1claude "Read analysis/student_analysis.py and explain:
21. What is this code trying to accomplish?
32. What statistical methods are being used?
43. What are the inputs and outputs?
54. What are the implicit assumptions?
6 
7Be specific—I need to present this work."

1claude "Review this code for:
21. Potential bugs or logic errors
32. Places where the methodology might be questioned by reviewers
43. Hardcoded values that should be parameters
54. Missing error handling that could cause silent failures
6 
7Don't rewrite the code yet—just identify issues with line numbers."

1claude "Based on your analysis, create a code review document I can share with my student:
21. What's working well (be encouraging)
32. Issues to address, ranked by importance
43. Suggested improvements with examples
54. Questions I need them to answer about methodology choices
6 
7Format this constructively—they're learning."

1claude "My student wrote this function:
2 
3[paste messy function]
4 
5Create a side-by-side comparison showing:
61. Their version
72. A cleaner version with better practices
83. Explanation of each improvement
9 
10Make this educational, not critical."

1claude "I have analysis code from three co-authors in:
2- author1_analysis/ (R)
3- author2_analysis/ (Python)
4- author3_analysis/ (Stata)
5 
6For each directory:
71. List the main scripts and their purpose
82. Identify the key outputs (tables, figures, statistics)
93. Note any overlapping analyses
104. Flag potential inconsistencies (e.g., different sample sizes)
11 
12Create a summary table I can share with co-authors."

1claude "Create a verification script that:
21. Loads key results from all three analyses
32. Checks that sample sizes match where appropriate
43. Verifies that shared variables have consistent descriptives
54. Flags any discrepancies
6 
7Output a report I can discuss at our next co-author meeting."

1claude "Combine results from all three analyses into:
21. Table 1: Descriptive statistics (from author1's R analysis)
32. Table 2: Main regression results (from author2's Python analysis)
43. Table 3: Robustness checks (from author3's Stata analysis)
5 
6Export each as:
7- LaTeX (I'll compile the manuscript)
8- Word (for co-authors who prefer it)
9 
10Use consistent formatting across all tables."

1claude "Make this analysis fully reproducible. Create:
2 
31. requirements.txt with pinned versions
42. A setup.sh script that creates the environment
53. A master run_analysis.sh that executes everything in order
64. A README.md with:
7   - System requirements
8   - Step-by-step instructions
9   - Expected outputs
10   - Troubleshooting common issues
11   - Contact info for questions
12 
135. A MANIFEST.md listing all files and their purposes
14 
15Test this by checking if the instructions are complete enough
16that someone could run it without asking me questions."

1claude "Imagine you're a new grad student joining my lab.
2Read the entire analysis/ directory and tell me:
3 
41. Can you figure out what this project does?
52. Can you run the analysis from start to finish?
63. What questions would you need to ask me?
74. What's confusing or undocumented?
8 
9Be honest—I want this to be self-explanatory."

1claude "My co-author sent coauthor_data.xlsx. Please:
2 
31. Read it and show me the structure
42. Identify any issues (missing values, inconsistent formatting)
53. Suggest how to clean it for merging with our main dataset
64. Create a simple script they could run themselves in the future
7 
8Keep explanations non-technical—they're a domain expert, not a coder."

1claude "Create technical documentation for analysis/main_model.py:
2 
31. Mathematical specification of the model
42. Algorithm pseudocode
53. Computational complexity
64. Key implementation decisions and alternatives considered
75. Validation approach
8 
9This is for a co-author who will scrutinize every methodological choice."

1claude "I'm presenting this analysis at lab meeting in an hour.
2Read analysis/ and create:
3 
41. A 5-bullet summary of what we did and found
52. The 3 most important figures (regenerate if needed)
63. A list of 'questions we should discuss'
74. Known limitations to acknowledge proactively
8 
9Format for screen sharing."

1claude "Based on this analysis, what critical questions might come up at lab meeting?
2For each question:
31. Why someone might ask it
42. How to respond
53. Any additional analysis that would strengthen our answer"

1claude "Help me set up a GitHub repository for our collaborative project:
2 
31. Create a good .gitignore for R/Python data analysis
42. Set up branch protection rules (explain why)
53. Create a CONTRIBUTING.md with our workflow:
6   - How to claim tasks
7   - Branch naming conventions
8   - PR review process
94. Create issue templates for:
10   - Bug reports
11   - Analysis requests
12   - Data questions
13 
14Make this appropriate for a team where some members are Git beginners."

1claude "We have a merge conflict in analysis/results.py.
2The conflict is between:
3- My version: Added bootstrap analysis
4- Co-author's version: Added sensitivity analysis
5 
6Help me:
71. Understand both changes
82. Merge them without losing either contribution
93. Test that the merged version works"

1claude "Draft an email to my collaborator requesting their data contribution.
2I need:
3- The processed survey responses
4- Their cleaning code (so I can document it)
5- A brief methods description
6 
7Make it polite but specific about exactly what format I need.
8We have a paper deadline in 3 weeks."

1claude "My co-author (a field ecologist) wants to understand what my Python analysis does.
2 
3Read analysis/statistical_model.py and create an explanation that:
4- Uses domain language, not programming jargon
5- Includes analogies to familiar concepts
6- Shows the logic flow visually (ASCII diagram is fine)
7- Explains what we'd conclude from different results
8 
9Assume expertise in ecology but not statistics or programming."

1claude "I'm wrapping up my involvement in this project. Create a handoff document:
2 
31. Current status of all analyses
42. What's done vs. what's still needed
53. Known issues and workarounds
64. Key decisions made and why
75. Files that are important vs. temporary
86. Who to contact for what questions
97. Upcoming deadlines
10 
11Make this comprehensive—I won't be available for questions."

1claude "A new postdoc is joining this project. Create an onboarding guide:
2 
31. Project overview (1 paragraph)
42. Key papers to read
53. Important files and what they do
64. How to set up their environment
75. First tasks they should try
86. Who to ask for what
97. Common gotchas to avoid
10 
11Make this warm and welcoming but efficient."

1claude "Add a DECISIONS.md file documenting:
2- Why we chose this statistical method over alternatives
3- How we handled missing data and why
4- What sensitivity analyses we ran
5- Reviewer comments that shaped our approach"

1claude "Create a dependency diagram showing:
2- Which scripts depend on which data files
3- Which outputs feed into which analyses
4- Where manual steps are required
5 
6Export as a Mermaid diagram I can add to the README."

claude "Add documentation that answers:
'Why did we do it this way?'
 
For every non-obvious decision in analysis/main.py, add a comment explaining the reasoning."