Academic research journal

Skill oghie/skillsets/skills/academic-research-journal

My personal collection of agent skills for software, security, product and research engineering. Runs on Claude (Claude Code), Codex, OpenCode, Antigravity (Gemini), and Hermes.

Install

npx -y skills add oghie/skillsets --skill academic-research-journal

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

5 stars5 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Use when evaluating, designing, drafting, revising, stress-testing, or preparing academic articles, empirical research reports, literature reviews, systematic reviews, meta-analyses, journal submissions, reviewer responses, research methods, sampling, measurement, analysis, publication readiness, or citation/source audits.

The file declares its own license as MIT. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

5.1 KB, as published. Nobody here has run it

Academic Research Journal

Core Rule

Evaluate and build academic work as a chain of claims: problem -> literature gap -> question -> design -> data -> measurement -> analysis -> interpretation -> contribution. Do not reward polish when the research logic is weak, and do not dismiss useful work merely because realistic constraints create limitations.

First Pass

Classify the task: evaluate, design, draft, revise, review, respond to reviewers, audit sources, or plan evidence synthesis.
Classify the article type: quantitative, qualitative, mixed methods, experimental, quasi-experimental, survey, field, case study, action research, policy/program evaluation, computational/big-data, literature review, systematic review, meta-analysis, theory, commentary, critique, or non-academic essay.
Extract the research skeleton: problem, purpose, question, theory, population/case, sample/data, measures, design, analysis, findings, limitations, contribution, and claim strength.
Identify the intended inference: exploratory insight, description, association, causality, mechanism, prediction, interpretation, policy/practice decision, or cumulative evidence.
Define the evidence needed and mark gaps as I/I = insufficient information; mark inapplicable criteria as N/A.

Required Reads By Task

Existing paper, essay, or article evaluation: tasks/evaluate-existing-work.md, references/research-quality-principles.md, and references/journal-readiness-scorecard.md.
Article type or methods classification: references/article-type-matrix.md.
Section-level critique: references/section-evaluation-matrix.md.
Sampling, measures, experiments, qualitative, quantitative, mixed methods, survey, or program evaluation: references/methods-evaluation-matrix.md.
Literature review, systematic review, or meta-analysis: tasks/evidence-synthesis.md and references/evidence-synthesis-and-review.md.
Drafting a manuscript, essay, thesis chapter, or journal article: tasks/manuscript-production.md.
Research design or empirical validation planning: tasks/design-research-project.md.
Reviewer simulation, revision strategy, or response letter: tasks/revision-and-peer-review.md.
Reference, citation, source, ethics, or integrity audit: tasks/source-and-reference-audit.md and references/integrity-and-source-audit.md.

Evaluation Protocol

Use the 1-5 scale unless the user requests another format: 5 strong; 4 mostly strong; 3 adequate but limited; 2 major weaknesses; 1 invalid or not journal-ready; N/A; I/I.

Always evaluate fit between the claim and the method:

Generalization requires a defensible population, sampling frame, recruitment logic, response/nonresponse handling, and subgroup size.
Causal language requires random assignment, credible quasi-experimental logic, temporal ordering, comparison conditions, and confound control.
Measurement claims require conceptual definitions, operational alignment, reliability, validity, and bias mitigation.
Qualitative claims require design fit, sampling rationale, recruitment detail, coding transparency, reflexivity, context, triangulation or other credibility checks, and analyzed evidence.
Mixed methods claims require explicit design, clear strand roles, integration, value added, and handling of contradictory findings.
Evidence-synthesis claims require transparent search, inclusion/exclusion criteria, study quality/bias assessment, heterogeneity handling, and interpretable synthesis.

Evidence And Verification

Never invent sources, quotations, statistics, DOI links, journal facts, methods, datasets, reviewer comments, or results.
Current facts such as journal scope, instructions, rankings, indexing, impact factor, citation counts, reporting standards, and software versions need live verification.
Treat statistical significance as limited evidence; check magnitude, practical importance, sampling assumptions, model choice, robustness, and replication.
Treat missing methods detail as an evaluation finding, not permission to infer.
Separate evidence, inference, speculation, and recommendation.

Script Helper

Run scripts/manuscript_static_audit.py <file> for a heuristic scan of section coverage, overclaiming, missing limitations/ethics cues, significance-language risks, and reference signals.

Output Standard

Lead with the highest-impact judgment. Name assumptions, article type, intended inference, strongest contribution, fatal or major weaknesses, concrete fixes, and residual uncertainty. Use scorecards and claim-audit tables for evaluations; use manuscript architecture and section builders for drafting.