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/reviewnpx skills add poteto/brainmaxxing --skill reviewgit 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 | $0.00046 | $0.01186 |
| Opus 5 | $0.00023 | $0.00593 |
| Sonnet 5 | $0.00009 | $0.00237 |
| Haiku 4.5 | $0.00005 | $0.00119 |
Grade A, and why
review 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 — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review
Thorough review grounded in project principles. Do NOT make changes — the review is the deliverable.
Use Tasks to track progress. Create a task for each step below (TaskCreate), mark each in_progress when starting and completed when done (TaskUpdate). Check TaskList after each step.
Step 1 — Load Principles
Read brain/principles.md. Follow every [[wikilink]] and read each linked principle file. These principles govern review judgments — refer back to them when evaluating issues.
Do NOT skip this. Do NOT use memorized principle content — always read fresh.
Step 2 — Determine Scope
Infer what to review from context — the user's message, recent diffs, or referenced plans/PRs. If genuinely ambiguous (nothing to infer), ask.
Auto-detect review mode from change size:
- BIG CHANGE (50+ lines changed, 3+ files, or new architecture) — all sections, at most 4 top issues per section
- SMALL CHANGE (under those thresholds) — one issue per section
Step 3 — Gather Context
For SMALL CHANGE reviews, read files directly in the main context — delegation overhead exceeds the cost of reading a few files.
For BIG CHANGE reviews, delegate exploration to subagents via the Task tool.
Spawn exploration agents (subagent_type: Explore) to:
- Read the code or plan under review
- Identify dependencies, callers, and downstream effects
- Map relevant types, tests, and infrastructure
Run multiple agents in parallel when investigating independent areas.
Step 4 — Gather Domain Skills
Check installed skills (.agents/skills/, .claude/skills/) for any that match the review's domain.
Invoke matched skills now — read their output and use domain guidance to inform your review.
For domains not covered by installed skills, use find-skills to search for a relevant skill.
Step 5 — Assessment Pipeline
Work through all sections in order. For each section, check against loaded principles.
1. Scope Check
If the review targets work against a plan phase:
- Read the plan phase that was assigned.
- Run
git diff --statandgit log --onelinefor the relevant commits. - Flag files changed outside the plan phase's stated scope as scope violations.
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 · 128 lines · 46 tokens per session scan A 657a97fae3ab
review is a skill published in the GitHub repository poteto/brainmaxxing (273 stars, last pushed 6mo ago), licensed MIT. It adds 46 tokens to every session and 1,186 once invoked, about $0.0002 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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