consult-zai

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 a temp file to avoid shell quoting issues:

  Step 1: Write the enhanced prompt to a temp file using the Write tool:

  Write to $CLAUDE_PROJECT_DIR/tmp/zai-prompt.txt with the ENHANCED_PROMPT content
  

  Step 2: Execute z.ai with the temp file:

  macOS:

  zsh -i -c 'zai -p "$(cat $CLAUDE_PROJECT_DIR/tmp/zai-prompt.txt)" --output-format json --append-system-prompt "You are GLM 4.7 model accessed via z.ai API." 2>&1'
  

  Linux:

  bash -i -c 'zai -p "$(cat $CLAUDE_PROJECT_DIR/tmp/zai-prompt.txt)" --output-format json --append-system-prompt "You are GLM 4.7 model accessed via z.ai API." 2>&1'
  

  This approach avoids all shell quoting issues regardless of prompt content.

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

This parallel execution significantly improves response time.

3. Cleanup Temp Files

After processing the z.ai response (success or failure), clean up the temp prompt file:

rm -f $CLAUDE_PROJECT_DIR/tmp/zai-prompt.txt

This prevents stale prompts from accumulating and avoids potential confusion in future runs.

4. 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

5. 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

Aspect	z.ai (GLM 4.7)	Code-Searcher (Claude)
File paths	[Specific/Generic/None]	[Specific/Generic/None]
Line numbers	[Provided/Missing]	[Provided/Missing]
Code snippets	[Yes/No + details]	[Yes/No + details]
Unique findings	[List any]	[List any]
Accuracy	[Note discrepancies]	[Note discrepancies]
Strengths	[Summary]	[Summary]

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]