BrainPilot is an open-source, human-supervised agent system for brain science that uses specialized agents, knowledge, skills, and tools to support research work. Researchers use it to review literature, plan studies, perform analyses, write reports, and check scientific claims. Its catalogue entries are skills and an MCP integration that extend or operate the BrainPilot research workflow.
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 NeuroAIHub/BrainPilot --skill audit-code-artifactgit clone --depth 1 https://github.com/NeuroAIHub/BrainPilotWrote 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/neuroaihub/brainpilot/audit-code-artifact)<a href="https://agentmods.dev/skills/neuroaihub/brainpilot/audit-code-artifact"><img src="https://agentmods.dev/badge/skills/neuroaihub/brainpilot/audit-code-artifact/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/neuroaihub/brainpilot/audit-code-artifact"><img src="https://agentmods.dev/badge/skills/neuroaihub/brainpilot/audit-code-artifact.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00044 | $0.00354 |
| Opus 5.5 | $0.00018 | $0.00142 |
| Sonnet 5.5 | $0.00009 | $0.00071 |
| Haiku 4.5 | $0.00004 | $0.00035 |
Grade A, and why
audit-code-artifact 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.
The source is not reproduced here
Licensed AGPL-3.0
The repository is licensed AGPL-3.0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 34 lines · 44 tokens per session scan A 94dee8564b79
audit-code-artifact is a skill published in the GitHub repository NeuroAIHub/BrainPilot (1,065 stars, last pushed 4d ago), licensed AGPL-3.0. It adds 44 tokens to every session and 354 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 2026-08-30.
Other skills, from other repositories
hypotheses
Turns a research direction into testable hypotheses with predictions, competing explanations and the experiments that discriminate between them, including the design (controls, randomization, blocking), sample size or seed count, the pre-specified analysis, and the result that would falsify each hypothesis. Use when a…
review-paper-code
Review research code for reproducibility and quality, extract the paper's main empirical claims, compare paper to code, and write a constructive markdown report. Designed for social science / economics projects with LaTeX papers and Stata, R, or Python code.
review-r
Read-only R code review protocol for .R scripts. Checks code quality, reproducibility, domain correctness, tidyverse idioms, and professional standards; produces a report without editing. Use when user says "review this R script", "check the R code", "audit the analysis code", "code review on the R", or when an R file…
paperlab_neqsim_api_claim_verification
Verify NeqSim API references in PaperLab books against Java source, tests, notebooks, and documented setup patterns. Use for code snippets, method names, constructor signatures, units, and API claims in book chapters.
ai-agents-research-methodology
How a hunch becomes an accepted result in this repo. Covers the evidence bar, hypothesis-predicts-numbers discipline, and the idea lifecycle from contradiction log through probe, eval baseline, ADR debate, calibrated gate, and post-ship monitoring. Use when you say how do I prove this idea, run the idea lifecycle…
workflows:review
Run multi-agent econometric review on estimation code, identification arguments, and research artifacts.