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 mxslr/mlcraft --skill literature-reviewgit 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/skills/mxslr/mlcraft/literature-review)<a href="https://agentmods.dev/skills/mxslr/mlcraft/literature-review"><img src="https://agentmods.dev/badge/skills/mxslr/mlcraft/literature-review/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/mxslr/mlcraft/literature-review"><img src="https://agentmods.dev/badge/skills/mxslr/mlcraft/literature-review.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00089 | $0.00470 |
| Opus 5 | $0.00044 | $0.00235 |
| Sonnet 5 | $0.00018 | $0.00094 |
| Haiku 4.5 | $0.00009 | $0.00047 |
Grade A, and why
literature-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 10d 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
Literature Review (research-first, skeptical)
Never pick a method from memory or hype. Ground it in evidence.
Do this
- Delegate breadth to the
paper-researcheragent when available; otherwise run diverseWebSearchyourself (by task, bydataset + "state of the art", by method family, by"patient-level" / "leakage"). - Read the top 2-5 sources. For each, record: method, dataset + split, headline metric, how the split was done.
Appraise every number adversarially
A high number is a hypothesis to disprove, not a target:
- Whole-input vs ROI/patch? ROI-crop (lesion already cropped) is a different, easier problem than whole-image. Numbers are not comparable.
- Split integrity? A random per-sample split with multiple samples per subject causes leakage and inflated scores. Demand group/patient/temporal or the official split.
- Test size & metric? A tiny test set or thresholded accuracy is unstable. Prefer AUC / mAP / F1 with confidence intervals.
- Comparable? Only the official benchmark split can be compared across papers.
Output an evidence brief
- SOTA table with columns: method, dataset/split, metric, credibility note.
- Realistic ceiling: best rigorous, leakage-free, comparable number - the honest target.
- Ranked techniques worth combining, each with evidence + caveat.
- Traps to avoid.
- Sources as markdown links.
Red flags = distrust, don't chase
99%+ on a hard whole-image medical/vision benchmark almost always means ROI-only or leakage. Say so plainly. The professional move is knowing which numbers are fake.
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.
- 10d ago First seen · 30 lines · 89 tokens per session scan A aacd337a568c
literature-review is a skill published in the GitHub repository mxslr/mlcraft (8 stars, last pushed 2mo ago), licensed MIT. It adds 89 tokens to every session and 470 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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