server-memory AGENTS.md

Repository instructions for working on server-memory, a local memory service that stores and searches information for coding agents. They require separate storage scopes and predictable command-line behavior.

In plain words
What is it for?
Use them when preparing, testing, or editing server-memory. They cover Python and SQLite FTS5 checks, isolated environment setup, and development installation.
Why use it?
They give agents setup steps and boundaries that protect the project’s local-only design, including no hosted services, telemetry, external credentials, or required network access.

Instructions file for CodexOpenCode

Install

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.

agentmods
npx agentmods add instructions/mk-986123/server-memory/agents-md
Clone the repo
git clone --depth 1 https://github.com/MK-986123/server-memory

Made for: Codex, OpenCode.

Per session 808 This file is loaded in full into every session.
When invoked 808 The same file — it is already loaded in full.
Security scan C 1 finding. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00808 $0.00808
Opus 5 $0.00404 $0.00404
Sonnet 5 $0.00162 $0.00162
Haiku 4.5 $0.00081 $0.00081

Measured 2d ago against content hash d4ed896fc604, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade C, and why

server-memory AGENTS.md scanned grade C 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.

Recursive force deletehighDestructive command

rm -rf with a variable or a broad path is one typo away from removing the wrong tree.

rm -rf dist build
AGENTS.md · 116 lines

How it starts

The opening of the file, as written. The whole thing — 116 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Repository instructions for coding agents

These instructions apply to AI coding agents and automated development tools working in this repository.

Objective

Preserve server-memory as a local-first MCP memory server with explicit storage, bounded recall, workspace and global scope separation, and predictable stdio behavior.

Do not introduce hosted services, external credentials, telemetry, or mandatory network dependencies into the core package without explicit maintainer approval.

Initial setup

  1. Confirm Python 3.10 or newer.
  2. Confirm SQLite FTS5 support.
  3. Create an isolated virtual environment.
  4. Install the editable project with development dependencies.

Linux and macOS:

python3 -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip
python -m pip install -e ".[dev]"
python -c "import sqlite3; c=sqlite3.connect(':memory:'); c.execute('CREATE VIRTUAL TABLE t USING fts5(content)'); c.close(); print('FTS5 available')"

Windows PowerShell:

py -m venv .venv
.venv\Scripts\Activate.ps1
python -m pip install --upgrade pip
python -m pip install -e ".[dev]"
python -c "import sqlite3; c=sqlite3.connect(':memory:'); c.execute('CREATE VIRTUAL TABLE t USING fts5(content)'); c.close(); print('FTS5 available')"

Install optional embeddings only when the task requires them:

python -m pip install -e ".[dev,embeddings]"

Safe test environment

Never use a real user memory database for tests or exploratory commands.

Set an isolated database path and disable global memory unless the task specifically tests global scope behavior:

export MEMORY_DB_PATH="$PWD/.tmp/agent-memory.db"
export MEMORY_GLOBAL_DB_ENABLED=false

PowerShell:

$env:MEMORY_DB_PATH = "$PWD\.tmp\agent-memory.db"
$env:MEMORY_GLOBAL_DB_ENABLED = "false"

Do not commit .tmp, SQLite databases, exports, backups, token files, model caches, virtual environments, or build artifacts.

Before editing

  • Read README.md, CONTRIBUTING.md, and the files directly involved in the task.
  • Inspect existing tests before changing behavior.
  • Preserve public tool names, parameter shapes, scope semantics, and JSON-RPC behavior unless the task explicitly requires a breaking change.
  • Keep stdio stdout protocol-clean. Send diagnostics to stderr or logging.
  • Keep destructive operations explicit and scoped. Do not weaken rejection of scope="all" for destructive tools.
  • Do not add performance claims without reproducible raw results.

Read the full file on GitHub · 116 lines

Changes

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.

  1. 2d ago First seen · 116 lines · 808 tokens per session scan C d4ed896fc604

Subscribe to this mod's changes

server-memory AGENTS.md is an instructions file published in the GitHub repository MK-986123/server-memory (1 stars, last pushed 24d ago), licensed MIT. It adds 808 tokens to every session, about $0.0040 per session on Opus 5. A static security scan graded it C with 1 finding (recursive force delete). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.