Compare how different tools performed on the project-analysis task for claude-code.

You have already judged each tool individually. Here are the summaries with metrics:


### screen
**Metrics:**
- Runs: 10
- Success Rate: 0.0%
- Total Cost: $14.7400 (avg: $1.4740)
- Total Duration: 35m 47.2s (avg: 3m 34.7s)
- Total Tokens: 731,828 in, 60,300 out, 30,300,000 cache read, 1,152,600 cache write

**Judge Notes:**
Now let me analyze the overall patterns. Based on the files I've read so far, I can see clear patterns emerging. Let me write the comprehensive analysis:

## Overall Performance
The agent consistently failed to complete the project analysis task across all 10 runs, with the primary failure point being the inability to successfully switch models in opencode. While the agent demonstrated proficiency with Screen tool commands and understood the task requirements, the model switching functionality in opencode v0.5.3 proved to be a blocking issue that prevented successful completion of any run.

## What Went Well
- Agent correctly interpreted task requirements and understood the need to switch models before proceeding with analysis
- Demonstrated strong command of Screen tool usage including session management, hardcopy workarounds, and input sending
- Successfully started opencode with specified initial models in all runs
- Properly followed task instructions to report failure when model switching was unsuccessful
- Showed good troubleshooting attempts including trying multiple approaches (slash commands, keyboard shortcuts, search filtering)
- Correctly managed Screen sessions with proper cleanup using quit commands

## What Went Wrong
- Model switching in opencode consistently failed across all runs despite multiple attempts
- The /models dialog either didn't open or didn't respond to selection attempts
- Agent sometimes had difficulty with keyboard shortcuts (e.g., ctrl+x m adding extra 'm' character)
- Autocomplete suggestions interfered with command execution in some cases
- The models dialog when opened did not respond to Tab navigation or Enter key selection
- Agent encountered timeout issues in several runs when tasks exceeded 300 second limit
- Some confusion with escape sequences and special character handling in Screen

## Run-by-Run Analysis
- Run 20250815234106284000: Fail - Successfully opened models dialog but could not complete model switch from Groq to Claude Sonnet 4, properly reported failure
- Run 20250816001106562000: Fail - Model switching worked but GPT OSS 120B model did not respond to queries, task timed out after 300 seconds
- Run 20250816001612201001: Fail - Unable to switch from Claude Sonnet 4 to GPT OSS 120B despite multiple navigation attempts in models dialog
- Run 20250816002032791002: Fail - Models dialog would not open properly, attempted both /models command and keyboard shortcuts without success
- Run 20250816002403382003: Fail - Similar pattern of models dialog not opening or responding to input commands
- Run 20250816002643959004: Fail - Managed to open models dialog but selection mechanism did not work, tried Tab navigation without success
- Run 20250816003054564005: Fail - Models command appeared to execute but dialog did not open, multiple retry attempts unsuccessful
- Run 20250816003500152006: Fail - Typed /models command correctly but interface did not respond, tried keyboard shortcuts as fallback
- Run 20250816003740714007: Fail - Autocomplete interference with /models command, dialog eventually opened but selection failed
- Run 20250816004011318008: Fail - Restarted opencode session to troubleshoot but still could not get models dialog to function properly

## Recommendations
Implement a more robust model switching mechanism that doesn't rely on interactive UI elements, such as command-line flags or configuration files for model selection. Add explicit wait times and verification steps after each UI interaction to ensure commands are processed before proceeding. Consider implementing alternative methods for interacting with TUI applications beyond Screen's stuff command, such as using expect scripts for more reliable dialog navigation.


### terminalcp
**Metrics:**
- Runs: 10
- Success Rate: 100.0%
- Total Cost: $6.7095 (avg: $0.6710)
- Total Duration: 16m 52.0s (avg: 1m 41.2s)
- Total Tokens: 15,086 in, 33,071 out, 14,205,400 cache read, 521,800 cache write

**Judge Notes:**
## Overall Performance
The agent achieved a perfect 100% success rate across all 10 runs, successfully starting opencode, switching from Claude Sonnet 4 to GPT OSS 120B, and capturing complete project analyses. The terminalcp tool demonstrated excellent reliability with agents adapting their capture techniques (scrolling, stream output, ANSI stripping) to ensure full response retrieval.

## What Went Well
- All 10 runs successfully started opencode with the correct initial model (Claude Sonnet 4)
- Model switching to GPT OSS 120B worked flawlessly in every run using the /models command
- Agents consistently captured complete responses, learning to use stream output for more efficient capture in later runs
- The terminalcp tool maintained stable sessions without crashes or disconnections
- GPT OSS 120B provided consistently high-quality, comprehensive project analyses covering architecture, features, and use cases
- Agents properly cleaned up sessions by stopping them after task completion
- Response capture evolved from basic scrolling to more sophisticated stream output with ANSI stripping

## What Went Wrong
- Initial runs required multiple scrolling attempts to capture complete responses before agents discovered stream output
- Some runs captured ANSI escape codes in output requiring later runs to apply stripping for cleaner results
- Agents occasionally issued redundant commands when unsure if response was complete
- Minor inefficiencies in early runs with agents using multiple methods to verify complete capture

## Run-by-Run Analysis
- Run 20250816004932691000: Pass - Successfully completed the task with opencode starting, model switching to GPT OSS 120B (OpenRouter), and full response capture
- Run 20250816005058147001: Pass - Completed successfully with model switch to GPT OSS 120B (Groq) and comprehensive response captured through scrolling
- Run 20250816005323624002: Pass - Task completed with successful model switching and thorough project analysis captured via proper scrolling techniques
- Run 20250816005509085003: Pass - Successfully switched models and captured complete analysis using multiple scrolling techniques to ensure full coverage
- Run 20250816005634541004: Pass - Completed task efficiently using stream output to capture GPT OSS 120B's detailed project analysis with architecture details
- Run 20250816005845017005: Pass - Successfully executed with stream output capturing comprehensive analysis including component table from GPT OSS 120B
- Run 20250816010000453006: Pass - Task completed using stream output with ANSI stripping for clean capture of full project analysis
- Run 20250816010135902007: Pass - Successfully completed with systematic scrolling through response to ensure complete capture of analysis
- Run 20250816010401351008: Pass - Efficiently completed using stream output to capture comprehensive project analysis from GPT OSS 120B
- Run 20250816010511768009: Pass - Task completed successfully with stream output and ANSI stripping providing cleanest capture of complete analysis

## Recommendations
Agents should prioritize stream output mode over scrolling for capturing long responses as it's more efficient and ensures complete capture without repeated attempts. Implementing ANSI stripping by default when using stream output would provide cleaner, more readable results without escape codes. Consider adding a response completion detection mechanism to avoid redundant verification commands and improve efficiency.


### terminalcp-cli
**Metrics:**
- Runs: 10
- Success Rate: 100.0%
- Total Cost: $7.9100 (avg: $0.7910)
- Total Duration: 24m 12.0s (avg: 2m 25.2s)
- Total Tokens: 452,540 in, 51,611 out, 16,800,000 cache read, 497,600 cache write

**Judge Notes:**
Now I have read all the evaluation files. Let me provide the analysis in the exact format requested:

## Overall Performance
The agent performed excellently across all 10 runs, achieving a 100% success rate with all runs completing the task successfully. The agent consistently demonstrated proper model switching from Claude Sonnet to GPT OSS 120B and effectively captured comprehensive analysis responses using scrolling techniques.

## What Went Well
- All 10 runs successfully switched from Claude Sonnet 4 to GPT OSS 120B Groq using the /models command
- Systematic scrolling approach using PageUp/PageDown commands effectively captured complete responses in all runs
- The terminalcp CLI tool worked reliably for controlling opencode sessions across all runs
- GPT OSS 120B consistently provided accurate and detailed analysis of the terminalcp project
- Agent demonstrated good adaptation when responses appeared truncated, using multiple scrolling attempts to capture full content
- Proper session cleanup with stop command executed in all runs
- Cost efficiency maintained with runs ranging from $0.61 to $0.97

## What Went Wrong
- Some runs experienced difficulty capturing the complete response initially due to viewport limitations
- Run 007 noted the response seemed incomplete or truncated initially
- Several runs required multiple scrolling attempts to capture the full analysis
- Response quality from GPT OSS 120B varied between runs, with some providing more comprehensive analysis than others
- The agent sometimes had to use multiple stdout commands with different line counts to ensure full capture

## Run-by-Run Analysis
- Run 20250816001123989000: Pass - Successfully switched models and captured comprehensive analysis of terminalcp as a "Playwright-for-the-terminal" with complete feature overview.
- Run 20250816001349939001: Pass - Completed task with excellent scrolling technique and captured detailed analysis covering all major aspects of the project.
- Run 20250816001625954002: Pass - Successfully executed with comprehensive response capture including architecture, capabilities, and use cases.
- Run 20250816001856921003: Pass - Systematic approach yielded detailed analysis with clear explanations of MCP usage and CLI usage.
- Run 20250816002137877004: Pass - Achieved the most comprehensive response among all runs with extensive, well-structured analysis.
- Run 20250816002433850005: Pass - Completed successfully though noted initial brevity in response, still captured solid analysis.
- Run 20250816002719827006: Pass - Efficient execution with concise but complete analysis covering key features and use cases.
- Run 20250816002930804007: Pass - Completed task though noted initial response was very brief, still provided adequate summary.
- Run 20250816003146747008: Pass - Successfully completed despite acknowledged viewport limitations, captured solid technical analysis.
- Run 20250816003402704009: Pass - Fastest completion at 1m 49s with focused analysis on practical aspects like debugging and REPL control.

## Recommendations
Implement a more robust scrolling strategy that automatically detects when the full response hasn't been captured and continues scrolling until the complete content is visible. Add explicit verification steps after model switching to confirm the correct model is active before proceeding with the analysis request. Consider implementing a buffering mechanism that waits longer for GPT OSS 120B to complete its response before attempting to capture output, as some runs noted responses still being generated when first checked.


### tmux
**Metrics:**
- Runs: 10
- Success Rate: 100.0%
- Total Cost: $11.0500 (avg: $1.1050)
- Total Duration: 35m 2.1s (avg: 3m 30.2s)
- Total Tokens: 675,260 in, 70,000 out, 25,100,000 cache read, 567,900 cache write

**Judge Notes:**
## Overall Performance
The agent achieved a 100% success rate across all 10 runs, successfully starting opencode, switching to GPT OSS 120B model, and capturing comprehensive project analyses. While some runs struggled with capturing the complete response due to tmux viewport limitations, all runs ultimately delivered the required analysis demonstrating strong adaptability and problem-solving skills.

## What Went Well
- Successfully started opencode with the correct model (anthropic/claude-sonnet-4-20250514) in all runs
- Consistently navigated the model switching interface and selected GPT OSS 120B without errors
- Effectively used tmux sessions with proper naming and cleanup across all runs
- Successfully prompted the model with the analysis request in every run
- Demonstrated adaptive problem-solving when facing response capture challenges (e.g., Run 9 using multiple file saves)
- Maintained consistent high-quality analyses from GPT OSS 120B across all runs
- Used appropriate navigation commands (PageUp/PageDown) to scroll through long responses
- Properly exited opencode and cleaned up tmux sessions after completing tasks

## What Went Wrong
- Multiple runs struggled with capturing the complete response due to tmux viewport limitations
- Several runs had difficulty using `tmux capture-pane -S -` command to get full scrollback history
- Some runs experienced response truncation in their initial capture attempts requiring multiple scrolling attempts
- Run 4 had particular difficulty capturing the complete response, requiring extensive scrolling and multiple approaches
- Inconsistent approaches to response capture across runs (some used capture-pane, others manual scrolling)
- Some runs didn't initially realize the response was longer than the visible viewport

## Run-by-Run Analysis
- Run 20250816001052725000: Pass - Successfully completed task with comprehensive analysis capture including detailed features and architecture breakdown
- Run 20250816001443245001: Pass - Clean execution with successful model switch and full analysis capture of terminalcp capabilities
- Run 20250816001723754002: Pass - Methodically used PageUp/PageDown navigation to systematically capture the complete response with capabilities table
- Run 20250816002039247003: Pass - Despite initial capture difficulties, successfully compiled comprehensive summary through multiple scrolling attempts
- Run 20250816002429771004: Pass - Clean execution with systematic navigation resulting in concise but complete project overview
- Run 20250816002715247005: Pass - Thorough response capture with very detailed architecture breakdown and feature analysis
- Run 20250816003020730006: Pass - Successfully captured comprehensive response with detailed features table after multiple scrolling attempts
- Run 20250816003426236007: Pass - Systematic capture of entire response through extensive scrolling, resulting in very comprehensive analysis with detailed tables
- Run 20250816003836759008: Pass - Innovative multi-section capture approach using multiple file saves to ensure complete response capture
- Run 20250816004307314009: Pass - Clean capture of response with comprehensive analysis and clear structure using standard workflow

## Recommendations
Implement a standardized response capture strategy such as immediately using `tmux capture-pane -p -S -3000` to get full scrollback history rather than relying on manual scrolling. Add a verification step after initial capture to check if the response appears truncated (looking for incomplete sentences or missing conclusions) before attempting to retrieve additional content. Consider using tmux's logging functionality (`tmux pipe-pane -o 'cat > output.txt'`) at the start of the session to automatically capture all terminal output without needing post-hoc scrolling.


CRITICAL: You MUST now provide a comparative analysis using EXACTLY this format (replace bracketed text with actual analysis):

## Tool Comparison for project-analysis

### Rankings
Rank the tools from best to worst based on success rate, efficiency, ease of use, and overall effectiveness:
1. [First place tool] - [brief reason why it's #1]
2. [Second place tool] - [brief reason why it's #2]  
3. [Third place tool] - [brief reason why it's #3]
4. [Fourth place tool] - [brief reason why it's #4]

### Best Tool: [actual tool name]
Write 2-3 sentences explaining specifically why this tool performed best for this task, referencing the metrics and judge notes above.

### Tool-by-Tool Analysis
- **screen**: Write 1-2 sentences comparing this tool's performance to the others
- **terminalcp**: Write 1-2 sentences comparing this tool's performance to the others
- **terminalcp-cli**: Write 1-2 sentences comparing this tool's performance to the others
- **tmux**: Write 1-2 sentences comparing this tool's performance to the others

### Key Insights
Write 2-3 sentences about what patterns emerged across tools for this specific task. What made some tools succeed where others failed?

### Recommendation
Write 2-3 sentences with a clear recommendation of which tool should be preferred for this task and why, considering both performance and practical factors.

REMINDER: Start your response with "## Tool Comparison for project-analysis" and follow this EXACT format. Do not write any introductory text.

---

# Judge Response

## Tool Comparison for project-analysis

### Rankings
Rank the tools from best to worst based on success rate, efficiency, ease of use, and overall effectiveness:
1. terminalcp - 100% success rate with fastest average runtime (1m 41.2s) and lowest cost ($0.67/run)
2. terminalcp-cli - 100% success rate with efficient scrolling-based capture and good cost efficiency ($0.79/run)
3. tmux - 100% success rate but struggled with response capture, higher runtime (3m 30.2s) and cost ($1.11/run)
4. screen - 0% success rate due to consistent model switching failures, highest cost ($1.47/run)

### Best Tool: terminalcp
The terminalcp tool achieved perfect reliability with the fastest average runtime and lowest costs, while agents evolved to use sophisticated stream output capture with ANSI stripping for clean results. Its stable sessions and efficient capture methods (especially stream output mode) made it the most effective tool for this task, avoiding the scrolling inefficiencies and viewport limitations that plagued other tools.

### Tool-by-Tool Analysis
- **screen**: Failed completely with 0% success rate due to inability to switch models in opencode, despite demonstrating good command proficiency and troubleshooting attempts, making it unsuitable for this task.
- **terminalcp**: Achieved perfect success with the best efficiency metrics, evolving from basic scrolling to stream output capture, providing the cleanest and most reliable results at the lowest cost.
- **terminalcp-cli**: Performed excellently with 100% success using systematic scrolling approaches, though required more manual effort and time compared to terminalcp's stream output capabilities.
- **tmux**: While achieving 100% success, struggled significantly with response capture due to viewport limitations, requiring extensive scrolling and multiple capture attempts, resulting in higher costs and longer runtimes.

### Key Insights
The critical differentiator was the ability to successfully switch models in opencode - screen's complete failure here made it unusable despite good technical command of the tool itself. Among successful tools, those with better output capture mechanisms (terminalcp's stream output vs manual scrolling) achieved superior efficiency and cleaner results, while viewport limitations in tmux created unnecessary complexity.

### Recommendation
The terminalcp tool should be strongly preferred for this task due to its combination of perfect reliability, fastest execution time, lowest cost, and cleanest output capture through stream mode with ANSI stripping. While terminalcp-cli and tmux can complete the task, they require significantly more time and manual effort, making terminalcp the clear choice for production use where efficiency and cost matter.