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claude-code-academic-workflow
AI-assisted academic workflow with LaTeX/Beamer, R, Quarto, multi-agent review, quality gates, and reproducibility protocols
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
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AI-assisted academic workflow with LaTeX/Beamer, R, Quarto, multi-agent review, quality gates, and reproducibility protocols
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
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| name | claude-code-academic-workflow |
| description | AI-assisted academic workflow with LaTeX/Beamer, R, Quarto, multi-agent review, quality gates, and reproducibility protocols |
| triggers | ["set up academic workflow for slides and papers","create lecture slides with quality review","review my research paper with adversarial QA","verify reproducibility of my analysis","run multi-agent review on my manuscript","check my Beamer slides for quality","compile and review my academic presentation","audit my paper for fabricated claims"] |
Skill by ara.so — Claude Code Skills collection.
A production-ready template for AI-assisted academic work using Claude Code. Provides specialized agents for reviewing slides, papers, and data analysis; quality scoring with 80/90/95 thresholds; adversarial QA loops; and reproducibility verification. Originally extracted from a PhD course, now community-maintained with 1200+ GitHub stars.
Core capabilities:
Supported artifacts:
# Fork on GitHub first, then:
git clone https://github.com/YOUR_USERNAME/claude-code-my-workflow.git my-project
cd my-project
./scripts/validate-setup.sh
Minimum requirements:
For LaTeX/Beamer demos:
For Quarto demos:
For R analysis:
tidyverse, fixest, modelsummaryRecommended:
gh)Start Claude Code and paste:
I am starting to work on [PROJECT NAME] in this repo. [Describe project in 2-3 sentences.]
I've set up the Claude Code academic workflow from pedrohcgs/claude-code-my-workflow.
Please read the configuration files (.claude/CLAUDE.md, .claude/rules/*.md, .claude/skills/*.md)
and adapt them for my project. Set my name to [YOUR NAME], institution to [YOUR INSTITUTION],
and configure the project metadata. Enter plan mode and start.
Claude will:
.claude/project.yaml/validate-setupCreate lecture slides:
/create-lecture "Introduction to Panel Data"
Compile LaTeX to PDF:
/compile-latex HelloWorld
Run quality review (slide-specific agents):
/slide-excellence Slides/MyLecture.tex
Adversarial QA loop:
/qa-beamer Slides/MyLecture.tex --rounds 5
Multi-agent peer review:
/review-paper manuscript.tex --peer
Adversarial review:
/review-paper manuscript.tex --adversarial
Multi-referee variance sampling (v1.9.0):
/review-paper manuscript.tex --variance 10
Runs 10 referees with sampled dispositions, reports decision distribution.
Verify claims against fabrication:
/verify-claims manuscript.tex
Chain-of-Verification with forked verifier. HIGH-WARN findings block /commit.
Audit reproducibility:
/audit-reproducibility manuscript.tex
Cross-checks every numeric claim against script output. Generates passport.yaml with PASS/FAIL/STALE status per claim.
Detect AI-voice patterns:
/humanize manuscript.tex
Read-only detection of boilerplate, hedging, sycophancy. Does NOT auto-rewrite.
Deploy Quarto document:
/deploy HelloWorld
QA Quarto document:
/qa-quarto Quarto/MyDoc.qmd
Review R script quality:
/review-r analysis/main.R
Literature review for R packages:
/lit-review "fixest two-way fixed effects"
Commit with quality gates:
/commit "Add panel data lecture" --require-score 80
Blocks if quality score < 80.
Create pull request:
/pr "Manuscript ready for review" --require-score 90
Blocks if quality score < 90.
Compress long session:
/compress-session
Distills conversation to structured note before auto-compaction. Preserves decisions, next actions, marks noise.
Promote learnings to shared memory:
/promote-memory
Five-critic council reviews personal-memory.md, promotes generic patterns to MEMORY.md.
Set effort level:
/effort high
Options: low, medium, high, xhigh, max. Opus 4.8 defaults to high.
Check usage:
/cost
/usage
Goal-driven persistence (v1.9.0):
/goal "all tests pass and manuscript score > 90"
Keeps working across turns until fast model confirms condition.
.claude/project.yaml:
project:
name: "My Research Project"
author: "Your Name"
institution: "Your University"
course: "ECON 5000" # or null for papers
quality:
commit_threshold: 80
pr_threshold: 90
excellence_threshold: 95
paths:
slides: "Slides"
quarto: "Quarto"
data: "Data"
analysis: "R"
.claude/agents/ directory contains 18+ agent specs:
Example — R Reviewer (.claude/agents/r-reviewer.md):
---
agent: r-reviewer
focus: R code quality and best practices
model: sonnet-4.6
effort: medium
---
# R Code Reviewer
## Responsibilities
- tidyverse idiom compliance
- fixest usage patterns
- modelsummary output quality
- data.table efficiency
- statistical correctness
## Review Checklist
- [ ] No base R subsetting in tidyverse pipelines
- [ ] Fixed effects specified correctly
- [ ] Standard errors clustered appropriately
- [ ] Tables formatted for publication
- [ ] Code reproducible from raw data
Skills are in .claude/skills/. Frontmatter specifies triggers, effort, context, hooks:
Example — /create-lecture:
---
skill: create-lecture
description: Create Beamer lecture slides with quality review
triggers:
- create lecture on
- make slides about
- build presentation for
effort: high
context: fork
hooks:
- PreEdit
- PostEdit
- PreCompact
---
# Create Lecture
(implementation follows)
.claude/rules/ directory:
core-principles.md — contractor mode, quality thresholds, verification disciplinelatex-rules.md — XeLaTeX compilation, Beamer best practices, TikZ preventionquarto-rules.md — rendering, deployment, cross-referencingr-rules.md — tidyverse style, fixest patterns, reproducibilitymeta-governance.md — MEMORY.md vs personal-memory.md distinction.claude/hooks/ directory enables event-driven workflows:
Example — PreCompact hook (.claude/hooks/pre-compact.yaml):
name: pre-compact
event: PreCompact
handler:
type: command
command: ./scripts/save-context-snapshot.sh
description: Save context before auto-compression
User: Create a lecture on difference-in-differences with 2x2 design
Claude: [Enters plan mode]
PLAN:
1. Create Slides/DID-2x2.tex with Beamer template
2. Sections: Motivation, Framework, Identification, Estimation, Inference
3. Include numerical example with treatment/control, pre/post
4. Add R code snippet showing fixest::feols()
5. Run /slide-excellence for quality review
6. Run /qa-beamer for adversarial QA
7. Compile to PDF
8. Score and report
[Implements → reviews → fixes → scores 87 → presents summary]
User: just do it
Claude: [Runs /commit with quality gates passed]
User: /audit-reproducibility paper.tex
Claude: [Scans paper for numeric claims]
Found 47 claims:
- Table 2, Row 1, Col 2: "0.342 (0.089)" → R/analysis.R:156
- Table 3, Row 4, Col 1: "-1.23" → R/robustness.R:89
- Text line 234: "23% increase" → R/summary-stats.R:45
[Executes scripts, compares output]
FINDINGS:
✓ 44/47 claims verified
⚠ 2 STALE (script modified after paper)
✗ 1 FAIL (Table 3 Row 4: script produces -1.19, paper says -1.23)
passport.yaml updated. Quality score: 72 (below commit threshold).
User: /review-paper paper.tex --variance 10
Claude: [Samples 10 referee dispositions, runs domain-referee + methods-referee + editor]
DECISION DISTRIBUTION (N=10):
- Accept: 2
- Minor Revision: 5
- Major Revision: 2
- Reject: 1
VARIANCE ANALYSIS:
- Methodological rigor concerns consistent across 8/10
- Contribution strength varies with reviewer disposition (ρ=0.83)
- Decision highly sensitive to prior belief about external validity
REPRESENTATIVE REVIEWS:
[Shows accept, minor, major, reject reviews with sampled contexts]
Recommendation: Address methodological concerns before submission.
User: /review-r analysis/main.R
Claude: [r-reviewer agent activated]
FINDINGS:
✓ tidyverse idioms correct
✓ fixest models specified properly
✗ Line 45: base R subsetting in dplyr pipeline
Replace: df[df$year > 2010, ]
With: df %>% filter(year > 2010)
✗ Line 89: standard errors not clustered
Add: , vcov = ~state
⚠ Line 134: modelsummary() missing stars_note = FALSE for publication
SCORE: 78/100 (below commit threshold)
[Presents fix plan]
User: /verify-claims manuscript.tex
Claude: [Forked verifier with no draft access]
CHAIN-OF-VERIFICATION:
Claim 1: "Smith et al. (2023) find a 15% reduction"
- Citation exists: ✓
- Numerical match: ✗ (paper reports 12%, not 15%)
- Severity: HIGH-WARN
Claim 2: "Table 2 shows no significant effect"
- Cross-check with Table 2: ✓
- p-value > 0.05: ✓
- Severity: PASS
Claim 3: "consistent with Jones (2022)"
- Citation exists: ✓
- Conceptual alignment: ✓ (verified via abstract)
- Severity: PASS
Claim 4: "as discussed in Section 3"
- Internal reference exists: ✗ (no Section 3 discussion of this)
- Severity: MED-WARN
RESULT: 1 HIGH-WARN finding blocks /commit. Fix Claim 1 before proceeding.
1. Clone repo
2. Run ./scripts/validate-setup.sh
3. Start Claude, paste starter prompt
4. Describe first task (lecture or paper)
5. Approve plan
6. Let contractor mode orchestrate
7. Say "just do it" to auto-commit when score > 80
/create-lecture "Topic Name"
→ Claude creates .tex, compiles, runs /slide-excellence, /qa-beamer
→ Reports score
→ You review findings or say "just do it"
→ Auto-commits if score ≥ 80
1. /review-paper manuscript.tex --peer
→ domain-referee + methods-referee + editor in parallel
2. /verify-claims manuscript.tex
→ Catches fabrications
3. /audit-reproducibility manuscript.tex
→ Verifies all numbers against scripts
4. /humanize manuscript.tex
→ Flags AI-voice patterns (read-only)
5. Address findings
6. /review-paper manuscript.tex --variance 10
→ Sample decision distribution
7. /pr "Ready for submission" --require-score 90
1. /audit-reproducibility manuscript.tex
→ Generates passport.yaml with claim→script mappings
2. Review STALE/FAIL findings
3. Fix scripts or manuscript
4. Re-run until all PASS
5. /commit "Replication package verified"
Symptom: /compile-latex errors with font not found
Fix:
# Rebuild font cache
sudo fc-cache -fv
# Or install missing fonts (example for Linux Libertine)
tlmgr install libertine
Symptom: "Score 76 below commit threshold 80"
Fix:
# Review findings
Show me the detailed findings from the last review
# Fix specific issues or override
/commit "Draft WIP" --override-score
Symptom: "Script modified after paper written"
Fix:
# Re-run analysis
Rscript R/analysis.R
# Re-audit
/audit-reproducibility manuscript.tex
Symptom: High cost for repeated operations
Fix:
# Check cache TTL (5-min API default, 1-hour on subscriptions)
/usage
# Batch related work to leverage cache
/create-lecture "Lecture 1"
/create-lecture "Lecture 2" # reuses cached rules
Symptom: /review-paper --peer stalls
Fix:
# Monitor agent dashboard (v1.9.0+)
claude agents
# Or run sequentially
/review-paper manuscript.tex --peer --sequential
Symptom: Context full, losing decisions
Fix:
# Manual compression before auto-compact
/compress-session
# Or promote learnings early
/promote-memory
Symptom: /verify-claims found fabricated citation, /commit blocked
Fix:
# Review specific finding
Show the HIGH-WARN finding details
# Fix the claim in manuscript
# Re-verify
/verify-claims manuscript.tex
# Commit once clear
/commit "Claims verified"
No secrets required for core workflow. Optional for extensions:
# GitHub CLI (for /pr skill)
export GH_TOKEN="your_github_token"
# Anthropic API key (if using API directly, not Claude Code app)
export ANTHROPIC_API_KEY="your_api_key"
# R package library (optional custom path)
export R_LIBS_USER="~/R/library"
/goal, claude agents, /compress-session)high by default (equivalent to prior xhigh)CHANGELOG.md in repo for version history