skills/29-quarcs-lab-project20XXy/dot-claude/skills/submission-prep/SKILL.md
Runs pre-submission checks (word count, anonymization, citations, placeholders, cross-refs) and generates a checklist. Use before journal submission.
npx skillsauth add brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research submission-prepInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Run pre-submission checks and generate a submission checklist for a journal submission.
Render the manuscript if _manuscript/ does not exist or is stale:
bash scripts/render.sh
Word count — Count words in the prose body of index.qmd:
Anonymization check — Scan index.qmd for content that may violate double-blind review:
author field) appearing in the body textCitation audit — Cross-check citations and references:
@key and [@key] citations from index.qmdreferences.bib.bib (errors).bib not cited in the manuscript (unused — informational only)Figures and tables inventory — List all embedded outputs:
{{< embed >}} shortcodes from index.qmdPlaceholder check — Scan for orphan [FILL:] placeholders in index.qmd:
grep -n "\[FILL:" index.qmd
Report each with line number and context.
Cross-reference check — Scan for @sec-, @fig-, @tbl- references in index.qmd and verify each target exists (section IDs in the document, figure/table labels in notebooks).
Generate submission checklist:
## Submission Checklist
- [ ] Word count within journal limit: <count> words
- [ ] No anonymization issues (or list issues to fix)
- [ ] All citations resolve to references.bib entries
- [ ] No orphan [FILL:] placeholders in manuscript
- [ ] All embedded figures/tables have valid notebook sources
- [ ] All cross-references resolve
- [ ] Abstract present and complete
- [ ] Keywords listed
- [ ] Acknowledgments section reviewed
- [ ] Data availability statement included
- [ ] PDF renders without errors
- [ ] Figures are high resolution
- [ ] Supplementary materials prepared (if applicable)
Report the full checklist with pass/fail status for each automated check, and leave manual items unchecked for 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.
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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.
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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.