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.
git clone --depth 1 https://github.com/GuiLopes29/brain-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/rules/guilopes29/brain-mcp/brain)<a href="https://agentmods.dev/rules/guilopes29/brain-mcp/brain"><img src="https://agentmods.dev/badge/rules/guilopes29/brain-mcp/brain/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/rules/guilopes29/brain-mcp/brain"><img src="https://agentmods.dev/badge/rules/guilopes29/brain-mcp/brain.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 | $0.00722 | $0.00722 |
| Opus 5.5 | $0.00289 | $0.00289 |
| Sonnet 5.5 | $0.00144 | $0.00144 |
| Haiku 4.5 | $0.00072 | $0.00072 |
Grade A, and why
brain 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 1mo 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 — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Brain MCP — Usage Rules
⚡ Task start: load the guardrails (1 cheap call)
BEFORE starting a coding task, call get_guidelines({ project: "<project-name>", source: "cursor" }) once.
It returns ≤12 short directives (learned rules and pitfalls) — hundreds of tokens, not thousands. Apply them to the work.
Token-economy rules:
- Call
get_guidelinesonly once per task, never repeatedly. - Use
search_knowledgeonly when you need detail on a specific solution the guardrails mentioned.
Automatic Knowledge Capture
When wrapping up a development session where a problem was solved or a technical decision was made, automatically call add_knowledge with:
- title: one-line summary of what was solved
- content: full description — problem, root cause, solution applied, alternatives considered
- tags: technologies and concepts involved
- project: name of the repository or project in context
- source:
"cursor" - problem: original problem description
- kind:
solution|rule|pitfall|decision - directive: ONE imperative, actionable line capturing the lesson (required when
kindisruleorpitfall)
Do this without being asked. It's the system's memory.
Quality criteria — only write if at least one of these is true
- The problem wasn't obvious (required real investigation to find the cause)
- It cost real time (more than ~10 min of debugging or research)
- It's a decision another AI would repeat without this context
- It's a pattern that will show up again in the project
Don't write: trivial fixes, typos, obvious language/framework behavior.
⚠️ NEVER write to the Brain
- Credentials, tokens, API keys, passwords, connection strings
- PII of real users (names, emails, national IDs, customer data)
- Code snippets with identifiable proprietary data from your employer/client
- Any string that looks like a secret (patterns:
AKIA,-----BEGIN,eyJ,Bearer,sk-)
If the content to write contains any of these, omit it or replace it with [REDACTED].
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.
- 1mo ago First seen · 66 lines · 722 tokens per session scan A 7a3817fa82a7
brain is a cursor rule published in the GitHub repository GuiLopes29/brain-mcp (0 stars, last pushed 3mo ago), licensed MIT. It adds 722 tokens to every session, about $0.0029 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 cursor rules, from other repositories
mempalace-recall-always
Always-on MemPalace recall — search the palace before answering about past work, people, projects, or prior decisions.
dreamd-recall
Recall lessons, decisions, and prior context from the .agent/ memory daemon. Use when starting work in a project that has a .agent/ folder, when the user references a past decision, or when you are about to make a choice that has a documented prior.
session-memory
Use at conversation wrap-up or when the user explicitly indicates end-of-session — capture residual lessons not captured in-flight.
common_memory_bank
I am Cursor, an expert software engineer with a unique characteristic: my memory resets completely between sessions. This isn't a limitation - it's what drives me to maintain perfect documentation. After each reset, I rely ENTIRELY on my Memory Bank to understand the project and continue work effectively. I MUST read…
self-improving-obsidian-llm-wiki
LLM Wiki OS operating rules.
memsprout
Cursor rule "memsprout" from memsprout/agents, covering using memsprout, search first, capturing, spaces and topics and files.