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/zanzythebar/mcp-memory-libsql-go/copilot-instructionsgit clone --depth 1 https://github.com/ZanzyTHEbar/mcp-memory-libsql-goWrote 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/zanzythebar/mcp-memory-libsql-go/copilot-instructions)<a href="https://agentmods.dev/instructions/zanzythebar/mcp-memory-libsql-go/copilot-instructions"><img src="https://agentmods.dev/badge/instructions/zanzythebar/mcp-memory-libsql-go/copilot-instructions.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 | $0.02602 | $0.02602 |
| Opus 5 | $0.01301 | $0.01301 |
| Sonnet 5 | $0.00520 | $0.00520 |
| Haiku 4.5 | $0.00260 | $0.00260 |
Grade A, and why
mcp-memory-libsql-go copilot-instructions.md 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 3d 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.
6. Include example curl verification How it starts
The opening of the file, as written. The whole thing — 343 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GitHub Copilot Instructions for mcp-memory-libsql-go
This document provides guidance for GitHub Copilot when working on this repository.
Project Overview
mcp-memory-libsql-go is a high-performance Go implementation of the MCP (Model Context Protocol) Memory Server using libSQL for persistent storage with vector search capabilities. It provides:
- Persistent Knowledge Graph: Store entities, observations, and relations
- Vector Search: Cosine similarity search using libSQL's vector capabilities
- Hybrid Search: Combines semantic and vector search using RRF (Reciprocal Rank Fusion)
- Multi-Project Support: Manage separate databases for multiple projects
- Multiple Transports: stdio and SSE (Server-Sent Events)
- Multiple Embeddings Providers: OpenAI, Ollama, Google Gemini, Vertex AI, LocalAI, VoyageAI
Technology Stack
- Language: Go 1.24.3
- Database: libSQL (SQLite fork by Turso)
- Database Driver:
tursodatabase/go-libsql - MCP SDK:
modelcontextprotocol/go-sdk - Metrics: Prometheus (optional)
- Testing:
stretchr/testify
Code Organization
.
├── cmd/
│ ├── mcp-memory-libsql-go/ # Main server binary
│ └── integration-tester/ # Integration testing tool
├── internal/
│ ├── apptype/ # Application type definitions
│ ├── buildinfo/ # Build metadata
│ ├── database/ # Database operations and schema
│ ├── embeddings/ # Embeddings provider implementations
│ ├── metrics/ # Prometheus metrics
│ └── server/ # MCP server implementation
├── prompts/ # External prompt definitions (.prompt, .json)
├── docker/ # Docker and compose files
└── docs/ # Additional documentation
Coding Standards
Go Conventions
- Formatting: Always run
gofmt -w .before committing - Linting: Run
go vet ./...to catch common issues - Testing: Run
go test ./...to verify all tests pass - Conventional Commits: Use conventional commit format for commit messages:
feat:for new featuresfix:for bug fixesdocs:for documentation changesrefactor:for code refactoringtest:for test additions/changeschore:for maintenance tasks
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.
- 3d ago First seen · 343 lines · 2,602 tokens per session scan A b831601a4204
mcp-memory-libsql-go copilot-instructions.md is an instructions file published in the GitHub repository ZanzyTHEbar/mcp-memory-libsql-go (7 stars, last pushed 10mo ago), licensed MIT. It adds 2,602 tokens to every session, about $0.0130 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It comes from a forked repository.
Other instructions, from other repositories
llm-wiki-agent CLAUDE.md
Claude Code instructions for SamurAIGPT/llm-wiki-agent, covering llm wiki agent — schema & workflow instructions, slash commands (claude code), directory layout, page format and ingest workflow.
llm-wiki CLAUDE.md
Instructions for ddsyasas/llm-wiki, covering project brief for claude code, read this first, core principles, do not violate, stack lock-in and working conventions.
fkf CLAUDE.md
Instructions for fmind/fkf, a project described as: Local-first Go CLI that turns plain JSON and Markdown work history into reproducible context packs for coding agents.
llm-wiki-agent GEMINI.md
Gemini CLI instructions for SamurAIGPT/llm-wiki-agent, covering llm wiki agent — schema & workflow instructions, how to use, directory layout, page format and ingest workflow.
engraphis AGENTS.md
AGENTS.md instructions for Coding-Dev-Tools/engraphis, covering agents.md — engraphis, internal subagent delegation, 0. read this first — two architectures live in one package, 1. commands and ── unified dashboard + memory inspector ──.
braindb CLAUDE.md
Instructions for dimknaf/braindb, covering braindb — claude instructions, ⚠ tool priority — read this first, it overrides habit, at the start of every session, 1. get always-on rules (behavioral guidelines) and project structure.