Borrowing it
Nothing to install: this file belongs to vicquick/nobrainr. 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/vicquick/nobrainr/main/CLAUDE.mdgit clone --depth 1 https://github.com/vicquick/nobrainrWrote 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/vicquick/nobrainr/claude-md)<a href="https://agentmods.dev/instructions/vicquick/nobrainr/claude-md"><img src="https://agentmods.dev/badge/instructions/vicquick/nobrainr/claude-md/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/instructions/vicquick/nobrainr/claude-md"><img src="https://agentmods.dev/badge/instructions/vicquick/nobrainr/claude-md.svg" alt="Reviewed on agentmods" width="80" 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 | $0.07442 | $0.07442 |
| Opus 5.5 | $0.02977 | $0.02977 |
| Sonnet 5.5 | $0.01488 | $0.01488 |
| Haiku 4.5 | $0.00744 | $0.00744 |
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 26d 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 — 492 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.
- 26d ago Changed · +16 lines · +385 tokens per session 9cfff7b55b2d
- 2mo 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 yesterday), licensed MIT. It adds 7,442 tokens to every session, about $0.0298 per session on Opus 5.5. 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
vscode buildNext.instructions.md
Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).
next.js AGENTS.md
AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, cache eligibility and rollout and applying the notice (cutover).
langchain AGENTS.md
AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.
vscode copilot-instructions.md
Copilot instructions for microsoft/vscode, covering vs code copilot instructions, project overview, root folders, core architecture (src/ folder) and built-in extensions (extensions/ folder).
deepseek-harness AGENTS.md
AGENTS.md instructions for deepseek-ai/deepseek-harness, covering agents.md, pre-stable apis and released session data, repository layout, commands and host sandbox failures.