Structure of the Reviewer Agent's Prompt in the AI Job Search Repository

The reviewer agent's prompt follows a modular five-section architecture defined in .claude/agents/gemini-research-expert.md, consisting of YAML front-matter, tool specifications, research methodology, operational guidelines, and communication protocols that dynamically inject CV and cover letter drafts for inline analysis.

The reviewer agent operates as the primary quality assurance mechanism within the /apply workflow of the MadsLorentzen/ai-job-search repository. Its prompt structure is designed to enforce rigorous research standards while maintaining token efficiency by passing draft content inline rather than through external file references.

Prompt File Location and Architecture

The complete prompt definition resides in .claude/agents/gemini-research-expert.md within the repository's Claude framework configuration. When the /apply command executes, the framework spawns the reviewer by injecting this prompt inline along with the current LaTeX drafts of the CV and cover letter. This design eliminates redundant file I/O operations and ensures the agent receives a complete, self-contained context for immediate analysis.

The prompt employs plain-text Markdown-compatible formatting that the framework parses and submits to the underlying model (configured as sonnet in the front-matter).

Five Core Sections of the Reviewer Agent Prompt

Front-Matter Configuration

The prompt opens with a YAML front-matter block that declares the agent's identity and runtime parameters:

---
name: gemini-research-expert
description: Use this agent when the user needs to perform research tasks, gather information from external sources, or investigate topics that require web searches and synthesis of information.
model: sonnet
---

This header identifies the agent's purpose as a research specialist capable of external information gathering and synthesis, distinguishing it from other specialized agents in the workflow.

Your Primary Tool

The prompt specifies the exact command syntax the agent must use to execute research tasks:

gemini -p "your research prompt here"

This directive ensures consistent tool invocation across all research operations, requiring the agent to formulate focused queries and pass them through the Gemini CLI interface.

Your Research Methodology

This section establishes a four-phase workflow that governs every research request:

1. Prompt Formulation – Instructions for crafting focused Gemini prompts with specificity, context requirements, desired output formats, citation standards, and scope limitations.

2. Research Execution – Exact command syntax requirements including mandatory components: clear questions, evaluation criteria, and constraint definitions.

3. Information Synthesis – Protocols for verifying relevance, organizing findings hierarchically, documenting information gaps, and flagging methodological caveats.

4. Quality Assurance – Cross-referencing procedures to verify facts against multiple sources, distinguishing between established facts and emerging trends, noting data recency, and identifying potential bias in sources.

Operational Guidelines

The prompt imposes specific behavioral constraints during research execution:

• Explain the research strategy before executing any commands
• Use multiple targeted searches when single queries prove insufficient
• Refine prompts iteratively based on interim results
• Provide transparent context with all findings, explicitly stating limitations and confidence levels

These guidelines ensure the agent maintains methodological rigor while remaining adaptable to varying research complexities.

Communication Style

Final output standards require the agent to:

• Anticipate follow-up questions proactively
• Structure findings with clear hierarchical organization
• Evaluate source reliability critically
• Maintain efficiency by eliminating redundant information

Dynamic Content Injection

The framework modifies the base prompt at runtime by appending the candidate's current drafts. The implementation follows this pattern:

review_prompt = f"""
--- 
name: gemini-research-expert
description: Researcher for company background and draft critique
model: sonnet
---

You are an elite Research Expert ...

# Drafts

CV_DRAFT:
{cv_tex_string}

COVER_LETTER_DRAFT:
{cover_letter_tex_string}
"""

agent = AgentTool(prompt=review_prompt)
feedback = agent.run()

This inline injection strategy ensures the reviewer analyzes the most current iteration of application materials without requiring separate file read operations, significantly reducing token consumption and latency.

Feedback Format and Integration

Upon completing analysis, the reviewer returns structured JSON feedback that the /apply command processes:

{
  "edits": [
    {
      "file": "cv/main_example.tex",
      "old_string": "Old bullet point text",
      "new_string": "Rewritten bullet point with stronger metrics"
    }
  ],
  "suggestions": [
    "Add a sentence about the company's recent expansion into renewable energy.",
    "Re-phrase the motivation paragraph to align with the firm's sustainability values."
  ]
}

The edits array contains precise string replacements for automated application, while the suggestions array provides strategic recommendations for manual review. This bifurcation allows the framework to apply technical corrections automatically while flagging substantive content improvements for human approval.

Related Configuration Files

Several ancillary files constrain and extend the reviewer agent's functionality:

• .claude/skills/job-application-assistant/03-writing-style.md – Prohibits unverified company claims and establishes tone requirements that the reviewer must enforce
• .claude/skills/job-application-assistant/04-job-evaluation.md – Defines the broader evaluation workflow where the reviewer's research feeds into final application scoring
• .claude/commands/apply.md – Orchestrates the reviewer spawning process and handles the JSON feedback integration

Summary

• The reviewer agent prompt is defined in .claude/agents/gemini-research-expert.md using a five-section modular architecture
• Front-matter declares the agent identity as gemini-research-expert using the sonnet model
• Primary Tool specifies the gemini -p command syntax for all research operations
• Research Methodology enforces a four-phase workflow: formulation, execution, synthesis, and quality assurance
• Dynamic injection passes CV and cover letter drafts inline to minimize token usage and eliminate file I/O
• JSON output provides structured edits for automated application and suggestions for manual review

Frequently Asked Questions

Where is the reviewer agent prompt defined in the AI Job Search repository?

The complete prompt structure is defined in .claude/agents/gemini-research-expert.md. This file contains the YAML front-matter, tool specifications, methodology guidelines, and communication protocols that govern the agent's behavior during the /apply workflow.

How does the reviewer agent receive the CV and cover letter content?

The framework passes draft content inline at runtime by appending CV_DRAFT and COVER_LETTER_DRAFT variables to the base prompt. This injection occurs in .claude/commands/apply.md, ensuring the agent analyzes current document states without performing additional file reads.

What research command does the reviewer agent use?

The prompt instructs the agent to execute all research using the syntax gemini -p "your research prompt here". This standardized command ensures consistent external information gathering across all review operations.

What format does the reviewer agent return for feedback?

The agent returns structured JSON containing two primary keys: edits (an array of objects specifying file paths, old strings, and replacement strings for automated corrections) and suggestions (an array of strings describing strategic improvements requiring manual implementation).

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}