Borrowing it
Nothing to install: this file belongs to AgenticFirst/Cerebro. 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/AgenticFirst/Cerebro/main/CLAUDE.mdgit clone --depth 1 https://github.com/AgenticFirst/CerebroWrote 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/agenticfirst/cerebro/claude-md)<a href="https://agentmods.dev/instructions/agenticfirst/cerebro/claude-md"><img src="https://agentmods.dev/badge/instructions/agenticfirst/cerebro/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/agenticfirst/cerebro/claude-md"><img src="https://agentmods.dev/badge/instructions/agenticfirst/cerebro/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 | $0.00851 | $0.00851 |
| Opus 5.5 | $0.00340 | $0.00340 |
| Sonnet 5.5 | $0.00170 | $0.00170 |
| Haiku 4.5 | $0.00085 | $0.00085 |
Grade A, and why
Cerebro 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 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 — 52 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cerebro — Claude Code context
Cerebro is an Electron + React + TypeScript desktop app with a Python/FastAPI backend that drives AI workflows from natural-language chat. Inference runs through the Claude Code CLI as a subprocess — every chat reply, expert delegation, and routine step that needs reasoning shells out to claude -p.
The full project context, layout, and conventions are in AGENTS.md. Read that first when arriving cold.
Read before touching integrations
docs/adding-integrations.md is the single source of truth for wiring a new connected service (Slack, Gmail, Notion, …). Wiring an integration touches ~15 files across the bridge, IPC, UI, engine actions, routine prompt, and chat skills. Skipping any of them silently breaks part of the surface. Work the playbook top-to-bottom.
Reference implementations: Telegram (src/telegram/), HubSpot (src/hubspot/), WhatsApp (src/whatsapp/). Copy from whichever auth model is closest.
Critical conventions
- Inference is Claude Code only. Don't propose routes via the Anthropic SDK / OpenAI / local models for chat — those exist as routine-step backends only.
- Bilingual (EN + ES) is non-negotiable. Every user-facing string lives in
src/i18n/locales/{en,es}.ts. Chat skills also need Spanish trigger phrases. - Credentials never enter LLM context. Bridges encrypt at rest via
src/secure-token.ts. The chat agent triggers integration setup, but the inline card collects raw tokens through IPC — they never reach the model. - Approvals gate external-facing actions.
run-chat-actionpauses writes/sends for human approval by default. Read-only actions skip the gate: anActionDefinitionwithreadOnly: true(HubSpot search/get/list, calendar query/free-time, Slack channel list) runs immediately — reads have no side effects, matching how routinegetStepDefaultsalready defaults reads torequiresApproval: false. For writes/sends, the one bypass is a user-set, per-destination "don't ask again" rule (today: a specific Slack channel) — stored inauto_approval_rules, managed via themanage-auto-approvalsskill. There is no global skip. Routine steps that touch external services (writes) should setrequiresApproval: true. - Don't delete user data without asking. Settings, conversations, routines, DB rows you didn't create yourself.
- For end-to-end tests, launch Cerebro yourself with
tail -f /dev/null | npm start &. You launch, you verify, you clean up. Kill spawned Electron + Python processes when done. - Memory is auto-managed. Don't write planning files into the repo — work from conversation context.
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 · 52 lines · 851 tokens per session scan A 35514ac28b9a
Cerebro CLAUDE.md is an instructions file published in the GitHub repository AgenticFirst/Cerebro (8 stars, last pushed 3mo ago), licensed MIT. It adds 851 tokens to every session, about $0.0034 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.
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