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Agent skill

Statistical Reporting

by aiming-lab in aiming-lab/AutoResearchClaw

Statistical test selection, assumption checking, and APA-formatted reporting.

MITAuto-check passedData & Analytics

Install Statistical Reporting

skills CLI
$ npx skills add aiming-lab/AutoResearchClaw --skill statistical-reporting -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install aiming-lab/AutoResearchClaw statistical-reporting --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/aiming-lab/AutoResearchClaw.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/statistical-reporting .claude/skills/statistical-reporting && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
statistical-reporting
GitHub stars
15k
Token cost
~756 tokens
SKILL.md length
345 words
Files
1
Skills in repo
34
Repo updated
First seen
Licence
MIT

At a glance

Statistical test selection, assumption checking, and APA-formatted reporting.

  • Works in 10 steps: Comparing two groups (independent,… → Comparing two groups (independent,… → Comparing two groups (paired, normal):… → …
  • Analyzing experimental results
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
  • Writing results sections

What it does

Statistical Reporting is an agent skill from aiming-lab/AutoResearchClaw. Statistical test selection, assumption checking, and APA-formatted reporting. Use when analyzing experimental results or writing results sections.

Its SKILL.md is about 760 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Data & Analytics, covering Statistics. The repository describes itself as: Fully autonomous & self-evolving research from idea to paper. Chat an Idea. Get a Paper. 🦞. The licence is MIT.

When your agent uses it

  • Analyzing experimental results
  • Writing results sections

Example prompts

  • “/statistical-reporting”

Workflow steps

10 steps, taken from the first numbered list in SKILL.md.

  1. Comparing two groups (independent, normal): Independent t-test
  2. Comparing two groups (independent, non-normal): Mann-Whitney U test
  3. Comparing two groups (paired, normal): Paired t-test
  4. Comparing two groups (paired, non-normal): Wilcoxon signed-rank test
  5. Comparing 3+ groups (independent, normal): One-way ANOVA + post-hoc
  6. Comparing 3+ groups (non-normal): Kruskal-Wallis test
  7. Relationship between continuous variables: Pearson or Spearman correlation
  8. Categorical outcomes: Chi-square or Fisher's exact test
  9. Predicting continuous outcome: Linear regression
  10. Predicting binary outcome: Logistic regression

What it can do on your machine

Read from SKILL.md and the folder at commit be4ba47. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Statistical Reporting loads about 756 tokens when it runs. Until then it costs about 42 tokens; SKILL.md has 345 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~42
When it runs · the whole SKILL.md, loaded when a task matches
~756

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from aiming-lab/AutoResearchClaw at commit be4ba47, republished under its MIT licence (© aiming-lab). 345 words, ~756 tokens.

Download SKILL.mdSave it as .claude/skills/statistical-reporting/SKILL.md (or your agent's skills folder).
name
statistical-reporting
description
Statistical test selection, assumption checking, and APA-formatted reporting. Use when analyzing experimental results or writing results sections.
metadata.category
writing
metadata.trigger-keywords
statistic,hypothesis test,p-value,regression,ANOVA,t-test,effect size,confidence interval
metadata.applicable-stages
14,17
metadata.priority
3
metadata.version
1.0
metadata.author
researchclaw
metadata.references
adapted from K-Dense-AI/claude-scientific-skills

Statistical Reporting Best Practice

Test Selection Quick Reference
  1. Comparing two groups (independent, normal): Independent t-test
  2. Comparing two groups (independent, non-normal): Mann-Whitney U test
  3. Comparing two groups (paired, normal): Paired t-test
  4. Comparing two groups (paired, non-normal): Wilcoxon signed-rank test
  5. Comparing 3+ groups (independent, normal): One-way ANOVA + post-hoc
  6. Comparing 3+ groups (non-normal): Kruskal-Wallis test
  7. Relationship between continuous variables: Pearson or Spearman correlation
  8. Categorical outcomes: Chi-square or Fisher's exact test
  9. Predicting continuous outcome: Linear regression
  10. Predicting binary outcome: Logistic regression
Assumption Checking
  1. Normality: Shapiro-Wilk test (n < 50) or visual Q-Q plots
  2. Homogeneity of variance: Levene's test before t-tests and ANOVA
  3. Independence: Verify study design ensures independent observations
  4. Linearity: Scatter plots and residual plots for regression
  5. Multicollinearity: VIF < 5 for multiple regression predictors
  6. When assumptions are violated, use non-parametric alternatives or robust methods
APA Reporting Format
  1. t-test: t(df) = X.XX, p = .XXX, d = X.XX
  2. ANOVA: F(df_between, df_within) = X.XX, p = .XXX, eta-squared = .XX
  3. Correlation: r(df) = .XX, p = .XXX [95% CI: .XX, .XX]
  4. Chi-square: chi-square(df, N = XXX) = X.XX, p = .XXX
  5. Regression: beta = X.XX, SE = X.XX, t = X.XX, p = .XXX
  6. Always report exact p-values (not "p < .05") unless p < .001
  7. 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
  1. ALWAYS report effect sizes alongside p-values
  2. Cohen's d for group comparisons: small = 0.2, medium = 0.5, large = 0.8
  3. Eta-squared for ANOVA: small = .01, medium = .06, large = .14
  4. R-squared for regression: report adjusted R-squared for multiple predictors
  5. Odds ratios for logistic regression with 95% confidence intervals
  6. Distinguish statistical significance from practical significance
Common Mistakes to Avoid
  1. Never say "the results were not significant, therefore there is no effect"
  2. Do not confuse correlation with causation in observational data
  3. Apply multiple comparison corrections (Bonferroni, FDR) when running many tests
  4. Report confidence intervals, not just point estimates
  5. State whether tests are one-tailed or two-tailed and justify the choice

© aiming-lab, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .claude/skills/statistical-reporting of aiming-lab/AutoResearchClaw.

Open the folder on GitHubat commit be4ba47

Compare with similar skills

Statistical Reporting next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Statistical Reporting compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Statistical Reporting this skillaiming-lab/AutoResearchClaw15k—~756Automated safety check: PassMIT
Statistical Analysisspacering-net/codeg3.8k3 repos~5kAutomated safety check: PassMIT
StatsmodelszLanqing/codex-claude-academic-skills4.6k16 repos~4.9kAutomated safety check: PassBSD-3-Clause
AI Daily DigestvigorX777/ai-daily-digest1.6k—~1.3kAutomated safety check: PassNone
Statistical Powerspacering-net/codeg3.8k1 repos~3.6kAutomated safety check: NotesMIT
Agent Session Monitorhigress-group/higress9.5k—~3.3kAutomated safety check: PassApache-2.0

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Categories

Questions about Statistical Reporting

What does Statistical Reporting do?

Statistical test selection, assumption checking, and APA-formatted reporting. Statistical Reporting is an agent skill from aiming-lab/AutoResearchClaw. Statistical test selection, assumption checking, and APA-formatted reporting.

When should I use Statistical Reporting?

Statistical Reporting fits situations like: analyzing experimental results; writing results sections.

How do I install Statistical Reporting in Claude Code?

Run `npx skills add aiming-lab/AutoResearchClaw --skill statistical-reporting -a claude-code`. Or copy the skill folder (.claude/skills/statistical-reporting in aiming-lab/AutoResearchClaw) into .claude/skills/statistical-reporting in your project. Claude Code loads it when a task matches its description.

How do I install Statistical Reporting in Codex?

Run `npx skills add aiming-lab/AutoResearchClaw --skill statistical-reporting -a codex`. Or copy the skill folder (.claude/skills/statistical-reporting in aiming-lab/AutoResearchClaw) into .agents/skills/statistical-reporting in your project. Codex loads it when a task matches its description.

Can I use Statistical Reporting in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add aiming-lab/AutoResearchClaw --skill statistical-reporting -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/statistical-reporting, .gemini/skills/statistical-reporting, .github/skills/statistical-reporting and .opencode/skills/statistical-reporting in your project.

What does Statistical Reporting need to run?

SKILL.md names no scripts, command-line tools or credentials: Statistical Reporting is instructions for the agent only.

Does Statistical Reporting access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Statistical Reporting safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Statistical Reporting use?

Statistical Reporting is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Statistical Reporting use?

About 756 tokens (SKILL.md is roughly 3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Statistical Reporting?

Skills that share tags, products or a category with Statistical Reporting: Statistical Analysis (spacering-net/codeg, 3.8k stars), Statsmodels (zLanqing/codex-claude-academic-skills, 4.6k stars), AI Daily Digest (vigorX777/ai-daily-digest, 1.6k stars) and Statistical Power (spacering-net/codeg, 3.8k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Statistical Reporting?

aiming-lab (a GitHub organization) maintains it in aiming-lab/AutoResearchClaw, which has 14,587 GitHub stars. The repository holds 34 skills in this directory. The repository was last updated on August 19, 2026.

Source: aiming-lab/AutoResearchClaw on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.