release\cortex-mem-mcp.exe (Windows)
Option 3: Download Pre-built Binary
Download the latest release from GitHub:
- GitHub Releases: https://github.com/sopaco/cortex-mem/releases
Choose the appropriate binary for your platform:
cortex-mem-mcp-linux-x86_64(Linux x64)cortex-mem-mcp-darwin-arm64(macOS Apple Silicon)cortex-mem-mcp-darwin-x86_64(macOS Intel)cortex-mem-mcp-windows-x86_64.exe(Windows x64)
Configuration
Step 1: Create Configuration File
Create a config.toml file (e.g., ~/.config/cortex-mem/config.toml):
[cortex]
# Data directory for storing memories
data_dir = "~/.cortex-data"
[llm]
# LLM API configuration
api_base_url = "https://api.openai.com/v1"
api_key = "your-api-key"
model_efficient = "gpt-5-mini"
temperature = 0.1
max_tokens = 65536
[embedding]
# Embedding configuration
api_base_url = "https://api.openai.com/v1"
api_key = "your-embedding-api-key"
model_name = "text-embedding-3-small"
batch_size = 10
timeout_secs = 30
[qdrant]
# Vector database configuration
url = "http://localhost:6334"
collection_name = "cortex_memories"
embedding_dim = 1536
timeout_secs = 30
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.
- 2d ago First seen · 459 lines · 72 tokens per session scan A 2a2ae96ac8d6
cortex-mem-mcp is a skill published in the GitHub repository sopaco/cortex-mem (306 stars, last pushed 1mo ago), licensed MIT. It adds 72 tokens to every session and 2,911 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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