Dual-AI Consultation: z.ai GLM 4.7 vs Code-Searcher

You orchestrate consultation between z.ai's GLM 4.7 model and Claude's code-searcher to provide comprehensive analysis with comparison.

When to Use This Skill

High value queries: - Complex code analysis requiring multiple perspectives - Debugging difficult issues - Architecture/design questions - Code review requests - Finding specific implementations across a codebase

Lower value (single AI may suffice): - Simple syntax questions - Basic file lookups - Straightforward documentation queries

Workflow

When the user asks a code question:

1. Build Enhanced Prompt

Wrap the user's question with structured output requirements:

[USER_QUESTION]

=== Analysis Guidelines ===

**Structure your response with:**
1. **Summary:** 2-3 sentence overview
2. **Key Findings:** bullet points of discoveries
3. **Evidence:** file paths with line numbers (format: `file:line` or `file:start-end`)
4. **Confidence:** High/Medium/Low with reasoning
5. **Limitations:** what couldn't be determined

**Line Number Requirements:**
- ALWAYS include specific line numbers when referencing code
- Use format: `path/to/file.ext:42` or `path/to/file.ext:42-58`
- For multiple references: list each with its line number
- Include brief code snippets for key findings

**Examples of good citations:**
- "The authentication check at `src/auth/validate.ts:127-134`"
- "Configuration loaded from `config/settings.json:15`"
- "Error handling in `lib/errors.ts:45, 67-72, 98`"

2. Invoke Both Analyses in Parallel

Launch both simultaneously in a single message with multiple tool calls:

• For z.ai GLM 4.7: Use Bash tool directly (NOT Task with zai-cli agent - the agent intercepts queries):

macOS: bash zsh -i -c "zai -p 'ENHANCED_PROMPT' --output-format json --append-system-prompt 'You are GLM 4.7 model accessed via z.ai API.'"

Linux: bash bash -i -c "zai -p 'ENHANCED_PROMPT' --output-format json --append-system-prompt 'You are GLM 4.7 model accessed via z.ai API.'"

Replace ENHANCED_PROMPT with the actual prompt (escape single quotes as '\'').

• For Code-Searcher: Use Task tool with subagent_type: "code-searcher" with the same enhanced prompt

This parallel execution significantly improves response time.

3. Handle Errors

• If one agent fails or times out, still present the successful agent's response
• Note the failure in the comparison: "Agent X failed to respond: [error message]"
• Provide analysis based on the available response

4. Create Comparison Analysis

Use this exact format:

z.ai (GLM 4.7) Response

[Raw output from zai-cli agent]

Code-Searcher (Claude) Response

[Raw output from code-searcher agent]

Comparison Table

Agreement Level

• High Agreement: Both AIs reached similar conclusions - Higher confidence in findings
• Partial Agreement: Some overlap with unique findings - Investigate differences
• Disagreement: Contradicting findings - Manual verification recommended

[State which level applies and explain]

Key Differences

• z.ai GLM 4.7: [unique findings, strengths, approach]
• Code-Searcher: [unique findings, strengths, approach]

Synthesized Summary

[Combine the best insights from both sources into unified analysis. Prioritize findings that are: 1. Corroborated by both agents 2. Supported by specific file:line citations 3. Include verifiable code snippets]

Recommendation

[Which source was more helpful for this specific query and why. Consider: - Accuracy of file paths and line numbers - Quality of code snippets provided - Completeness of analysis - Unique insights offered]