AutoResearchClaw is a system that turns a research idea into a scientific paper through autonomous and collaborative AI research workflows. It is for researchers who want agents to investigate questions, run experiments, and produce papers, with optional human guidance. Catalogue skills and agents provide parts of its research workflow.
Borrowing it
Nothing to install: this file belongs to aiming-lab/AutoResearchClaw. 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/aiming-lab/AutoResearchClaw/main/.claude/skills/statistical-reporting/SKILL.mdgit clone --depth 1 https://github.com/aiming-lab/AutoResearchClawWrote 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/aiming-lab/autoresearchclaw/statistical-reporting)<a href="https://agentmods.dev/skills/aiming-lab/autoresearchclaw/statistical-reporting"><img src="https://agentmods.dev/badge/skills/aiming-lab/autoresearchclaw/statistical-reporting/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/aiming-lab/autoresearchclaw/statistical-reporting"><img src="https://agentmods.dev/badge/skills/aiming-lab/autoresearchclaw/statistical-reporting.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00029 | $0.00835 |
| Opus 5.5 | $0.00012 | $0.00334 |
| Sonnet 5.5 | $0.00006 | $0.00167 |
| Haiku 4.5 | $0.00003 | $0.00084 |
Grade A, and why
statistical-reporting 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 2mo 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 — 59 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Statistical Reporting Best Practice
Test Selection Quick Reference
- Comparing two groups (independent, normal): Independent t-test
- Comparing two groups (independent, non-normal): Mann-Whitney U test
- Comparing two groups (paired, normal): Paired t-test
- Comparing two groups (paired, non-normal): Wilcoxon signed-rank test
- Comparing 3+ groups (independent, normal): One-way ANOVA + post-hoc
- Comparing 3+ groups (non-normal): Kruskal-Wallis test
- Relationship between continuous variables: Pearson or Spearman correlation
- Categorical outcomes: Chi-square or Fisher's exact test
- Predicting continuous outcome: Linear regression
- Predicting binary outcome: Logistic regression
Assumption Checking
- Normality: Shapiro-Wilk test (n < 50) or visual Q-Q plots
- Homogeneity of variance: Levene's test before t-tests and ANOVA
- Independence: Verify study design ensures independent observations
- Linearity: Scatter plots and residual plots for regression
- Multicollinearity: VIF < 5 for multiple regression predictors
- When assumptions are violated, use non-parametric alternatives or robust methods
APA Reporting Format
- t-test: t(df) = X.XX, p = .XXX, d = X.XX
- ANOVA: F(df_between, df_within) = X.XX, p = .XXX, eta-squared = .XX
- Correlation: r(df) = .XX, p = .XXX [95% CI: .XX, .XX]
- Chi-square: chi-square(df, N = XXX) = X.XX, p = .XXX
- Regression: beta = X.XX, SE = X.XX, t = X.XX, p = .XXX
- Always report exact p-values (not "p < .05") unless p < .001
- Use leading zero for values that can exceed 1 (e.g., t = 0.50) but not for those bounded by 1 (e.g., p = .032, r = .45)
Effect Sizes
- ALWAYS report effect sizes alongside p-values
- Cohen's d for group comparisons: small = 0.2, medium = 0.5, large = 0.8
- Eta-squared for ANOVA: small = .01, medium = .06, large = .14
- R-squared for regression: report adjusted R-squared for multiple predictors
- Odds ratios for logistic regression with 95% confidence intervals
- Distinguish statistical significance from practical significance
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.
- 2mo ago First seen · 59 lines · 29 tokens per session scan A 95e9d1bfbcc2
statistical-reporting is a skill published in the GitHub repository aiming-lab/AutoResearchClaw (14,822 stars, last pushed 2mo ago), licensed MIT. It adds 29 tokens to every session and 835 once invoked, about $0.0001 per session on Opus 5.5. 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.
statistical-reporting compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| statistical-reportingaiming-lab/AutoResearchClaw | Skill | 15k | 2mo ago | MIT | A | 29 | Claude Code |
| aclawdemyLeoYeAI/openclaw-master-skills | Skill | 2.2k | 5d ago | MIT | A | 30 | Claude Code, Codex |
| adme-property-predictorLeoYeAI/openclaw-master-skills | Skill | 2.2k | 5d ago | MIT | A | 61 | Claude Code |
| arxiv-summarizer-orchestratorInternLM/WildClawBench | Skill | 534 | yesterday | MIT | A | 75 | Codex |
| nerf-to-3dgs-migratorjaccen/Awesome-Gaussian-Skills | Skill | 178 | today | Apache-2.0 | A | 106 | Claude Code |
| baseline-comparison-auditwanshuiyin/Anti-Autoresearch | Skill | 179 | 7d ago | MIT | A | 310 | Claude Code |
| eval-design-forensicswanshuiyin/Anti-Autoresearch | Skill | 179 | 7d ago | MIT | A | 458 | Claude Code |