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

The runs are located in evaluation-results/ with filenames:
evaluation-results/claude-code--python-repl--terminalcp--20250815223546723000-prompt.md
evaluation-results/claude-code--python-repl--terminalcp--20250815223546723000-scrollbuffer.txt
evaluation-results/claude-code--python-repl--terminalcp--20250815223752205001-prompt.md
evaluation-results/claude-code--python-repl--terminalcp--20250815223752205001-scrollbuffer.txt
evaluation-results/claude-code--python-repl--terminalcp--20250815224002688002-prompt.md
evaluation-results/claude-code--python-repl--terminalcp--20250815224002688002-scrollbuffer.txt
evaluation-results/claude-code--python-repl--terminalcp--20250815224203166003-prompt.md
evaluation-results/claude-code--python-repl--terminalcp--20250815224203166003-scrollbuffer.txt
evaluation-results/claude-code--python-repl--terminalcp--20250815224408653004-prompt.md
evaluation-results/claude-code--python-repl--terminalcp--20250815224408653004-scrollbuffer.txt
evaluation-results/claude-code--python-repl--terminalcp--20250815224604131005-prompt.md
evaluation-results/claude-code--python-repl--terminalcp--20250815224604131005-scrollbuffer.txt
evaluation-results/claude-code--python-repl--terminalcp--20250815224804624006-prompt.md
evaluation-results/claude-code--python-repl--terminalcp--20250815224804624006-scrollbuffer.txt
evaluation-results/claude-code--python-repl--terminalcp--20250815225005130007-prompt.md
evaluation-results/claude-code--python-repl--terminalcp--20250815225005130007-scrollbuffer.txt
evaluation-results/claude-code--python-repl--terminalcp--20250815225200603008-prompt.md
evaluation-results/claude-code--python-repl--terminalcp--20250815225200603008-scrollbuffer.txt
evaluation-results/claude-code--python-repl--terminalcp--20250815225421140009-prompt.md
evaluation-results/claude-code--python-repl--terminalcp--20250815225421140009-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 20250815223546723000: Pass or Fail - Provide a specific 1-2 sentence assessment of this run
- Run 20250815223752205001: Pass or Fail - Provide a specific 1-2 sentence assessment of this run
- Run 20250815224002688002: Pass or Fail - Provide a specific 1-2 sentence assessment of this run
- Run 20250815224203166003: Pass or Fail - Provide a specific 1-2 sentence assessment of this run
- Run 20250815224408653004: Pass or Fail - Provide a specific 1-2 sentence assessment of this run
- Run 20250815224604131005: Pass or Fail - Provide a specific 1-2 sentence assessment of this run
- Run 20250815224804624006: Pass or Fail - Provide a specific 1-2 sentence assessment of this run
- Run 20250815225005130007: Pass or Fail - Provide a specific 1-2 sentence assessment of this run
- Run 20250815225200603008: Pass or Fail - Provide a specific 1-2 sentence assessment of this run
- Run 20250815225421140009: 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

## Overall Performance
The agent achieved a 100% success rate across all 10 runs, successfully completing all 6 steps of the Python REPL task despite consistently encountering indentation errors when defining multi-line functions. All runs demonstrated excellent adaptability by switching to one-liner function definitions when the traditional multi-line approach failed.

## What Went Well
- Successfully calculated 42 * 17 = 714 in all runs without any issues
- Correctly imported math module and calculated math.factorial(10) = 3628800 in every run
- Generated the correct list comprehension [0, 1, 4, 9, 16, 25, 36, 49, 64, 81] consistently
- Demonstrated excellent error recovery by switching to one-liner functions when indentation failed
- Successfully identified 97 as prime in all runs using the adapted function
- Cleanly exited the REPL using exit() in every run
- Maintained consistent performance with completion times between 1m 45s and 2m 20s

## What Went Wrong
- Terminalcp systematically failed to handle multi-line Python function definitions with proper indentation in 100% of runs
- Initial attempts to define the is_prime function line by line with proper indentation always resulted in IndentationError
- Terminal output truncation with "… +N lines (ctrl+r to expand)" may have limited agent visibility
- Agent had to use workaround one-liner functions instead of properly formatted multi-line code
- The indentation handling issue appears to be a fundamental limitation of the terminalcp tool

## Run-by-Run Analysis
- Run 20250815223546723000: Pass - Successfully completed all tasks after recovering from indentation error by using one-liner function definition
- Run 20250815223752205001: Pass - Completed all steps successfully, adapted to indentation issue with compact function syntax
- Run 20250815224002688002: Pass - Achieved all objectives despite initial indentation failure, used one-liner recovery strategy
- Run 20250815224203166003: Pass - Successfully finished all tasks, handled indentation error with single-line function approach
- Run 20250815224408653004: Pass - Completed all requirements after switching from multi-line to one-liner function due to indentation issues
- Run 20250815224604131005: Pass - All tasks successful, recovered from indentation error using lambda expression instead of def
- Run 20250815224804624006: Pass - Successfully completed all steps, adapted to terminal indentation limitations with compact syntax
- Run 20250815225005130007: Pass - Achieved all goals despite indentation challenges, used one-liner function workaround
- Run 20250815225200603008: Pass - All tasks completed successfully after recovering from multi-line function indentation failure
- Run 20250815225421140009: Pass - Successfully finished all requirements, handled indentation issue with single-line function definition

## Recommendations
Fix the terminalcp tool's handling of multi-line indented Python code by properly preserving and transmitting whitespace characters, particularly for REPL environments where indentation is critical for syntax. 
Implement better detection and handling of truncated terminal output to ensure the agent has full visibility of command results and error messages.
Consider adding a fallback mechanism or explicit documentation about known limitations with multi-line code entry in REPL environments to help agents proactively avoid these issues.