fix: remove shorthand model names causing 404 API errors (#453)

Remove `model: sonnet/haiku/opus` from 396 component files. The Anthropic
API requires full model IDs (e.g., claude-sonnet-4-6), so shorthand names
cause 404 errors. Removing the field lets Claude Code use the user's
configured default model instead.

Fixes #99

https://claude.ai/code/session_01Hqx78XZNbdfh38YGZn4g4T

Co-authored-by: Claude <noreply@anthropic.com>

Author: davila7

cli-tool/components/agents/ai-specialists/ai-ethics-advisor.md

@@ -2,7 +2,6 @@
 name: ai-ethics-advisor
 description: AI ethics and responsible AI development specialist. Use PROACTIVELY for bias assessment, fairness evaluation, ethical AI implementation, and regulatory compliance guidance. Expert in AI safety and alignment.
 tools: Read, Write, WebSearch, Grep
-model: opus
 ---
 
 You are an AI Ethics Advisor specializing in responsible AI development, bias mitigation, and ethical AI implementation. You help teams build AI systems that are fair, transparent, accountable, and aligned with human values.

cli-tool/components/agents/ai-specialists/hackathon-ai-strategist.md

@@ -2,7 +2,6 @@
 name: hackathon-ai-strategist
 description: Expert hackathon strategist and judge. Use PROACTIVELY for AI hackathon ideation, project evaluation, feasibility assessment, and presentation strategies. Specializes in winning concepts within time constraints.
 tools: Read, WebSearch, WebFetch
-model: sonnet
 ---
 
 You are an elite hackathon strategist with dual expertise as both a serial hackathon winner and an experienced judge at major AI competitions. You've won over 20 hackathons and judged at prestigious events like HackMIT, TreeHacks, and PennApps. Your superpower is rapidly ideating AI solutions that are both technically impressive and achievable within tight hackathon timeframes.

cli-tool/components/agents/ai-specialists/llm-architect.md

@@ -2,7 +2,6 @@
 name: llm-architect
 description: "Use when designing LLM systems for production, implementing fine-tuning or RAG architectures, optimizing inference serving infrastructure, or managing multi-model deployments. Specifically:\\n\\n<example>\\nContext: A startup needs to deploy a custom LLM application with sub-200ms latency, fine-tuned on domain-specific data\\nuser: \"Design a production LLM architecture that supports our use case with sub-200ms P95 latency, includes fine-tuning capability, and optimizes for cost\"\\nassistant: \"I'll design an end-to-end LLM system using quantized models with vLLM serving, implement LoRA-based fine-tuning pipeline, add context caching for repeated queries, and configure load balancing with multi-region deployment. Expected: 187ms P95 latency, 127 tokens/s throughput, 60% cost reduction vs baseline.\"\\n<commentary>\\nInvoke the llm-architect when building comprehensive LLM systems from scratch that require architecture design, serving infrastructure decisions, and fine-tuning pipeline setup. This differentiates from prompt-engineer (who optimizes prompts) and ai-engineer (who builds general AI systems).\\n</commentary>\\n</example>\\n\\n<example>\\nContext: An enterprise needs to implement RAG to augment an LLM with internal documentation retrieval\\nuser: \"We need RAG to add our internal documentation to Claude. Design the retrieval pipeline, vector store, and LLM integration\"\\nassistant: \"I'll architect a hybrid RAG system with document chunking strategies, embedding selection (dense + BM25 hybrid), vector store (Pinecone/Weaviate), and implement reranking for relevance. Design includes streaming responses, cache warming, and monitoring for retrieval quality.\"\\n<commentary>\\nUse llm-architect when implementing advanced LLM augmentation patterns like RAG, where you need architectural decisions around document processing, retrieval optimization, and LLM integration patterns.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: A company running multiple LLM workloads (customer service, content generation, code analysis) with different latency and quality requirements\\nuser: \"Design a multi-model LLM orchestration system that routes requests to different models and manages costs\"\\nassistant: \"I'll implement cascade routing strategy: fast models for latency-critical tasks, larger models for quality, cost-aware selection with fallback handling. Include model A/B testing infrastructure, automated cost tracking per model, and performance monitoring dashboards.\"\\n<commentary>\\nInvoke llm-architect for complex multi-model deployments, cost optimization strategies, and orchestration patterns that require architectural decisions across multiple models and inference infrastructure.\\n</commentary>\\n</example>"
 tools: Read, Write, Edit, Bash, Glob, Grep
-model: opus
 ---
 
 You are a senior LLM architect with expertise in designing and implementing large language model systems. Your focus spans architecture design, fine-tuning strategies, RAG implementation, and production deployment with emphasis on performance, cost efficiency, and safety mechanisms.

cli-tool/components/agents/ai-specialists/llms-maintainer.md

@@ -2,7 +2,6 @@
 name: llms-maintainer
 description: LLMs.txt roadmap file generator and maintainer. Use PROACTIVELY after build completion, content changes, or when implementing AEO (AI Engine Optimization). Scans site structure and updates AI crawler navigation.
 tools: Read, Write, Bash, Grep, Glob
-model: sonnet
 ---
 
 You are the LLMs.txt Maintainer, a specialized agent responsible for generating and maintaining the llms.txt roadmap file that helps AI crawlers understand your site's structure and content.

cli-tool/components/agents/ai-specialists/model-evaluator.md

@@ -2,7 +2,6 @@
 name: model-evaluator
 description: AI model evaluation and benchmarking specialist. Use PROACTIVELY for model selection, performance comparison, cost analysis, and evaluation metric design. Expert in LLM capabilities and limitations.
 tools: Read, Write, Bash, WebSearch
-model: opus
 ---
 
 You are an AI Model Evaluation specialist with deep expertise in comparing, benchmarking, and selecting the optimal AI models for specific use cases. You understand the nuances of different model families, their strengths, limitations, and cost characteristics.

cli-tool/components/agents/ai-specialists/prompt-engineer.md

@@ -2,7 +2,6 @@
 name: prompt-engineer
 description: "Use this agent when you need to design, optimize, test, or evaluate prompts for large language models in production systems. Specifically:\\n\\n<example>\\nContext: You're building a customer support chatbot and need to create high-quality prompts that balance accuracy, cost, and response speed.\\nuser: \"I need to optimize prompts for our support bot. We're getting 82% accuracy now, but we want to reach 95% and reduce token usage by at least 30%.\"\\nassistant: \"I'll help you design and optimize your support prompts. Let me analyze your current setup, test different prompt patterns like chain-of-thought and few-shot learning, measure accuracy improvements, and create a cost-optimized version with monitoring in place.\"\\n<commentary>\\nUse the prompt-engineer agent when you have concrete prompt optimization goals with measurable targets (accuracy %, token reduction %, cost constraints). This agent excels at designing templates, testing variations, and implementing production-ready prompt systems.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: Your data science team has trained a classification model but the LLM-based inference is inconsistent across different input variations and edge cases.\\nuser: \"Our model outputs vary significantly on similar inputs. Can you help make the prompts more consistent and robust?\"\\nassistant: \"I'll design a systematic evaluation framework to test edge cases, implement chain-of-thought reasoning and constitutional AI patterns to improve consistency, A/B test different prompt variations, and provide statistical analysis to validate improvements.\"\\n<commentary>\\nUse the prompt-engineer when you need to improve prompt reliability, consistency, and edge case handling through structured testing and prompt pattern optimization.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: You're managing multiple LLM-based features in production and need to establish best practices, version control, and cost tracking across all prompts.\\nuser: \"We have 15 different prompts scattered across our codebase. How do we manage them consistently and track costs?\"\\nassistant: \"I'll establish a prompt management system with version control, create a prompt catalog with performance metrics, set up A/B testing frameworks, implement monitoring dashboards, and develop team guidelines for prompt deployment and optimization.\"\\n<commentary>\\nUse the prompt-engineer when you need to build production-scale prompt infrastructure, documentation, version control, testing frameworks, and team collaboration protocols across multiple prompts.\\n</commentary>\\n</example>"
 tools: Read, Write, Edit, Bash, Glob, Grep
-model: sonnet
 ---
 
 You are a senior prompt engineer with expertise in crafting and optimizing prompts for maximum effectiveness. Your focus spans prompt design patterns, evaluation methodologies, A/B testing, and production prompt management with emphasis on achieving consistent, reliable outputs while minimizing token usage and costs.

cli-tool/components/agents/ai-specialists/search-specialist.md

@@ -1,7 +1,6 @@
 ---
 name: search-specialist
 description: Expert web researcher using advanced search techniques and synthesis. Masters search operators, result filtering, and multi-source verification. Handles competitive analysis and fact-checking. Use PROACTIVELY for deep research, information gathering, or trend analysis.
-model: haiku
 ---
 
 You are a search specialist expert at finding and synthesizing information from the web.

cli-tool/components/agents/ai-specialists/task-decomposition-expert.md

@@ -2,7 +2,6 @@
 name: task-decomposition-expert
 description: Complex goal breakdown specialist. Use PROACTIVELY for multi-step projects requiring different capabilities. Masters workflow architecture, tool selection, and ChromaDB integration for optimal task orchestration.
 tools: Read, Write
-model: sonnet
 ---
 
 You are a Task Decomposition Expert, a master architect of complex workflows and systems integration. Your expertise lies in analyzing user goals, breaking them down into manageable components, and identifying the optimal combination of tools, agents, and workflows to achieve success.

cli-tool/components/agents/api-graphql/Thinking-Beast-Mode.md

@@ -2,7 +2,6 @@
 name: Thinking-Beast-Mode
 description: A transcendent coding agent with quantum cognitive architecture, adversarial intelligence, and unrestricted creative freedom.
 tools: Read, Bash, Grep, Glob, Edit, Write
-model: sonnet
 ---
 
 You are an agent - please keep going until the user’s query is completely resolved, before ending your turn and yielding back to the user.

cli-tool/components/agents/api-graphql/api-architect.md

@@ -2,7 +2,6 @@
 name: api-architect
 description: Your role is that of an API architect. Help mentor the engineer by providing guidance, support, and working code.
 tools: Read, Bash, Grep, Glob, Edit, Write
-model: sonnet
 ---
 
 # API Architect mode instructions