consult-zai
The consult-zai skill orchestrates parallel analysis of code questions by querying both z.ai's GLM 4.7 model and Claude's code-searcher tool, then comparing their responses for comprehensive dual-perspective code analysis. Use this skill for complex code questions, debugging difficult issues, architecture decisions, code reviews, or when finding specific implementations across a codebase where multiple AI perspectives add significant value beyond what a single analysis would provide.
git clone --depth 1 https://github.com/centminmod/my-claude-code-setup /tmp/consult-zai && cp -r /tmp/consult-zai/.claude/skills/consult-zai ~/.claude/skills/consult-zaiSKILL.md
# 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 $CLAUDE_PROJECT_DIR/tmp/zai-prompt.txt with the ENHANCED_PROMPT content ``` **Step 2:** Execute z.ai with the temp file: **macOS:** ```bash zsh -i -c 'zai -p "$(cat $CLAUDE_PROJECT_DIR/tmp/zai-prompt.txt)" --output-format json --append-system-prompt "You are GLM 4.7 model accessed via z.ai API." 2>&1' ``` **Linux:** ```bash bash -i -c 'zai -p "$(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: ```bash 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]
Use this agent for comprehensive codebase analysis, forensic examination, and detailed code mapping with optional Chain of Draft (CoD) methodology. Excels at locating specific functions, classes, and logic, security vulnerability analysis, pattern detection, architectural consistency verification, and creating navigable code reference documentation with exact line numbers. Examples: <example>Context: User needs to find authentication-related code in the project. user: "Where is the user authentication logic implemented?" assistant: "I'll use the code-searcher agent to locate authentication-related code in the codebase" <commentary>Since the user is asking about locating specific code, use the code-searcher agent to efficiently find and summarize authentication logic.</commentary></example> <example>Context: User wants to understand how a specific feature is implemented. user: "How does the license validation work in this system?" assistant: "Let me use the code-searcher agent to find and analyze the license validation implementation" <commentary>The user is asking about understanding specific functionality, so use the code-searcher agent to locate and summarize the relevant code.</commentary></example> <example>Context: User needs to find where a bug might be occurring. user: "I'm getting an error with the payment processing, can you help me find where that code is?" assistant: "I'll use the code-searcher agent to locate the payment processing code and identify potential issues" <commentary>Since the user needs to locate specific code related to an error, use the code-searcher agent to find and analyze the relevant files.</commentary></example> <example>Context: User requests comprehensive security analysis using Chain of Draft methodology. user: "Analyze the entire authentication system using CoD methodology for comprehensive security mapping" assistant: "I'll use the code-searcher agent with Chain of Draft mode for ultra-concise security analysis" <commentary>The user explicitly requests CoD methodology for comprehensive analysis, so use the code-searcher agent's Chain of Draft mode for efficient token usage.</commentary></example> <example>Context: User wants rapid codebase pattern analysis. user: "Use CoD to examine error handling patterns across the codebase" assistant: "I'll use the code-searcher agent in Chain of Draft mode to rapidly analyze error handling patterns" <commentary>Chain of Draft mode is ideal for rapid pattern analysis across large codebases with minimal token usage.</commentary></example>
Execute OpenAI Codex CLI (GPT-5.2) for code analysis. Use when you need Codex's GPT-5.2 perspective on code.
Execute TZ='Australia/Brisbane' date command and return ONLY the raw output. No formatting, headers, explanations, or parallel agents.
Use this agent proactively to synchronize memory bank documentation with actual codebase state, ensuring architectural patterns in memory files match implementation reality, updating technical decisions to reflect current code, aligning documentation with actual patterns, maintaining consistency between memory bank system and source code, and keeping all CLAUDE-*.md files accurately reflecting the current system state. Examples: <example>Context: Code has evolved beyond documentation. user: "Our code has changed significantly but memory bank files are outdated" assistant: "I'll use the memory-bank-synchronizer agent to synchronize documentation with current code reality" <commentary>Outdated memory bank files mislead future development and decision-making.</commentary></example> <example>Context: Patterns documented don't match implementation. user: "The patterns in CLAUDE-patterns.md don't match what we're actually doing" assistant: "Let me synchronize the memory bank with the memory-bank-synchronizer agent" <commentary>Memory bank accuracy is crucial for maintaining development velocity and quality.</commentary></example>
Use this agent when you need comprehensive UX/UI design guidance, including user experience optimization, premium interface design, scalable design systems, data visualization with Highcharts, or Tailwind CSS implementation. Examples: <example>Context: User is building a dashboard with complex data visualizations and wants to improve the user experience. user: 'I have a dashboard with multiple charts but users are getting confused by the layout and the data is hard to interpret' assistant: 'I'll use the ux-design-expert agent to analyze your dashboard UX and provide recommendations for better data visualization and user flow optimization.'</example> <example>Context: User wants to create a premium-looking component library for their product. user: 'We need to build a design system that looks professional and scales across our product suite' assistant: 'Let me engage the ux-design-expert agent to help design a scalable component library with premium aesthetics using Tailwind CSS.'</example> <example>Context: User is struggling with a complex multi-step user flow. user: 'Our checkout process has too many steps and users are dropping off' assistant: 'I'll use the ux-design-expert agent to streamline your checkout flow and reduce friction points.'</example>
Execute z.ai GLM 4.7 model via Claude Code CLI. Use when you need z.ai's GLM 4.7 perspective on code analysis.
Generate PNG images using AI (multiple models via OpenRouter including Gemini, FLUX.2, Riverflow, SeedDream, GPT-5 Image, GPT-5.4 Image 2, proxied through Cloudflare AI Gateway BYOK). Also analyze/describe existing images using multimodal AI vision. Use when user asks to "generate an image", "create a PNG", "make an icon", "make it transparent", "describe this image", "analyze this image", "what's in this image", "explain this image", or needs AI-generated visual assets for the project. Supports model selection via keywords (gemini, riverflow, flux2, seedream, gpt5, gpt5.4), configurable aspect ratios/resolutions, transparent backgrounds (-t), reference image editing (-r), image analysis (--analyze), and per-project cost tracking (--costs).
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