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/vicquick/nobrainr/claude-mdgit clone --depth 1 https://github.com/vicquick/nobrainrWhat 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 | $0.07057 | $0.07057 |
| Opus 5 | $0.03528 | $0.03528 |
| Sonnet 5 | $0.01411 | $0.01411 |
| Haiku 4.5 | $0.00706 | $0.00706 |
Grade A, and why
nobrainr CLAUDE.md 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 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.
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 — 476 lines — stays where its author put it; the contents beside it link to each section on GitHub.
nobrainr — Collective Agent Memory Service
What This Is
Persistent shared memory for AI coding agents. If you're an agent reading this:
you can store learnings with memory_store, search past knowledge with memory_search,
and explore the knowledge graph with entity_search / entity_graph. Everything you
store is available to every other agent instance connected to this server.
Provides hybrid search (vector + full-text RRF) by default, context-enriched embeddings, chunked document ingestion with overlapping context, optional cross-encoder reranking, chunk-aware retrieval, automatic entity extraction, on-write dedup, embedding versioning, and a Vue 3 dashboard with interactive graph visualization.
Canonical Categories
Use one of these when storing memories — freeform categories are auto-normalized:
architecture, debugging, deployment, infrastructure, patterns, tooling,
security, frontend, backend, data, business, documentation, session-log,
insight, _archived
The system learns autonomously: it summarizes, consolidates duplicates, synthesizes cross-entity insights, detects contradictions, validates its own extractions, discovers cross-machine patterns, and archives stale knowledge — all on scheduled LLM-powered jobs.
Architecture
- Backend — Python ASGI: FastMCP (HTTP + SSE) + pure JSON API (Starlette)
- Frontend — Vue 3 + Vuetify + Cytoscape.js (separate container, nginx)
- PostgreSQL 18 + pgvector — storage, vector similarity, knowledge graph
- llama-swap (Coolify-deployed) — hosts three on-GPU
llama-serverprocesses serving the entire LLM stack from a single container:- Main LLM —
Qwen3.6-27B-IQ4_XS.gguf(port 5803, 32K ctx, parallel=2, all layers on GPU) handles extraction, chat, scheduler jobs, dedup, synthesis - Embeddings —
Qwen3-Embedding-0.6B-Q8_0.gguf(port 5802, 4K ctx, all layers on GPU) 1024-dim vectors, OpenAI-compatible/v1/embeddings - Reranker —
bge-reranker-v2-m3-Q8_0.gguf(port 5800, 4K ctx, all layers on GPU) cross-encoder rerank via llama.cpp/v1/rerank, sigmoid-normalised
- Main LLM —
- Crawl4AI — web crawling service (CPU-only container on
mcpnetwork, port 11235)
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 · 476 lines · 7,057 tokens per session scan A 3baab2042990
nobrainr CLAUDE.md is an instructions file published in the GitHub repository vicquick/nobrainr (1 stars, last pushed 12d ago), licensed MIT. It adds 7,057 tokens to every session, about $0.0353 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 2026-08-31.
Other instructions, from other repositories
mem0-mcp-selfhosted CLAUDE.md
Instructions for elvismdev/mem0-mcp-selfhosted, covering claude.md, mcp servers, build & test commands and architecture.
Muninn GEMINI.md
Gemini CLI instructions for wjohns989/Muninn, covering muninn mcp — sota+ development context, 🚀 sota+ development philosophy, 🛠 operational guidelines (always-on), 🏗 system architecture and 📂 project structure.
flux-memory CLAUDE.md
Instructions for harsh5i/flux-memory, covering flux memory v0.5 — claude.md, hard constraints (never deviate), repo layout, development rules and status.
dakera-mcp CLAUDE.md
Claude Code instructions for Dakera-AI/dakera-mcp, covering dakera-mcp, key commands, architecture and conventions.
remnic AGENTS.md
Instructions for joshuaswarren/remnic, covering remnic - agent guide, architecture boundaries (non-negotiable), upstream references, adapter implementation rules and openclaw compatibility window.
sibyl CLAUDE.md
Instructions for hyperb1iss/sibyl, covering sibyl development guide, project overview, sibyl integration, always use skills and research → do → reflect cycle.