/lop_eval_3__file_operations_test
- Pass the prompt into each nano-agent AS IS, replacing MODEL_NAME with the actual model name. - Each agent works in isolation by using their unique model name in file paths.
How it fires
How this command gets triggered: by you, by Claude, or both.
- Fires itselfClaude auto-loads it when your prompt matches the work.
- You can call itInvoke it directly when you want it.
- Slash command
/lop_eval_3__file_operations_test
Context preview
What this command does when you run it.
- Pass the prompt into each nano-agent AS IS, replacing MODEL_NAME with the actual model name. - Each agent works in isolation by using their unique model name in file paths.
Command definition
lop_eval_3__file_operations_test.mdProblem #3: File Operations Test
Instructions
- Pass the prompt into each nano-agent AS IS, replacing MODEL_NAME with the actual model name.
- Each agent works in isolation by using their unique model name in file paths.
Variables
PROMPT: "Complete the following tasks: 1. Read the .claude/settings.json file and extract all unique hook names (the keys of the 'hooks' object). 2. Create a file called 'summary_MODEL_NAME.json' (replace MODEL_NAME with your actual model name) with the following content: { 'model': 'MODEL_NAME', 'hook_names': [<array of all unique hook names you found>], 'test_status': 'completed' } 3. Create another file called 'signature_MODEL_NAME.txt' with the content: 'MODEL_NAME was here - Successfully completed file operations test' 4. List the current directory to show your created files exist.
Respond with your entire JSON response structure as is."
Agents
IMPORTANT: You're calling the respective claude code sub agents - do not call the `mcp__nano-agent__prompt_nano_agent` tool directly, let the sub agent's handle that.
@agent-nano-agent-gpt-5-nano PROMPT @agent-nano-agent-gpt-5-mini PROMPT @agent-nano-agent-gpt-5 PROMPT @agent-nano-agent-claude-opus-4-1 PROMPT @agent-nano-agent-claude-opus-4 PROMPT @agent-nano-agent-claude-sonnet-4 PROMPT @agent-nano-agent-claude-3-haiku PROMPT @agent-nano-agent-gpt-oss-20b PROMPT @agent-nano-agent-gpt-oss-120b PROMPT
Expected Output
Verify each agent created their uniquely named files and that the contents match the specifications.
IMPORTANT: All agents must will respond with this JSON structure. Don't change the structure or add any additional fields. Output it as the given structure as raw JSON for each agent with no preamble.
{
"success": true,
"result": "<summary of files created and directory listing>",
"error": null,
"metadata": {
...keep all fields given
},
"execution_time_seconds": X.XX
}Grading rubric
- Did the agent correctly extract all unique hook names from .claude/settings.json? ["PreToolUse", "PostToolUse", "Notification", "Stop", "SubagentStop", "PreCompact", "UserPromptSubmit", "SessionStart"]
- Did the agent create both files with correct JSON structure and content?
- Did the agent use unique file names to avoid conflicts with other agents?
- Did the agent complete all 4 tasks successfully?
Read more
Problem #3: File Operations Test
Instructions
- Pass the prompt into each nano-agent AS IS, replacing MODEL_NAME with the actual model name.
- Each agent works in isolation by using their unique model name in file paths.
Variables
PROMPT: "Complete the following tasks: 1. Read the .claude/settings.json file and extract all unique hook names (the keys of the 'hooks' object). 2. Create a file called 'summary_MODEL_NAME.json' (replace MODEL_NAME with your actual model name) with the following content: { 'model': 'MODEL_NAME', 'hook_names': [<array of all unique hook names you found>], 'test_status': 'completed' } 3. Create another file called 'signature_MODEL_NAME.txt' with the content: 'MODEL_NAME was here - Successfully completed file operations test' 4. List the current directory to show your created files exist.
Respond with your entire JSON response structure as is."
Agents
IMPORTANT: You're calling the respective claude code sub agents - do not call the `mcp__nano-agent__prompt_nano_agent` tool directly, let the sub agent's handle that.
@agent-nano-agent-gpt-5-nano PROMPT @agent-nano-agent-gpt-5-mini PROMPT @agent-nano-agent-gpt-5 PROMPT @agent-nano-agent-claude-opus-4-1 PROMPT @agent-nano-agent-claude-opus-4 PROMPT @agent-nano-agent-claude-sonnet-4 PROMPT @agent-nano-agent-claude-3-haiku PROMPT @agent-nano-agent-gpt-oss-20b PROMPT @agent-nano-agent-gpt-oss-120b PROMPT
Expected Output
Verify each agent created their uniquely named files and that the contents match the specifications.
IMPORTANT: All agents must will respond with this JSON structure. Don't change the structure or add any additional fields. Output it as the given structure as raw JSON for each agent with no preamble.
{
"success": true,
"result": "<summary of files created and directory listing>",
"error": null,
"metadata": {
...keep all fields given
},
"execution_time_seconds": X.XX
}Grading rubric
- Did the agent correctly extract all unique hook names from .claude/settings.json? ["PreToolUse", "PostToolUse", "Notification", "Stop", "SubagentStop", "PreCompact", "UserPromptSubmit", "SessionStart"]
- Did the agent create both files with correct JSON structure and content?
- Did the agent use unique file names to avoid conflicts with other agents?
- Did the agent complete all 4 tasks successfully?
Watched how we used GPT-5 and Claude Code with nano-agents here. What? A MCP Server for experimental, small scale engineering agents with multi-provider LLM support. Why?
Other commands on nano-agent.
- /build
Implement a task directly without creating a plan first.
Open command - /convert_paths_absolute
Converts relative paths in .claude/settings.json command scripts to absolute paths
Open command - /convert_paths_relative
Converts absolute paths in .claude/settings.json command scripts to relative paths
Open command - /create_worktree
Create a new git worktree for an agent to work in isolation.
Open command - /hop_evaluate_nano_agents
Using the nano-agent mcp server, execute the following nano agents with their respective prompts, models, and providers then rank the results based on the `Response Format`.
Open command - /lop_eval_1__dummy_test
- Pass the prompt into each nano-agent AS IS. Do not change the prompt in any way.
Open command

