skills/15-Felpix-Studios-social-science-research/skills/proofread/SKILL.md
Run the proofreading protocol on academic writing — papers or manuscripts. Checks grammar, typos, layout issues, consistency, and academic writing quality. Produces a report without editing files. Make sure to use this skill whenever the user wants surface-level writing errors found — not substantive academic critique. Triggers include: "proofread", "check for typos", "grammar check", "look for errors in my draft", "proofread all", "polish this", "check my writing", "are there any mistakes", "proofread before I send this", or when the user wants a clean-up pass rather than feedback on arguments or methods.
npx skillsauth add brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research proofreadInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Run the mandatory proofreading protocol on papers or manuscripts. Produces a report of all issues WITHOUT editing any source files.
Identify files to review:
$ARGUMENTS is a specific filename: review that file only$ARGUMENTS is all: review all files in manuscripts/ and Quarto/ (if it exists)$ARGUMENTS is a file in manuscripts/: treat as manuscript (not slides)$ARGUMENTS is empty or ambiguous and multiple files exist, use AskUserQuestion:
For each file, launch the proofreader agent that checks for:
GRAMMAR: Subject-verb agreement, articles (a/an/the), prepositions, tense consistency TYPOS: Misspellings, search-and-replace artifacts, duplicated words LAYOUT ISSUES: Overfull hbox (LaTeX slides/manuscripts), content exceeding slide boundaries (Quarto), table/column overflow in manuscripts CONSISTENCY: Citation format, notation, terminology, variable names matching table column names ACADEMIC QUALITY: Informal language, missing words, awkward constructions
Produce a detailed report for each file listing every finding with:
Save each report to quality_reports/:
.tex slide files: quality_reports/FILENAME_report.md.qmd slide files: quality_reports/FILENAME_qmd_report.mdquality_reports/FILENAME_proofread.mdIMPORTANT: Do NOT edit any source files.
Only produce the report. Fixes are applied separately after user review (see rules/proofreading-protocol.md).
Present summary to the user:
development
Conduct rigorous thematic analysis (TA) of qualitative data following Braun and Clarke's (2006) six-phase framework. Use whenever the user mentions 'thematic analysis', 'TA', 'Braun and Clarke', 'qualitative coding', 'identifying themes', or asks for help analysing interviews, focus groups, open-ended survey responses, or transcripts to identify patterns. Also trigger for questions about inductive vs theoretical coding, semantic vs latent themes, essentialist vs constructionist epistemology, building a thematic map, or writing up a qualitative findings section. Covers all six phases, the four upfront analytic decisions, the 15-point quality checklist, and the five common pitfalls. Produces a Word document write-up and an annotated thematic map. Does NOT cover IPA, grounded theory, discourse analysis, conversation analysis, or narrative analysis — use a different method for those.
development
Guide users through writing a systematic literature review (SLR) following the PRISMA 2020 framework. Use this skill whenever the user mentions 'systematic review', 'systematic literature review', 'SLR', 'PRISMA', 'PRISMA 2020', 'PRISMA flow diagram', 'PRISMA checklist', or asks for help writing, structuring, or auditing a literature review that follows reporting guidelines. Also trigger when the user asks about inclusion/exclusion criteria for a review, search strategies for databases like Scopus/WoS/PubMed, study selection processes, risk of bias assessment, or narrative synthesis for a review paper. This skill covers the full PRISMA 2020 checklist (27 items), produces a Word document manuscript in strict journal article format, generates an annotated PRISMA flow diagram, and enforces APA 7th Edition referencing throughout. It does NOT cover meta-analysis or statistical pooling. By Chuah Kee Man.
testing
Performs placebo-in-time sensitivity analysis with hierarchical null model and optional Bayesian assurance. Use when checking model robustness, verifying lack of pre-intervention effects, or estimating study power.
data-ai
Fit, summarize, plot, and interpret a chosen CausalPy experiment. Use after the causal method has been selected, including when configuring PyMC/sklearn models and scale-aware custom priors.