paper-review-helper
Copilot for scientific paper review. Use when reviewing a research paper (PDF/LaTeX), guiding section-by-section analysis, logging issues, and generating structured review responses.
What this skill does
# Paper Review Helper
Guide user through structured paper review, logging issues and generating formal review response.
## Phase 1: Setup
1. **Locate paper** from `$ARGUMENTS`:
- If PDF file: create `<paper-name>-review/` folder, copy PDF into it
- If folder: search for `.tex` first (prioritize over PDF)
- No paper found → `AskUserQuestion`: "No paper detected. Please provide path to paper."
2. **Convert PDF → LaTeX** (if no .tex exists):
```bash
# Check credentials
[ -n "$MATHPIX_APP_ID" ] && [ -n "$MATHPIX_API_KEY" ] && echo "OK" || echo "MISSING"
```
- If MISSING → notify user: "Set MATHPIX_APP_ID and MATHPIX_API_KEY for PDF→LaTeX. Proceeding with direct PDF (figures not extractable)."
- If OK → run conversion:
```bash
python ~/.claude/plugins/science-skill/skills/paper-review-helper/scripts/pdf2tex.py "<pdf_path>" "<paper-folder>"
```
Output structure: `<paper-folder>/<pdf_id>/<pdf_id>.tex` with `images/` subfolder for figures.
3. **Initialize workspace**:
```text
<paper-folder>/
├── <pdf_id>/ # Mathpix output (if converted)
│ ├── <pdf_id>.tex # Converted LaTeX
│ └── images/ # Extracted figures
├── artifact/
│ ├── review-log.md # Conversation log
│ ├── issues-major.md # Major issues
│ ├── issues-minor.md # Minor issues
│ └── programs/ # Math verification scripts
└── original.pdf # Source PDF (if applicable)
```
## Phase 2: Section-by-Section Review
Parse LaTeX structure: `\section`, `\subsection`, `\begin{abstract}`. For each section:
1. **Chunk appropriately**:
- Section ≤5 paragraphs: review whole
- Section >5 paragraphs: split by `\subsection` or paragraph groups
2. **Present section** with:
1. Section text - save the partial tex to markdown format.
2. Inline markers for issues (see Grammar Check Markers) in the markdown file.
3. and provide the user with a path link to the file in the workspace.
4. Figure refs: `[Figure X: images/<filename> - <caption>]` with a path link to the file in the workspace.
3. **Ask user** via `AskUserQuestion`:
- "Questions about this section?"
- "Any concerns or unclear points?"
- "Rate clarity: clear / somewhat unclear / confusing"
4. **Respond to requests**:
- **Citations**: `WebSearch` for DOI, author names, paper titles
- **Background**: `WebSearch`, `WebFetch` for concepts
- **Math check**: write script to `artifact/programs/`, run with Python/SymPy
- **Figures**: read from `images/` folder, describe or use `/vision` skill
5. **Log** to `artifact/review-log.md`:
```markdown
## [Section Name] - [Timestamp]
### User Questions
- Q: ...
- A: ...
### Issues Identified
- [MAJOR] ...
- [MINOR] ...
### Tools Used
- WebSearch: "query" → finding
```
6. **Classify issues**:
- **Major** → `issues-major.md`: methodology flaws, unsupported claims, logical errors
- **Minor** → `issues-minor.md`: grammar, typos, unclear wording
**Skip Supplementary/Appendix** unless user requests.
## Phase 3: Review Generation
1. **Gather context** via `AskUserQuestion`:
- "Overall impression? (accept / minor revision / major revision / reject)"
- "Editor's specific questions?"
- "Journal level? (top-tier / mid-tier / specialized)"
2. **Generate review** to `artifact/REVIEW.md`:
```markdown
# Review of [Paper Title]
## Summary
[1-2 sentences]
## Overall Recommendation
[User's decision + justification]
## Major Issues
1. **Issue**: [description]
- **Location**: Section X, paragraph Y / Equation N
- **Impact**: [why this matters]
- **Suggestion**: [fix or note if unfixable]
## Minor Issues
[Grouped by type: grammar, clarity, formatting]
## User Misunderstandings Analysis
[If user had confusion during review:]
- **Confusion**: [what]
- **Cause**: paper vagueness / reader knowledge gap
- **Recommendation**: [should paper clarify?]
## Constructive Feedback
[Positives + specific improvements]
## Editor Questions Response
[If provided]
```
## Grammar Check Markers
Inline markers when presenting text:
- `[G: ...]` - grammar error
- `[C: ...]` - clarity issue
- `[?]` - ambiguous/unsupported claim
- `[REF?]` - missing or questionable citation
- `[EQ?]` - equation to verify
Related in Writing & Docs
jax-development
IncludedUse this skill when the user is writing, debugging, profiling, refactoring, reviewing, benchmarking, parallelising, exporting, or explaining JAX code, or when they mention JAX, jax.numpy, jit, grad, value_and_grad, vmap, scan, lax, random keys, pytrees, jax.Array, sharding, Mesh, PartitionSpec, NamedSharding, pmap, shard_map, Pallas, XLA, StableHLO, checkify, profiler, or the JAX repo. It helps turn NumPy or PyTorch-style code into pure functional JAX, fix tracer/control-flow/shape/PRNG bugs, remove recompiles and host-device syncs, choose transforms and sharding strategies, inspect jaxpr/lowering/IR, and benchmark compiled code correctly.
nature-article-writer
IncludedDrafts, rewrites, diagnostically critiques, and style-calibrates primary research manuscripts for Nature and Nature Portfolio journals. Use when the user wants a Nature-style title, summary paragraph or abstract, introduction, results, discussion, methods, figure legends, presubmission enquiry, cover letter, reviewer response, or when a scientific draft sounds generic, jargon-heavy, structurally weak, or AI-ish and needs precise, broad-reader-friendly prose without inventing data, analyses, or references. Best for primary research articles and letters rather than reviews or press releases unless explicitly adapting one.
deckrd
IncludedDocument-driven framework that derives requirements, specifications, implementation plans, and executable tasks from goals through structured AI dialogue. Use when user says "write requirements", "create spec", "plan implementation", "derive tasks", "structure this feature", "break down into tasks", or "document this module". Also use for reverse engineering existing code into docs (/deckrd rev). Do NOT use for direct code writing — use /deckrd-coder after tasks are generated. Do NOT use when the user only wants to run or fix existing code without planning.
clinical-decision-support
IncludedGenerate professional clinical decision support (CDS) documents for pharmaceutical and clinical research settings, including patient cohort analyses (biomarker-stratified with outcomes) and treatment recommendation reports (evidence-based guidelines with decision algorithms). Supports GRADE evidence grading, statistical analysis (hazard ratios, survival curves, waterfall plots), biomarker integration, and regulatory compliance. Outputs publication-ready LaTeX/PDF format optimized for drug development, clinical research, and evidence synthesis.
handling-sf-data
IncludedSalesforce data operations with 130-point scoring. Use this skill to create, update, delete, bulk import/export, generate test data, and clean up org records using sf CLI and anonymous Apex. TRIGGER when: user creates test data, performs bulk import/export, uses sf data CLI commands, needs data factory patterns for Apex tests, or needs to seed/clean records in a Salesforce org. DO NOT TRIGGER when: SOQL query writing only (use querying-soql), Apex test execution (use running-apex-tests), or metadata deployment (use deploying-metadata).
accelint-ac-to-playwright
IncludedConvert and validate acceptance criteria for Playwright test automation. Use when user asks to (1) review/evaluate/check if AC are ready for automation, (2) assess if AC can be converted as-is, (3) validate AC quality for Playwright, (4) turn AC into tests, (5) generate tests from acceptance criteria, (6) convert .md bullets or .feature Gherkin files to Playwright specs, (7) create test automation from requirements. Handles both bullet-style markdown and Gherkin syntax with JSON test plan generation and validation.