mcp-memory-service is a self-hosted memory backend that lets AI agents store and retrieve shared project context through REST, MCP, OAuth, a command-line interface, and a dashboard. It is intended for agent pipelines and clients such as LangGraph, CrewAI, AutoGen, Claude Desktop, and OpenCode, with support for knowledge graphs and memory consolidation. The catalogue includes skills, agents, commands, instructions, hooks, a setting, an MCP entry, and a plugin for its workflows.
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/doobidoo/mcp-memory-service/gitnexus-exploringnpx skills add doobidoo/mcp-memory-service --skill gitnexus-exploringgit clone --depth 1 https://github.com/doobidoo/mcp-memory-serviceWrote 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/skills/doobidoo/mcp-memory-service/gitnexus-exploring)<a href="https://agentmods.dev/skills/doobidoo/mcp-memory-service/gitnexus-exploring"><img src="https://agentmods.dev/badge/skills/doobidoo/mcp-memory-service/gitnexus-exploring.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 | $0.00016 | $0.00661 |
| Opus 5 | $0.00008 | $0.00331 |
| Sonnet 5 | $0.00003 | $0.00132 |
| Haiku 4.5 | $0.00002 | $0.00066 |
Grade A, and why
gitnexus-exploring 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 5d 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 — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Exploring Codebases with GitNexus
When to Use
- "How does authentication work?"
- "What's the project structure?"
- "Show me the main components"
- "Where is the database logic?"
- Understanding code you haven't seen before
Workflow
1. READ gitnexus://repos → Discover indexed repos
2. READ gitnexus://repo/{name}/context → Codebase overview, check staleness
3. gitnexus_query({query: "<what you want to understand>"}) → Find related execution flows
4. gitnexus_context({name: "<symbol>"}) → Deep dive on specific symbol
5. READ gitnexus://repo/{name}/process/{name} → Trace full execution flow
If step 2 says "Index is stale" → run
npx gitnexus analyzein terminal.
Checklist
- [ ] READ gitnexus://repo/{name}/context
- [ ] gitnexus_query for the concept you want to understand
- [ ] Review returned processes (execution flows)
- [ ] gitnexus_context on key symbols for callers/callees
- [ ] READ process resource for full execution traces
- [ ] Read source files for implementation details
Resources
| Resource | What you get |
|---|---|
gitnexus://repo/{name}/context |
Stats, staleness warning (~150 tokens) |
gitnexus://repo/{name}/clusters |
All functional areas with cohesion scores (~300 tokens) |
gitnexus://repo/{name}/cluster/{name} |
Area members with file paths (~500 tokens) |
gitnexus://repo/{name}/process/{name} |
Step-by-step execution trace (~200 tokens) |
Tools
gitnexus_query — find execution flows related to a concept:
gitnexus_query({query: "payment processing"})
→ Processes: CheckoutFlow, RefundFlow, WebhookHandler
→ Symbols grouped by flow with file locations
gitnexus_context — 360-degree view of a symbol:
gitnexus_context({name: "validateUser"})
→ Incoming calls: loginHandler, apiMiddleware
→ Outgoing calls: checkToken, getUserById
→ Processes: LoginFlow (step 2/5), TokenRefresh (step 1/3)
Example: "How does payment processing work?"
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.
- 5d ago First seen · 76 lines · 16 tokens per session scan A 23a2284d3745
gitnexus-exploring is a skill published in the GitHub repository doobidoo/mcp-memory-service (1,923 stars, last pushed 3d ago), licensed Apache-2.0. It adds 16 tokens to every session and 661 once invoked, about $0.0001 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 skills, from other repositories
cognee-cli
Use when the user wants to drive cognee from the terminal with cognee-cli — remember/recall/forget/improve memory commands, managing datasets and config, or database migrations.
cognee-install
Use when the user wants to install cognee and run their first remember → recall flow with the Python SDK — fresh setup, virtual env, extras selection, or a minimal working example.
cognee-docker
Use when the user wants to run cognee with Docker or docker compose — trying it out from the prebuilt image, starting the API server in a container, or bringing up the full stack (UI, MCP, Postgres, Neo4j) with compose profiles.
cognee-server
Use when the user wants to run the cognee API server (and optional UI) on their own machine — starting it, checking it's healthy, connecting the SDK or other clients to it, and choosing the right auth posture.
compartmentalize
Sweep this conversation and save everything potentially worth knowing again into Compartment, the encrypted memory vault. Run it before compacting or summarizing so nothing is lost to the summary, or on its own at any point to bank the session.
memwal
Walrus Memory SDK — portable agent memory that works across apps, sessions, and workflows. Use when users say: "add memory to my app" "portable agent memory" "integrate Walrus Memory" "AI agent memory" "memory across agents" "Walrus memory storage" "setup Walrus Memory" "recall memories".