skills/15-Felpix-Studios-social-science-research/skills/review-paper/SKILL.md
Comprehensive manuscript review covering argument structure, econometric specification, citation completeness, and potential referee objections. Make sure to use this skill whenever the user wants substantive academic feedback on a paper — not just surface edits. Triggers include: "review my paper", "give me feedback on this draft", "what would a referee say", "anticipate referee objections", "act as a referee", "check my identification strategy", "is my argument convincing", "review this manuscript", "critique my paper", "will this pass review", or any request for deep critique of academic writing beyond typos and grammar.
npx skillsauth add brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research review-paperInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
Produce a thorough, constructive review of an academic manuscript — the kind of report a top-journal referee would write.
Input: $ARGUMENTS — path to a paper (.tex, .pdf, or .qmd), or a filename in manuscripts/ or references/papers/.
Locate and read the manuscript. Check:
$ARGUMENTSmanuscripts/$ARGUMENTSreferences/papers/$ARGUMENTSmanuscripts/ and references/papers/Read the full paper end-to-end. For long PDFs, read in chunks (5 pages at a time).
Dispatch domain-reviewer agent via Task for deep substance review (see below).
Evaluate writing quality and presentation (dimensions 5-6) — the skill handles these directly since the agent explicitly does not cover presentation.
After the agent completes, merge its findings with your writing/presentation evaluation. Generate 3-5 "referee objections" synthesized from both.
Produce the unified review report.
Save to quality_reports/paper_review_[sanitized_name].md
Dispatch the domain-reviewer agent via Task for the deep substance check. The agent applies 5 lenses that go deeper than broad dimensional evaluation — actual equation verification, derivation step checking, code-theory alignment, and backward logic tracing.
Task prompt: "You are the domain-reviewer agent. Review the manuscript at [path].
Research question: [from spec if available].
Apply all 5 review lenses:
1. Assumption stress test
2. Derivation verification
3. Citation fidelity
4. Code-theory alignment
5. Backward logic check
Also check cross-document consistency.
Follow the domain-reviewer agent instructions and return your full substance review report."
After the agent completes, collect its findings. These feed into the "Major Concerns" and "Referee Objections" sections of the final report.
The skill evaluates dimensions 5-6 directly (the agent does not cover these), then merges everything into the unified report format below.
# Manuscript Review: [Paper Title]
**Date:** [YYYY-MM-DD]
**Reviewer:** review-paper skill
**File:** [path to manuscript]
## Summary Assessment
**Overall recommendation:** [Strong Accept / Accept / Revise & Resubmit / Reject]
[2-3 paragraph summary: main contribution, strengths, and key concerns]
## Strengths
1. [Strength 1]
2. [Strength 2]
3. [Strength 3]
## Major Concerns
### MC1: [Title]
- **Dimension:** [Identification / Econometrics / Argument / Literature / Writing / Presentation]
- **Issue:** [Specific description]
- **Suggestion:** [How to address it]
- **Location:** [Section/page/table if applicable]
[Repeat for each major concern]
## Minor Concerns
### mc1: [Title]
- **Issue:** [Description]
- **Suggestion:** [Fix]
[Repeat]
## Referee Objections
These are the tough questions a top referee would likely raise:
### RO1: [Question]
**Why it matters:** [Why this could be fatal]
**How to address it:** [Suggested response or additional analysis]
[Repeat for 3-5 objections]
## Specific Comments
[Line-by-line or section-by-section comments, if any]
## Summary Statistics
| Dimension | Rating (1-5) |
|-----------|-------------|
| Argument Structure | [N] |
| Identification | [N] |
| Econometrics | [N] |
| Literature | [N] |
| Writing | [N] |
| Presentation | [N] |
| **Overall** | **[N]** |
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.