Awesome Journal Skills is a collection of agent skill packs tailored to hundreds of academic journals across fields including economics, social science, medicine, science, and engineering. Researchers use the packs for tasks such as choosing topics, designing empirical strategies, preparing tables and figures, submitting papers, and responding to reviewers. The catalogue entries are the project's journal-specific skills and related plugins.
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 brycewang-stanford/Awesome-Journal-Skills --skill amj-data-analysisgit clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-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/brycewang-stanford/awesome-journal-skills/amj-data-analysis)<a href="https://agentmods.dev/skills/brycewang-stanford/awesome-journal-skills/amj-data-analysis"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/amj-data-analysis/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/brycewang-stanford/awesome-journal-skills/amj-data-analysis"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/amj-data-analysis.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 | $0.00081 | $0.01474 |
| Opus 5.5 | $0.00032 | $0.00590 |
| Sonnet 5.5 | $0.00016 | $0.00295 |
| Haiku 4.5 | $0.00008 | $0.00147 |
Grade A, and why
amj-data-analysis 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 1mo 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 — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Analysis & Validity (amj-data-analysis)
When to trigger
- Data are collected and it is time to estimate and report
- You are unsure whether your estimator matches your design (nested data, latent constructs, panel)
- Reviewers will probe measurement validity, common-method bias, or endogeneity
- Interaction/mediation effects need correct testing and reporting
- A reviewer says "the analysis does not support the inference" or "validity is not established"
Establish measurement before estimation
AMJ reviewers expect the measurement model to be defended first:
- Reliability: Cronbach's alpha and/or composite reliability for each multi-item scale.
- Confirmatory factor analysis (CFA): report fit (e.g., CFI, TLI, RMSEA, SRMR) and show the hypothesized factor structure fits better than plausible alternatives (one-factor, combined-factor models).
- Convergent & discriminant validity: AVE per construct; AVE > inter-construct squared correlations (or HTMT). Report the correlation matrix with reliabilities on the diagonal.
- Aggregation (multilevel): justify with ICC(1), ICC(2), and r_wg(j) before aggregating to a higher level.
- Qualitative analysis: where the design is qualitative, "validity" becomes trustworthiness — present a Gioia-style data structure (first-order codes → second-order themes → aggregate dimensions), an audit trail, and representative quotations so the path from raw data to constructs is traceable.
Choose the estimator that matches the design
| Data structure / claim | Estimator |
|---|---|
| Latent constructs, mediation, full model | Structural equation modeling (SEM) |
| Nested data (indiv. in teams/firms) | Multilevel / hierarchical linear modeling (HLM) |
| Panel with unit heterogeneity | Fixed/random effects; cluster-robust SE |
| Manipulated cause | ANOVA/regression with manipulation & attention checks |
| Endogenous archival regressor | 2SLS/IV, DiD, Heckman, propensity matching (per design) |
| Count/limited dependent variable | Poisson/negative binomial, logit/probit, Tobit as fits |
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.
- 1mo ago First seen · 104 lines · 81 tokens per session scan A 7153d476fabb
amj-data-analysis is a skill published in the GitHub repository brycewang-stanford/Awesome-Journal-Skills (1,259 stars, last pushed 19d ago), licensed MIT. It adds 81 tokens to every session and 1,474 once invoked, about $0.0003 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 2026-09-15.
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