Dual-AI Consultation: z.ai GLM 5.2 vs Code-Searcher

You orchestrate consultation between z.ai's GLM 5.2 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 5.2: Use a temp file to avoid shell quoting issues.

Step 0 — run-scope the filename and sweep stale prompts. Pick a unique RUN_ID for this run (e.g. a timestamp + short random token such as 20260616-a3f1) and use the literal filename zai-prompt-RUN_ID.txt (with RUN_ID substituted) in Steps 1–2 and in §3 cleanup. Run-scoping prevents a concurrent consult-zai run from truncating this run's prompt. First sweep any orphans left by interrupted past runs: bash mkdir -p "$CLAUDE_PROJECT_DIR/tmp" find "$CLAUDE_PROJECT_DIR/tmp" -maxdepth 1 -name 'zai-prompt-*.txt' -mmin +60 -delete 2>/dev/null

Step 1: Write the enhanced prompt to the temp file using the Write tool: Write to $CLAUDE_PROJECT_DIR/tmp/zai-prompt-RUN_ID.txt with the ENHANCED_PROMPT content

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

macOS: bash zsh -i -c 'zai -p "$(cat "$CLAUDE_PROJECT_DIR/tmp/zai-prompt-RUN_ID.txt")" --output-format json --append-system-prompt "You are GLM 5.2 model accessed via z.ai API." 2>&1'

Linux: bash bash -i -c 'zai -p "$(cat "$CLAUDE_PROJECT_DIR/tmp/zai-prompt-RUN_ID.txt")" --output-format json --append-system-prompt "You are GLM 5.2 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:

bash
rm -f "$CLAUDE_PROJECT_DIR/tmp/zai-prompt-RUN_ID.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 5.2) Response

Raw output from zai-cli agent]

Code-Searcher (Claude) Response

Raw output from code-searcher agent]

Comparison Table

(MANDATORY — always render this table on a multi-agent run; it is the at-a-glance visual diff readers rely on, so never skip it. Omit only in a degraded single-AI run, where there is nothing to compare.)

| Aspect | z.ai (GLM 5.2) | 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 5.2: 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]