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 skills/poteto/brainmaxxing/plannpx skills add poteto/brainmaxxing --skill plangit clone --depth 1 https://github.com/poteto/brainmaxxingWhat 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 | $0.00053 | $0.01546 |
| Opus 5 | $0.00026 | $0.00773 |
| Sonnet 5 | $0.00011 | $0.00309 |
| Haiku 4.5 | $0.00005 | $0.00155 |
Grade A, and why
plan 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 — 157 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Plan
Produce implementation plans grounded in project principles. Write plans to brain/plans/. Do NOT implement anything — the plan is the deliverable.
Use Tasks to track progress. Create a task for each step (TaskCreate), mark each in_progress when starting and completed when done (TaskUpdate). Check TaskList after completing each step.
Step 0 — Triage Complexity
Before running the full planning workflow, assess whether this task actually needs a plan:
Trivially small (1–2 files, obvious approach): Tell the user this task doesn't need a plan and suggest implementing directly without the plan skill. Stop here — do not implement.
Needs planning (proceed to Step 1):
- The change spans 3+ files or introduces new architecture
- There are multiple valid approaches and the user should weigh in
- The task has unclear scope or cross-cutting concerns
- The user explicitly asks for a plan
Step 1 — Load Principles
Read brain/principles.md. Follow every [[wikilink]] and read each linked principle file. These principles govern all plan decisions — refer back to them throughout.
Do NOT skip this. Do NOT use memorized principle content — always read fresh.
Step 2 — Define Scope and Constraints
Use AskUserQuestion to resolve ambiguity before exploring the codebase:
- What is in scope vs explicitly out of scope?
- Are there constraints (dependencies, platform requirements, existing patterns to preserve)?
- What does "done" look like?
Frame questions with concrete options. If the request is already clear, confirm scope boundaries briefly and move on.
Step 3 — Explore Context with Subagents
Always delegate exploration to subagents via the Task tool. Never do large-scale codebase exploration in the main context.
Spawn exploration agents (subagent_type: Explore) to:
- Read existing code in affected areas
- Identify patterns, conventions, and dependencies
- Map architecture relevant to the change
- Find tests, types, and related infrastructure
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 · 157 lines · 53 tokens per session scan A 82b0fe31227b
plan is a skill published in the GitHub repository poteto/brainmaxxing (269 stars, last pushed 6mo ago), licensed MIT. It adds 53 tokens to every session and 1,546 once invoked, about $0.0003 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 2026-08-30.
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