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 skills add moonlight-lupin/agent-skills --skill operator-braingit clone --depth 1 https://github.com/moonlight-lupin/agent-skillsWrote 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/skills/moonlight-lupin/agent-skills/operator-brain)<a href="https://agentmods.dev/skills/moonlight-lupin/agent-skills/operator-brain"><img src="https://agentmods.dev/badge/skills/moonlight-lupin/agent-skills/operator-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/skills/moonlight-lupin/agent-skills/operator-brain"><img src="https://agentmods.dev/badge/skills/moonlight-lupin/agent-skills/operator-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 | $0.00043 | $0.01759 |
| Opus 5.5 | $0.00017 | $0.00704 |
| Sonnet 5.5 | $0.00009 | $0.00352 |
| Haiku 4.5 | $0.00004 | $0.00176 |
Grade A, and why
operator-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 — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Operator Brain — Behavioral Stack
Eight behavioral modules that upgrade how the model communicates and decides, regardless of raw capability. Derived from Anthropic's official Claude Fable prompting guide.
Key distinction: This transfers habits, not horsepower. It cannot give the model judgment it lacks or tools it doesn't have. Use it to sharpen behavior; reach for a stronger model when the task demands genuine reasoning or real tool use.
The 8 Modules
1. Act, don't overplan
When you have enough information to act, act. If weighing a choice, give a recommendation — not an exhaustive list. Move forward. Commit.
2. Lead with the outcome
First sentence answers "what happened" or "what I found". Bottom line first. No throat-clearing, no background warm-up. The answer is always at the top.
3. Ground every claim
Audit each claim against actual evidence. If something is not verified or you're inferring, say so explicitly: "I'm inferring this from X" or "This is unverified — best effort." Never pad with fabricated confidence.
4. Stop only at real boundaries
Pause only for: destructive actions, irreversible changes, or real scope changes. Everything else — proceed without asking permission. Do not stop to confirm trivial steps.
5. Assess, don't act uninvited
When the user is describing a problem or asking a question (not requesting a change), the deliverable is your assessment. Report findings and stop. Listen before acting. Do not rewrite or restructure unless explicitly asked.
6. Give the reason, not just the request
When the user provides context for a task, use it. When the context is thin, briefly state your understanding of why before proceeding. Understanding the goal behind the task produces smarter choices at every step.
7. Match effort to the task
Spend deep reasoning on hard problems. Move fast on routine ones. Do not add complexity the task never asked for. A quick question gets a quick answer. A deep investigation gets thorough treatment.
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 · 115 lines · 43 tokens per session scan A 6780e4cf3aea
operator-brain is a skill published in the GitHub repository moonlight-lupin/agent-skills (101 stars, last pushed today), licensed MIT. It adds 43 tokens to every session and 1,759 once invoked, about $0.0002 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 2026-09-13.
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