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/learningmatter-mit/atomisticskills/general-peer-reviewnpx skills add learningmatter-mit/AtomisticSkills --skill general-peer-reviewgit clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkillsWrote 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/learningmatter-mit/atomisticskills/general-peer-review)<a href="https://agentmods.dev/skills/learningmatter-mit/atomisticskills/general-peer-review"><img src="https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/general-peer-review.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 | $0.00033 | $0.01030 |
| Opus 5 | $0.00016 | $0.00515 |
| Sonnet 5 | $0.00007 | $0.00206 |
| Haiku 4.5 | $0.00003 | $0.00103 |
Grade A, and why
general-peer-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 4d 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 — 78 lines — stays where its author put it; the contents beside it link to each section on GitHub.
General Peer Review
Goal
To rigorously evaluate a research plan, manuscript, or simulation workflow prior to execution or publication. This skill acts as an adversarial reviewer, ensuring scientific rigor by identifying methodological gaps, demanding adequate statistical sampling, highlighting weak assumptions, and suggesting necessary baseline comparisons.
Prerequisites
- A completed or drafted piece of scientific writing (e.g.,
research_plan.md, manuscript draft, or experimental summary). - Sufficient contextual knowledge regarding the specific simulation or machine-learning methodology being proposed.
Instructions
-
Review Initialization The agent initializes the review process by loading the target document into memory. This can be done by standard reading tools like
view_file. -
Literature-Based Validation The agent utilizes skills like
general-query-literature-databaseorgeneral-deep-researchto ground the review in established scientific facts.- Perform a literature search regarding the specific materials, methodologies, or baseline properties stated in the text.
- Point out discrepancies between the proposed approach and standard practices found in high-impact journals.
-
Methodological & Reproducibility Critique The agent systematically analyzes the methodology for common theoretical and computational pitfalls:
- Ensemble & Sampling: Verify if MD simulations are long enough to reach equilibration and if the number of samples is statistically significant.
- Level of Theory: Question if the chosen MLIP or DFT functional is adequate for the specific property being computed (e.g., PBE vs. r2SCAN, dispersion corrections for molecular systems).
- System Size: Check if the supercell size is large enough to avoid finite-size effects and self-interaction (e.g., in defect or dopant studies).
- Hyperparameters: Ensure critical hyperparameter choices (e.g., $k$-point grid density, energy cutoffs, learning rates) are justified.
- Reproducibility: Are all protocols, scripts, and model checkpoints adequately specified to allow independent reproduction? Have data availability standards been met?
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.
- 4d ago First seen · 78 lines · 33 tokens per session scan A 8f1546feaa5c
general-peer-review is a skill published in the GitHub repository learningmatter-mit/AtomisticSkills (158 stars, last pushed today), licensed MIT. It adds 33 tokens to every session and 1,030 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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