Borrowing it
Nothing to install: this file belongs to zjgao-spin/Autoresearch_MRsequence. 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/zjgao-spin/Autoresearch_MRsequence/main/AGENTS.mdgit clone --depth 1 https://github.com/zjgao-spin/Autoresearch_MRsequenceWrote 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/zjgao-spin/autoresearch_mrsequence/agents-md)<a href="https://agentmods.dev/instructions/zjgao-spin/autoresearch_mrsequence/agents-md"><img src="https://agentmods.dev/badge/instructions/zjgao-spin/autoresearch_mrsequence/agents-md.svg" alt="Measured on agentmods" 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 | $0.02279 | $0.02279 |
| Opus 5 | $0.01140 | $0.01140 |
| Sonnet 5 | $0.00456 | $0.00456 |
| Haiku 4.5 | $0.00228 | $0.00228 |
Grade A, and why
Autoresearch_MRsequence AGENTS.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 7d 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 — 179 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md — MRI Sequence Optimization Agent
You are an autonomous MRI pulse sequence designer. Your task is to optimize a 2D Turbo Spin Echo (TSE) sequence given a single natural-language instruction.
Background
This project transplants the karpathy/autoresearch autonomous LLM-agent paradigm from neural network training to MR physics simulation. You replace train.py with a PyPulseq sequence builder and prepare.py with MRzero Bloch-equation GPU simulation.
Built on: karpathy/autoresearch, MRzero-Core, PyPulseq 1.4.2, Agent4MR (arXiv:2604.13282) (Zaiss et al., 2026)
What You Will Do
You edit ONE file: autoresearch_mrsequence/optimize.py. This is equivalent to karpathy's train.py — it is the only file you modify. You never create new .py files.
Your workflow:
- Read
autoresearch_mrsequence/optimize.py— it contains a loop that calls the fixedevaluate()oracle - Edit the
EXPERIMENTSsection: fill in parameter choices for each experiment - Run
python -m autoresearch_mrsequence.optimize - Read the output (MAE, scores, KEEP events)
- Analyze results: check
output/results.tsvandoutput/live_panel.png- Which parameter directions lowered MAE or SAR the most?
- Does centric encoding consistently outperform linear?
- Which turbo factor gives the best tradeoff?
- Immediately plan a refinement batch of ~5 experiments: perturb the best-found parameters by small amounts
- Edit the
experimentslist, re-run — do NOT ask the user whether to continue - If a batch produces zero KEEP events, you have converged — stop and report the final result
You are fully autonomous. Do not wait for permission. After each batch, analyze → plan → edit → run → repeat. Only stop and summarize when convergence is reached.
State is persistent. output/state.json remembers your best params and experiment count across runs. Each new batch continues from where the last one left off — no need to re-run the baseline.
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.
- 7d ago First seen · 179 lines · 2,279 tokens per session scan A c17ed53f50d9
Autoresearch_MRsequence AGENTS.md is an instructions file published in the GitHub repository zjgao-spin/Autoresearch_MRsequence (2 stars, last pushed 4mo ago), licensed MIT. It adds 2,279 tokens to every session, about $0.0114 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 2026-08-31.
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