/lop_eval_2__basic_read_test
- Pass the prompt into each nano-agent AS IS. Do not change the prompt in any way. - Each agent should execute the task independently.
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_2__basic_read_test
Context preview
What this command does when you run it.
- Pass the prompt into each nano-agent AS IS. Do not change the prompt in any way. - Each agent should execute the task independently.
Command definition
lop_eval_2__basic_read_test.mdProblem #2: Basic File Reading Test
Instructions
- Pass the prompt into each nano-agent AS IS. Do not change the prompt in any way.
- Each agent should execute the task independently.
Variables
PROMPT: "Read the README.md file. Provide exactly the first 10 lines and the last 10 lines of the file. Format your response as follows: FIRST 10 LINES: [lines here]
LAST 10 LINES: [lines here]
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
Read the first and last 10 lines of the README.md file yourself and grade the agents based on the `Grading rubric` below.
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": "<the formatted first and last 10 lines>",
"error": null,
"metadata": {
...keep all fields given
},
"execution_time_seconds": X.XX
}Grading rubric
- Did the agent correctly identify and return the first and last 10 lines? How close?
- Did the agent follow the specified format exactly? How well?
Read more
Problem #2: Basic File Reading Test
Instructions
- Pass the prompt into each nano-agent AS IS. Do not change the prompt in any way.
- Each agent should execute the task independently.
Variables
PROMPT: "Read the README.md file. Provide exactly the first 10 lines and the last 10 lines of the file. Format your response as follows: FIRST 10 LINES: [lines here]
LAST 10 LINES: [lines here]
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
Read the first and last 10 lines of the README.md file yourself and grade the agents based on the `Grading rubric` below.
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": "<the formatted first and last 10 lines>",
"error": null,
"metadata": {
...keep all fields given
},
"execution_time_seconds": X.XX
}Grading rubric
- Did the agent correctly identify and return the first and last 10 lines? How close?
- Did the agent follow the specified format exactly? How well?
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

