Paper Excellence Review
SkillFiles & storageComprehensive multi-dimensional review of the sewage-house-prices project. Runs econometrics audit, code review, manuscript proofread, and bibliography validation in parallel. Computes a weighted aggregate score. This skill should be used when asked for a "full review", "quality check", "paper excellence", or before submission milestones.
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
Account requirements not reviewed. Check the skill instructions before use; ahel provides instructions and does not run this skill.
Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
Then ask your AI: use the Paper Excellence Review skill
What this skill tells your AI
The instructions your AI receives, as published by brycewang-stanford/auto-empirical-research-skills in skills/41-sticerd-eee-sewage-econometrics-check/skills/paper-excellence/SKILL.md and read by ahel’s review.
Run a comprehensive quality assessment of the sewage-house-prices project across all dimensions.
Input: $ARGUMENTS — all for full review, or a specific component.
Workflow
Step 1: Identify Targets
docs/overleaf/*.tex— Manuscript sectionsscripts/R/09_analysis/— Analysis scriptsoutput/tables/— Generated tablesoutput/figures/— Generated figuresdocs/overleaf/refs.bib— Bibliography
Step 2: Launch Review Agents (Parallel)
Launch up to 4 agents simultaneously:
Agent 1: Econometrics Audit Review all identification strategies (hedonic, repeat sales, long diff, DiD, upstream/downstream, dry spills). Cross-reference manuscript claims against analysis scripts. Weight: 30%
Agent 2: Code Review
Review all scripts in scripts/R/09_analysis/ for code quality, reproducibility, and project convention compliance.
Weight: 15%
Agent 3: Manuscript Proofread
Review all .tex files for structure, claims-evidence alignment, identification fidelity, writing quality, grammar, and LaTeX compilation.
Weight: 35%
Agent 4: Bibliography Validation
Cross-reference all citations against refs.bib. Check for missing entries, unused references, and quality issues.
Weight: 5%
Step 3: Compute Weighted Aggregate Score
Overall = 0.30 × Econometrics + 0.15 × Code + 0.35 × Paper + 0.05 × Bibliography + 0.15 × Polish
Where Polish is derived from the Proofreader's writing quality subscore.
If components are missing (e.g. no manuscript sections yet), renormalise weights over available components.
Step 4: Present Results
# Paper Excellence Report: Sewage in Our Waters
**Date:** YYYY-MM-DD
**Aggregate Score:** XX/100
## Score Breakdown
| Component | Weight | Score | Issues | Source |
|-----------|--------|-------|--------|--------|
| Econometrics | 30% | XX | N | /econometrics-check |
| Code | 15% | XX | N | /review-r |
| Paper | 35% | XX | N | /proofread |
| Bibliography | 5% | XX | N | /validate-bib |
| Polish | 15% | XX | N | Writing quality subscore |
## Priority Fixes (Top 5)
1. **[CRITICAL]** [Most important issue]
2. **[MAJOR]** [Second priority]
3. ...
## Quality Gate
- Score >= 90: "Ready for submission."
- Score >= 80: "Commit-ready. Address major issues before submission."
- Score < 80: "Blocked. Must fix critical/major issues."
## Full Reports
- Econometrics: output/log/econometrics_check_all.md
- Code: output/log/code_review_all.md
- Proofread: output/log/proofread_report_all.md
- Bibliography: output/log/bib_validation.md
Save to output/log/paper_excellence_[date].md.
Principles
- Parallel execution. All agents run simultaneously for efficiency.
- Weighted aggregation. Not a simple average — econometrics and paper quality dominate.
- Don't double-count. Same issue found by multiple agents counts once in priority list.
- One unified report. User sees one priority list, not separate reports.
- Proportional gating. Working papers get developmental feedback. Near-final manuscripts get submission-level scrutiny.
Signals
- GitHub stars
- 4k
- Forks
- 531
- Last commit
- Sep 2026
Advanced
- Item type
- skill
- Key
paper-excellence- Source
- github.com/brycewang-stanford/auto-empirical-research-skills
github.com/brycewang-stanford/auto-empirical-research-skills
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