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 muhammad1438/academic-writer-skills --skill statistical-reportinggit clone --depth 1 https://github.com/muhammad1438/academic-writer-skillsWrote 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/muhammad1438/academic-writer-skills/statistical-reporting)<a href="https://agentmods.dev/skills/muhammad1438/academic-writer-skills/statistical-reporting"><img src="https://agentmods.dev/badge/skills/muhammad1438/academic-writer-skills/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/muhammad1438/academic-writer-skills/statistical-reporting"><img src="https://agentmods.dev/badge/skills/muhammad1438/academic-writer-skills/statistical-reporting.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 | $0.00000 | $0.04013 |
| Opus 5 | $0.00000 | $0.02006 |
| Sonnet 5 | $0.00000 | $0.00803 |
| Haiku 4.5 | $0.00000 | $0.00401 |
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 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 — 350 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Statistical Reporting
Statistical reporting translates numbers into verifiable, interpretable scientific communication. The goal is completeness: every reader must be able to evaluate the strength of evidence and reproduce the analysis from your description alone.
Choosing a Reporting Style
| Discipline | Primary Style | Key Standard |
|---|---|---|
| Psychology, Education | APA 7th | Publication Manual Chapter 7 |
| Medicine, Clinical | APA 7th or Vancouver | CONSORT, STROBE |
| Biology, Ecology | APA or CSE | ARRIVE (animal), STROBE (observational) |
| Epidemiology, Public Health | STROBE / APA hybrid | Confidence intervals mandatory |
| Systematic reviews / Meta-analyses | APA + PRISMA | PRISMA 2020 |
| Engineering, Computer Science | IEEE | Varies by journal |
Rule: APA 7th is the default for behavioral and social sciences. Always check the target journal's author guidelines — many override style with their own statistical reporting requirements.
Exact Format Templates (APA 7th)
t-Test
Independent samples:
t(df) = X.XX, p = .XXX, d = X.XX, 95% CI [X.XX, X.XX]
Example: t(58) = 2.34, p = .023, d = 0.61, 95% CI [0.09, 1.12]
Paired samples / dependent:
t(df) = X.XX, p = .XXX, d = X.XX
Example: t(29) = −3.12, p = .004, d = −0.57
One-sample:
t(df) = X.XX, p = .XXX, d = X.XX (comparison to μ = XX)
Formatting rules:
- Italicise the statistic letter: t, F, r, p, M, SD
- Report exact p values (not p < .05), except when p < .001 — then write p < .001
- Use a leading zero before decimals only when the value can exceed 1: M = 3.45, but p = .034 (not p = 0.034)
- Use two decimal places for most statistics; three for p values and correlations
ANOVA (One-Way and Factorial)
One-way:
F(df_between, df_within) = X.XX, p = .XXX, η² = .XX
Example: F(2, 87) = 7.43, p = .001, η² = .15
Factorial:
Main effect of A: F(df_A, df_error) = X.XX, p = .XXX, ηₚ² = .XX
Main effect of B: F(df_B, df_error) = X.XX, p = .XXX, ηₚ² = .XX
A × B interaction: F(df_AB, df_error) = X.XX, p = .XXX, ηₚ² = .XX
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 · 350 lines · 0 tokens per session scan A d0fa45f6984a
statistical-reporting is a skill published in the GitHub repository muhammad1438/academic-writer-skills (6 stars, last pushed 5mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 4,013 tokens. 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
thesis-control
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manuscript-reframe
Reframe report-like academic drafts into paper-form scientific arguments while preserving or explicitly renegotiating author intent; requires an approved old-versus-proposed spine, evidence and argument baselines, analysis-role control, and post-edit drift review.
argument-governance
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audit
Check thesis chapters for consistency before submission — contradictory numbers, terminology drift, and broken cross-references.
release-governance
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self-review
Review the user's own manuscript, paper, thesis chapter, rebuttal, or release packet with clean-room anti-contamination controls and, when needed, an unfamiliar-reader comprehension gate. Use for internal review, readiness checks, reviewer simulation, or claim-evidence self-audit where prior chat memory and unstated…