Borrowing it
Nothing to install: this file belongs to n24q02m/mnemo-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/n24q02m/mnemo-mcp/main/CLAUDE.mdgit clone --depth 1 https://github.com/n24q02m/mnemo-mcpWrote 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/n24q02m/mnemo-mcp/claude-md)<a href="https://agentmods.dev/instructions/n24q02m/mnemo-mcp/claude-md"><img src="https://agentmods.dev/badge/instructions/n24q02m/mnemo-mcp/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 | $0.02991 | $0.02991 |
| Opus 5 | $0.01496 | $0.01496 |
| Sonnet 5 | $0.00598 | $0.00598 |
| Haiku 4.5 | $0.00299 | $0.00299 |
Grade B, and why
mnemo-mcp CLAUDE.md scanned grade B 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 8d 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.
Asks for rootmediumPrivilege escalation
A mod that escalates privileges can change anything on the machine, not only the project.
token_store.py # OAuth token storage (secure file-based, chmod 600) This is a copy
100% identical to mnemo-mcp AGENTS.md — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 194 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CLAUDE.md - mnemo-mcp
MCP Server cho AI memory. Python 3.13, uv, hatchling, src layout.
Hybrid search: FTS5 + sqlite-vec semantic. 15 tools: 11 specialized memory tools (add_memory, search_memory, list_memories, update_memory, delete_memory, export_memories, import_memories, memory_stats, restore_memory, archived_memories, consolidate_memories) + legacy memory dispatcher (DEPRECATED -- use the granular tools instead) + config + help + config__open_relay.
2-mode embedding: cloud chain (EMBEDDING_MODELS) > Local (Qwen3 ONNX khi chain rong). Per-task model chains (EMBEDDING_MODELS/RERANK_MODELS/LLM_MODELS, order = litellm fallback). LLM/Embed/Rerank: litellm passthrough qua mcp_core.llm (mcp-core[llm]).
Commands
# Setup
uv sync --group dev
# Lint & Type check
uv run ruff check .
uv run ruff format --check .
uv run ty check
# Fix
uv run ruff check --fix .
uv run ruff format .
# Test (integration excluded by default)
uv run pytest
uv run pytest tests/test_db.py -v # single file
uv run pytest tests/test_db.py::TestSearch::test_basic -v # single test
# Build & Run
uv build
uv run mnemo-mcp # run server (warmup/setup_sync via config tool)
# Mise shortcuts
mise run setup # full dev setup
mise run lint # ruff check + format check + ty check
mise run test # pytest
mise run fix # ruff fix + format
Pytest
asyncio_mode = "auto"-- khong can@pytest.mark.asyncio- Timeout: 30s/test
- Integration marker:
@pytest.mark.integration(can network/services) - Default:
-m 'not integration and not live and not full' - Snapshot testing: syrupy
Cau truc thu muc
src/mnemo_mcp/
__main__.py # python -m mnemo_mcp entrypoint
config.py # Pydantic Settings (singleton), env vars khong co prefix
server.py # FastMCP server, tools, resources, prompts
setup_tool.py # Warmup + setup-sync logic (config tool actions)
db.py # SQLite: CRUD, FTS5, vector search (sqlite-vec)
embedder.py # Dual-backend: multi-provider cloud (Jina/Gemini/OpenAI/Cohere) + fastretrieval local
reranker.py # Dual-backend reranking: cloud (Jina/Cohere) + local (fastretrieval cross-encoder)
graph.py # Knowledge graph: entity/relation extraction via LLM
relay_setup.py # Zero-config relay: create session, poll for config
relay_schema.py # Relay form schema (local + cloud modes)
sync/ # Sync backends: gdrive.py (OAuth Device Code, httpx) + s3.py (R2/B2/MinIO) + delta/bundle/base
token_store.py # OAuth token storage (secure file-based, chmod 600)
docs/ # Tool documentation markdown
tests/ # 1:1 mapping voi source modules
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.
- 8d ago First seen · 194 lines · 2,991 tokens per session scan B 1ea1118c863f
mnemo-mcp CLAUDE.md is an instructions file published in the GitHub repository n24q02m/mnemo-mcp (10 stars, last pushed today), licensed Apache-2.0. It adds 2,991 tokens to every session, about $0.0150 per session on Opus 5. A static security scan graded it B with 1 finding (asks for root). It is 100% identical to mnemo-mcp AGENTS.md, differing in 2 lines, and is treated as a copy.
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