Borrowing it
Nothing to install: this file belongs to FilippoScaramuzza/agentic-reviewer. 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/FilippoScaramuzza/agentic-reviewer/main/.cursor/skills/review-statistics/SKILL.mdgit clone --depth 1 https://github.com/FilippoScaramuzza/agentic-reviewerWrote 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/filipposcaramuzza/agentic-reviewer/review-statistics)<a href="https://agentmods.dev/skills/filipposcaramuzza/agentic-reviewer/review-statistics"><img src="https://agentmods.dev/badge/skills/filipposcaramuzza/agentic-reviewer/review-statistics/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/filipposcaramuzza/agentic-reviewer/review-statistics"><img src="https://agentmods.dev/badge/skills/filipposcaramuzza/agentic-reviewer/review-statistics.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 | $0.00044 | $0.00610 |
| Opus 5 | $0.00022 | $0.00305 |
| Sonnet 5 | $0.00009 | $0.00122 |
| Haiku 4.5 | $0.00004 | $0.00061 |
Grade A, and why
review-statistics 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.
How it starts
The opening of the file, as written. The whole thing — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review Statistics
Evaluate the statistical rigor and data presentation quality of an academic paper.
Prerequisites
Before starting, read:
- The paper file
context/<journal-slug>/scope-and-criteria.mdif the journal has specific statistical requirements
Review Checklist
Study Design & Power
- Was an a priori power analysis conducted?
- Is the sample size adequate for the claimed effects?
- Were there multiple comparisons that required correction?
Statistical Methods
- Are the statistical tests appropriate for the data type and research question?
- Are assumptions of the tests checked and reported (normality, homoscedasticity, independence)?
- Are non-parametric alternatives used when assumptions are violated?
- Are model fit indices reported (for regression/SEM models)?
- Is the software/package used for analysis stated?
Effect Sizes & Confidence Intervals
- Are effect sizes reported (not just p-values)?
- Are confidence intervals provided?
- Is practical significance discussed, not just statistical significance?
Multiple Comparisons
- Are multiple comparison corrections applied where needed (Bonferroni, FDR, etc.)?
- Is the family-wise error rate controlled?
- Are pre-registered analyses distinguished from post-hoc analyses?
Data Presentation
- Are results clearly presented in tables and figures?
- Is there redundancy between text, tables, and figures?
- Are standard deviations, not just means, reported?
- Are exact p-values reported rather than just significance thresholds?
Reproducibility
- Is analysis code available or described in sufficient detail?
- Are data availability statements included?
- Could the analysis be replicated from the information provided?
Red Flags
- p-hacking indicators (optional stopping, selective reporting)
- HARKing (Hypothesizing After Results are Known)
- Data dredging or fishing expeditions
- Selective reporting of analyses
- Overinterpretation of marginal results
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 · 70 lines · 44 tokens per session scan A a87576c9941c
review-statistics is a skill published in the GitHub repository FilippoScaramuzza/agentic-reviewer (2 stars, last pushed 2mo ago), licensed MIT. It adds 44 tokens to every session and 610 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 2026-08-31.
Other skills, from other repositories
search-math-results
Find program-conditioned math results, constructions, examples, counterexamples, analogies, and background references. Use when the current active program needs repair, mutation, analogy, a program shift, or carefully gated obstruction search.
check-referenced-statements
Validate externally referenced theorems by querying arXiv theorem search first and Codex's built-in web search second. Use when a markdown proof cites statements from external papers.
verify-sequential-statements
Verify a markdown proof in the order it is written. Use when the task is to check local correctness, theorem applicability, and reasoning gaps statement by statement through a paper-style proof.
construct-counterexamples
Construct candidate counterexamples to test a proposed conjecture, lemma, or intermediate claim by keeping the assumptions true while making the claimed conclusion fail. Use when a proposed conjecture/claim feels fragile or unproved, or when you are stuck in reasoning and want to see where the assumptions take effect…
construct-toy-examples
Generate and analyze simpler examples that satisfy both the assumptions and the conclusion of a theorem statement or subgoal. Use when you are stuck in reasoning and need simpler examples to regain traction, or when you want to see where the assumptions take effect and gain intuition.
obtain-immediate-conclusions
Derive immediate mathematical consequences from a theorem statement or subgoal. Use when starting a new problem, branch, or subgoal, or when cheap progress or a cleaner reformulation is needed before deeper proof search.