Analyze these evaluation runs for claude-code using screen on python-repl.

The runs are located in evaluation-results/ with filenames:
evaluation-results/claude-code--python-repl--screen--20250815222817685000-prompt.md
evaluation-results/claude-code--python-repl--screen--20250815222817685000-scrollbuffer.txt
evaluation-results/claude-code--python-repl--screen--20250815223033260001-prompt.md
evaluation-results/claude-code--python-repl--screen--20250815223033260001-scrollbuffer.txt
evaluation-results/claude-code--python-repl--screen--20250815223233828002-prompt.md
evaluation-results/claude-code--python-repl--screen--20250815223233828002-scrollbuffer.txt
evaluation-results/claude-code--python-repl--screen--20250815223509394003-prompt.md
evaluation-results/claude-code--python-repl--screen--20250815223509394003-scrollbuffer.txt
evaluation-results/claude-code--python-repl--screen--20250815223749968004-prompt.md
evaluation-results/claude-code--python-repl--screen--20250815223749968004-scrollbuffer.txt
evaluation-results/claude-code--python-repl--screen--20250815224025535005-prompt.md
evaluation-results/claude-code--python-repl--screen--20250815224025535005-scrollbuffer.txt
evaluation-results/claude-code--python-repl--screen--20250815224331134006-prompt.md
evaluation-results/claude-code--python-repl--screen--20250815224331134006-scrollbuffer.txt
evaluation-results/claude-code--python-repl--screen--20250815224636748007-prompt.md
evaluation-results/claude-code--python-repl--screen--20250815224636748007-scrollbuffer.txt
evaluation-results/claude-code--python-repl--screen--20250815224932339008-prompt.md
evaluation-results/claude-code--python-repl--screen--20250815224932339008-scrollbuffer.txt
evaluation-results/claude-code--python-repl--screen--20250815225132920009-prompt.md
evaluation-results/claude-code--python-repl--screen--20250815225132920009-scrollbuffer.txt

CRITICAL INSTRUCTIONS:
1. You MUST read EVERY file listed above IN FULL
2. You MUST use the EXACT format provided below - no deviations allowed
3. You MUST fill in ACTUAL content for each section - no placeholder text

After reading ALL files, output your response in EXACTLY this format (replace the bracketed text with your actual analysis):

## Overall Performance
Write a 2-3 sentence summary of how well the agent performed across all runs. Be specific about success rates and patterns you observed.

## What Went Well
- Specific strength observed in the runs
- Another specific strength with evidence
- Continue listing all strengths you observed

## What Went Wrong  
- Specific issue or failure pattern
- Another problem you identified
- Continue listing all issues found

## Run-by-Run Analysis
- Run 20250815222817685000: Pass or Fail - Provide a specific 1-2 sentence assessment of this run
- Run 20250815223033260001: Pass or Fail - Provide a specific 1-2 sentence assessment of this run
- Run 20250815223233828002: Pass or Fail - Provide a specific 1-2 sentence assessment of this run
- Run 20250815223509394003: Pass or Fail - Provide a specific 1-2 sentence assessment of this run
- Run 20250815223749968004: Pass or Fail - Provide a specific 1-2 sentence assessment of this run
- Run 20250815224025535005: Pass or Fail - Provide a specific 1-2 sentence assessment of this run
- Run 20250815224331134006: Pass or Fail - Provide a specific 1-2 sentence assessment of this run
- Run 20250815224636748007: Pass or Fail - Provide a specific 1-2 sentence assessment of this run
- Run 20250815224932339008: Pass or Fail - Provide a specific 1-2 sentence assessment of this run
- Run 20250815225132920009: Pass or Fail - Provide a specific 1-2 sentence assessment of this run

## Recommendations
Write 2-3 specific, actionable improvements that would help the agent perform better on this task.

REMINDER: You MUST follow this EXACT format. Do not write a summary paragraph. Do not skip sections. Do not use placeholder text. Start your response with "## Overall Performance" and follow the format exactly.

---

# Judge Response

Now I have read all 10 evaluation runs. Let me provide the analysis in the exact format requested:

## Overall Performance
The agent successfully completed the Python REPL task in all 10 runs, achieving a 100% success rate. Every run correctly calculated all mathematical operations (42*17=714, factorial(10)=3628800), created the list comprehension, defined a prime-checking function, verified 97 is prime, and exited cleanly, though all runs struggled with multiline function indentation and resorted to lambda functions.

## What Went Well
- Successfully started Python REPL sessions using Screen in all runs
- Correctly calculated 42 * 17 = 714 in every run without issues
- Successfully imported math module and calculated factorial(10) = 3628800 in all runs
- Created the list comprehension [x**2 for x in range(10)] correctly in all runs
- Consistently adapted to indentation problems by switching to lambda functions
- Correctly identified 97 as prime (True) in all 10 runs
- Successfully exited the REPL cleanly using exit() in every run
- Proper use of Screen hardcopy functionality to verify output after each step

## What Went Wrong
- Multiline function definition with proper indentation failed in every single run due to Screen's handling of Python's interactive indentation
- Initial attempts to define the prime function using traditional def syntax with multiple lines resulted in IndentationError in all runs
- Some runs had multiple failed attempts at multiline function definition before switching approaches
- Screen's handling of Python's interactive prompt indentation was problematic across all runs
- The workaround of using lambda functions, while successful, wasn't the intended line-by-line solution requested in the task

## Run-by-Run Analysis
- Run 20250815222817685000: Pass - Successfully completed all tasks but had to use lambda function after multiline indentation errors
- Run 20250815223033260001: Pass - Completed all requirements but encountered indentation issues, resolved with one-liner function definition
- Run 20250815223233828002: Pass - All tasks completed successfully, resorted to lambda function after indentation problems
- Run 20250815223509394003: Pass - Successfully finished all steps, multiple indentation attempts before using lambda approach
- Run 20250815223749968004: Pass - Completed all tasks correctly, switched to lambda after multiline definition failed
- Run 20250815224025535005: Pass - All requirements met, had extensive indentation troubles before lambda solution worked
- Run 20250815224331134006: Pass - Successfully completed, multiple failed multiline attempts before one-liner succeeded
- Run 20250815224636748007: Pass - All tasks finished correctly, indentation errors forced simplified function approach
- Run 20250815224932339008: Pass - Completed successfully but had wrong lambda initially (missing != 0), corrected and verified
- Run 20250815225132920009: Pass - All tasks completed, consistent indentation issues resolved with lambda function

## Recommendations
Screen's handling of Python's interactive indentation should be addressed by either using expect scripts to properly manage the continuation prompts, or by writing the function to a temporary file and importing it. The agent should be trained to recognize indentation issues earlier and switch to alternative approaches like heredoc strings or using screen's paste buffer functionality. Consider implementing a fallback strategy that detects IndentationError patterns and automatically switches to single-line definitions or file-based function definitions.