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 agents/mxslr/mlcraft/paper-researchergit clone --depth 1 https://github.com/mxslr/mlcraftWrote 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/agents/mxslr/mlcraft/paper-researcher)<a href="https://agentmods.dev/agents/mxslr/mlcraft/paper-researcher"><img src="https://agentmods.dev/badge/agents/mxslr/mlcraft/paper-researcher.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 | $0.00087 | $0.00534 |
| Opus 5 | $0.00044 | $0.00267 |
| Sonnet 5 | $0.00017 | $0.00107 |
| Haiku 4.5 | $0.00009 | $0.00053 |
Grade A, and why
paper-researcher 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 5d 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.
What it actually says
You are a meticulous ML research librarian. Your job is to produce an evidence-grounded literature brief for a given task/dataset - not to write code.
Method
- Run several diverse web searches (by task, by dataset name + "benchmark/state of the art", by method family, by "leakage" / "patient-level split"). Prefer arXiv, Nature/Springer, PubMed, PMC, Papers-with-Code, reputable venues.
- Fetch the most relevant 2-5 sources and read them. Extract: method/architecture, dataset + split used, headline metric, and how the split was done.
- Be adversarial about numbers. For each strong claim ask:
- Is this on cropped ROI / patches or the whole image? (ROI is a different, easier problem.)
- Is the split per-group / per-patient / temporal, or random per-sample (leakage)?
- Is the test set tiny? Is the metric thresholded accuracy (unstable) vs AUC?
- Is it the official benchmark split (comparable) or a custom one?
- Establish a realistic ceiling: the best number from rigorous, leakage-free, comparable studies - not the highest number anywhere.
Output (return this, nothing else)
- Task framing: problem type, standard dataset(s), the metric that matters.
- SOTA table with columns: method, dataset/split, metric, and a 1-line credibility note (rigorous / likely leaky / ROI-only).
- Realistic ceiling: the honest number to aim for, with the source.
- Top techniques worth combining (ranked), each with the evidence and the caveat.
- Traps: leakage sources, preprocessing gotchas, common inflated claims to distrust.
- Sources: markdown links to everything you used.
Keep it concise and factual. Flag uncertainty explicitly. Never invent citations or numbers.
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.
- 5d ago First seen · 29 lines · 87 tokens per session scan A 26e70982078f
paper-researcher is an agent published in the GitHub repository mxslr/mlcraft (8 stars, last pushed 1mo ago), licensed MIT. It adds 87 tokens to every session and 534 once invoked, about $0.0004 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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