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scdenney/open-science-skills

38 skills

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SkillInstalls
46-orchestrateOrchestrate complex work as the Codex lead on the GPT-5.6 family.—advisorConsult this library's independent GPT-5.6 advisor before committing to a substantive interpretation, approach, or final result.—citation-checkAudit citation existence and fabrication risk, in-text/reference parity, DOIs, claim support, and style.—conjoint-cleaningClean and reshape Qualtrics conjoint exports to analysis-ready long format.—conjoint-designDesign conjoint experiments covering attributes, power, and AMCE/AMIE estimation.—conjoint-diagnosticsDiagnose conjoint design integrity, estimation choices, and validity.—cross-national-designDesign cross-national survey experiments covering power, equivalence, and localization.—divergeBefore implementing, generate 3-5 conceptually distinct approaches labeled by creativity dimension (Novel, Surprising, Diverse, Conventional), then hold for…—diverge-codexDelegate creative divergence to a fresh Codex subagent before implementation.—fable-orchestrateRun a multi-model orchestration workflow led by Fable 5, the strongest model on the team.—fact-checkFact-check manuscript claims against cited sources in a per-source Markdown knowledge base.—fair-checkAudit manuscript and replication package against FAIR open-science principles.—figure-table-auditAudit figures, tables, captions, cross-references, and statistical notes.—figuresDesign and format publication-quality figures. Use for chart choice, color, scales, legends, captions, accessibility, and reproducible figure workflows.—hypothesis-buildingBuild falsifiable causal hypotheses. Use for DAGs, FPCI, equivalence testing, mechanism specification, and deriving testable predictions from theory.—journal-reviewDraft a senior peer-review report on a social-science manuscript.—list-experimentDesign and diagnose list experiments (item count technique).—literature-reviewBuild or audit a literature review. Use for evidence maps, gap analysis, contribution checks, source verification, and synthesis planning.—llm-calibration-logprobsAnalyze LLM token logprobs and calibration. Use for per-decision confidence, ECE, Brier scores, reliability diagrams, and low-confidence triage.—methods-reportingCheck methods reporting against CONSORT, JARS, DA-RT standards.—model-committeeRun a deliberative two-model committee between GPT-5.6 "Sol" and Claude Opus 5.—model-committee-fableRun a deliberative two-model committee between GPT-5.6 "Sol" and Claude Opus 5, chaired by Fable 5.—model-committee-solRun a deliberative two-model committee between GPT-5.6 "Terra" and Claude Opus 5, chaired by GPT-5.6 "Sol." Same two deliberating members as model-committee;…—model-council-votingLLM council/panel voting — multi-model coders, consensus rules, inter-rater agreement (kappa, alpha), correlated-error diagnostics.—narrative-buildingDraft or audit scientific introductions. Use for argument logic, framing, contribution structure, and coherence across multiple studies or experiments.—paper-review-liteRun a pre-submission manuscript audit covering argument, numerics, references, writing, figures, methods, preregistration, and replication readiness.—paper-review-lite-codexRun a cross-model adversarial pre-submission audit from Codex.—paper-texTypeset a working paper or journal submission in house-style LaTeX from Markdown, Word, TeX, ODT, RTF, or HTML.—post-ocr-cleanupClean post-OCR research text. Use for correction, quality assurance, multilingual handling, uncertainty preservation, and provenance-aware cleanup.—pre-registration-writingWrite pre-analysis plans and preregistrations. Use for PAP structure, registry requirements, estimands, analysis strategy, exclusions, and deviation planning.—replication-packageScaffold or audit a social-science replication package at a target directory.—research-repoScaffold or audit an entire research project repository organized around its source library.—survey-designDesign survey instruments. Use for question wording, scales, flow, pretesting, respondent burden, and social-desirability mitigation.—tablesDesign and format publication-quality tables. Use for column order, row grouping, notes, statistical precision, accessibility, and reproducibility.—text-classificationDesign and validate LLM-based text classification. Use for codebooks, prompts, validation samples, agreement statistics, and reporting model-coded data.—topic-modelingDesign and diagnose structural topic models. Use for STM specification, topic-count selection, coherence-exclusivity tradeoffs, validation, and interpretation.—vlm-ocr-evaluationCompare OCR systems before a bulk run: candidate set, stratified ground truth, CER/WER, normalization, per-language and per-stratum accuracy.—vlm-ocr-pipelineDesign VLM-based OCR pipelines. Use for model selection, prompting, architecture, multilingual documents, structured output, and evaluation planning.—