alterlab-results-transparency
From the author
Enforces results-reporting transparency as a discipline gate built on the Iron Law "NO RESULTS CLAIM WITHOUT REPORTING EVERY ANALYSIS RUN" — a numbered Gate Function (IDENTIFY the claim, LIST every test actually run including the ones that did not "work", CHECK assumptions were reported, CHECK effect size with 95% CI is present, CHECK pre-registration deviations are disclosed, ONLY THEN write the sentence), plus an Excuse-vs-Reality table and Red-Flags-STOP list for selective reporting, cherry-picking, and bare p-values. Use when writing up Results, claiming a finding from a subset of analyses, reporting a p-value without an effect size or confidence interval, dropping outliers post hoc, or omitting analyses that did not pan out. Orchestrates alterlab-statistical-analysis (tests, effect sizes), alterlab-preregistration-discipline (the frozen plan), and alterlab-open-science (TOP, disclosure); it does not run the tests itself. Part of the AlterLab Academic Skills suite.
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npx skills add AlterLab-IEU/AlterLab-Academic-Skills --skill alterlab-results-transparency -a claude-code
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Similar skills
Closest to this one by what they do and when they are used.
- alterlab-preregistration-disciplineAlterLab-IEU/AlterLab-Academic-SkillsEnforces pre-registration discipline with the Iron Law NO DATA ANALYSIS WITHOUT A PRE-REGISTERED ANALYSIS PLAN FIRST, a spirit-vs-letter line, an Excuse-vs-Reality rationalization table, and a Red-Flags-STOP list (HARKing, optional stopping, post-hoc covariates, outlier-dropping, test-shopping). Runs a PLAN/COLLECT/CONFIRM/EXPLORE workflow that freezes hypotheses, tests, exclusions, and stopping rules before data, then forces unplanned findings to be labeled exploratory (their p-values lose confirmatory status, per COS confirmatory/exploratory model). Orchestrates, not replaces, alterlab-open-science (OSF/AsPredicted registration), alterlab-statistical-analysis (test selection, assumptions), and alterlab-scientific-thinking (bias grading). Use when analyzing data without a frozen plan, switching the primary outcome or adding covariates after seeing results, weighing early stopping, dropping outliers post-hoc, pre-registering a study, or rationalizing deviation. Part of the AlterLab Academic Skills suite.Testing★ 78
- alterlab-test-selection-guardAlterLab-IEU/AlterLab-Academic-SkillsEnforces statistical-test selection as a discipline, holding the Iron Law NO TEST CHOSEN AFTER SEEING THE P-VALUE: routes the choice through a fixed decision tree (outcome type -> groups -> paired? -> normality) terminating in named tests (t-test, Mann-Whitney U, ANOVA, Kruskal-Wallis, Wilcoxon, Friedman, chi-square, Pearson/Spearman, regression), gates interpretation behind a mandatory Shapiro-Wilk/Levene/linearity assumption check, blocks test-shopping with an Excuse-vs-Reality table and Red-Flags-STOP list, and applies a 3+-tests escalation gate forcing Bonferroni/FDR correction or an exploratory label. Use when choosing or switching a statistical test, asking which test to run, dropping a test after a non-significant result, or running many tests hunting for significance. For executing the chosen test prefer alterlab-statistical-analysis or alterlab-statsmodels; for the broader frozen-plan discipline see alterlab-preregistration-discipline. Part of the AlterLab Academic Skills suite.Testing★ 78
- alterlab-ssci-inference-gateAlterLab-IEU/AlterLab-Academic-SkillsAudits final inferential claims against the design, sample, and uncertainty before they are written or published — refuses causal language unless the design's identifying assumption is defended (else downgrades to associational), corrects p-value and confidence-interval misreadings (a p-value is not the probability the null is true, non-significance is not proof of no effect, a 95% CI is not a 95% probability the parameter is inside it), demands effect sizes with intervals rather than significance stars, flags uncorrected multiple comparisons and optional stopping / HARKing, and scopes generalization to the sampling frame. Use when writing or checking a results or discussion section, interpreting a p-value or confidence interval, or deciding whether a finding supports a causal or population claim. For choosing the statistical test prefer alterlab-test-selection-guard; to execute the analysis prefer alterlab-statistical-analysis. Part of the AlterLab Academic Skills suite.Testing★ 78
- alterlab-research-workflowsAlterLab-IEU/AlterLab-Academic-SkillsRuns AlterLab's packaged multi-agent academic workflows — citation audit, independent peer-review panel, claim stress-test, PRISMA dual-screening for systematic reviews, point-by-point rebuttal, grant mock panel, and literature map. In Claude Code they run as dynamic workflows (/alterlab-workflows:<name>) with genuinely independent agents, adversarial re-checks, and tallies computed in code; on claude.ai, the API, or anywhere without the Workflow runtime this skill runs the same stages as a staged playbook. Use when a research task needs many independent readers or a second, adversarial opinion at scale — auditing every citation in a manuscript, dual-screening hundreds of records, simulating a review panel or study section, drafting a response to reviewers, or mapping a field before a proposal. Part of the AlterLab Academic Skills suite.Science★ 78
- alterlab-recommendation-lettersAlterLab-IEU/AlterLab-Academic-SkillsDrafts evidence-anchored academic reference and recommendation letters across types — graduate admission, faculty hiring, tenure/promotion external review, fellowship, and award nomination — from a structured prompt of candidate accomplishments, role context, evaluator relationship, and audience, calibrating specificity and register to the letter type and running a no-fabrication guard that flags unsupported superlatives, unanchored rankings, and claims with no evidence in the supplied dossier. Ships scripts/letter_scaffold.py to emit a typed section skeleton and scripts/claim_guard.py to lint a draft for evidence-free assertions. Use when the request is to write a recommendation or reference letter for a student or colleague, a tenure or promotion external-review letter, a fellowship or award nomination, or to check a letter draft for unsupported claims. For a candidate writing their own CV, research statement, or career narrative prefer alterlab-academic-career. Part of the AlterLab Academic Skills suite.Science★ 78
- alterlab-ai-use-disclosureAlterLab-IEU/AlterLab-Academic-SkillsHelps faculty and researchers disclose generative-AI use and comply with the rules for manuscripts, grant proposals, peer review, theses and figures. Walks a venue → policy → placement decision tree over policies verified on 2026-09-23 (ICMJE, Springer Nature/Nature, Elsevier, Wiley, Taylor & Francis, Sage, IEEE, PLOS, arXiv; NIH NOT-OD-25-132 and NOT-OD-23-149, NSF, UKRI, ERC, TÜBİTAK; YÖK's 2024 ethics guide; EU AI Act Article 50), drafts English and Turkish statements, and ships scripts/disclosure_builder.py to emit a statement plus warnings where a policy forbids the use. Use when the request mentions an AI or ChatGPT disclosure or declaration, whether and where AI use must be declared, AI in peer review or grant review, AI-generated figures, AI rules for a thesis, or 'yapay zekâ kullanım beyanı'. For course AI policies and APA/MLA citations of ChatGPT prefer alterlab-syllabus-ai-policy; for writing the paper itself prefer alterlab-scientific-writing. Part of the AlterLab Academic Skills suite.Science★ 78
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