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chore: update model references to Claude 4.6 and GPT-5.2
- Claude Opus 4.5 → Opus 4.6, Claude Sonnet 4.5 → Sonnet 4.6 (Haiku stays 4.5) - Update claude-sonnet-4-5 model IDs to claude-sonnet-4-6 in code examples - Update SWE-bench stat from 80.9% to 80.8% for Opus 4.6 - Update GPT refs: GPT-5 → GPT-5.2, GPT-4o → gpt-5.2, GPT-4o-mini → GPT-5-mini - Fix GPT-5.2-mini → GPT-5-mini (correct model name per OpenAI) - Bump marketplace to v1.5.2 and affected plugin versions
This commit is contained in:
Seth Hobson
@@ -7,7 +7,7 @@
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},
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"metadata": {
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"description": "Production-ready workflow orchestration with 73 focused plugins, 112 specialized agents, and 146 skills - optimized for granular installation and minimal token usage",
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"version": "1.5.1"
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"version": "1.5.2"
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},
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"plugins": [
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{
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@@ -118,7 +118,7 @@
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"name": "code-review-ai",
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"source": "./plugins/code-review-ai",
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"description": "AI-powered architectural review and code quality analysis",
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"version": "1.2.0",
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"version": "1.2.1",
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"author": {
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"name": "Seth Hobson",
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"email": "seth@major7apps.com"
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@@ -181,8 +181,8 @@
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},
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{
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"name": "llm-application-dev",
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"description": "LLM application development with LangGraph, RAG systems, vector search, and AI agent architectures for Claude 4.5 and GPT-5.2",
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"version": "2.0.3",
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"description": "LLM application development with LangGraph, RAG systems, vector search, and AI agent architectures for Claude 4.6 and GPT-5.2",
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"version": "2.0.4",
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"author": {
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"name": "Seth Hobson",
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"email": "seth@major7apps.com"
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@@ -196,7 +196,7 @@
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"name": "agent-orchestration",
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"source": "./plugins/agent-orchestration",
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"description": "Multi-agent system optimization, agent improvement workflows, and context management",
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"version": "1.2.0",
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"version": "1.2.1",
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"author": {
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"name": "Seth Hobson",
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"email": "seth@major7apps.com"
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@@ -404,7 +404,7 @@
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"name": "performance-testing-review",
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"source": "./plugins/performance-testing-review",
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"description": "Performance analysis, test coverage review, and AI-powered code quality assessment",
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"version": "1.2.0",
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"version": "1.2.1",
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"author": {
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"name": "Seth Hobson",
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"email": "seth@major7apps.com"
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14
README.md
14
README.md
@@ -1,6 +1,6 @@
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# Claude Code Plugins: Orchestration and Automation
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> **⚡ Updated for Opus 4.5, Sonnet 4.5 & Haiku 4.5** — Three-tier model strategy for optimal performance
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> **⚡ Updated for Opus 4.6, Sonnet 4.6 & Haiku 4.5** — Three-tier model strategy for optimal performance
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[](https://smithery.ai/skills?ns=wshobson&utm_source=github&utm_medium=badge)
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@@ -203,14 +203,14 @@ Strategic model assignment for optimal performance and cost:
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| Tier | Model | Agents | Use Case |
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| ---------- | -------- | ------ | ----------------------------------------------------------------------------------------------- |
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| **Tier 1** | Opus 4.5 | 42 | Critical architecture, security, ALL code review, production coding (language pros, frameworks) |
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| **Tier 1** | Opus 4.6 | 42 | Critical architecture, security, ALL code review, production coding (language pros, frameworks) |
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| **Tier 2** | Inherit | 42 | Complex tasks - user chooses model (AI/ML, backend, frontend/mobile, specialized) |
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| **Tier 3** | Sonnet | 51 | Support with intelligence (docs, testing, debugging, network, API docs, DX, legacy, payments) |
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| **Tier 4** | Haiku | 18 | Fast operational tasks (SEO, deployment, simple docs, sales, content, search) |
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**Why Opus 4.5 for Critical Agents?**
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**Why Opus 4.6 for Critical Agents?**
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- 80.9% on SWE-bench (industry-leading)
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- 80.8% on SWE-bench (industry-leading)
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- 65% fewer tokens for complex tasks
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- Best for architecture decisions and security audits
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@@ -218,14 +218,14 @@ Strategic model assignment for optimal performance and cost:
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Agents marked `inherit` use your session's default model, letting you balance cost and capability:
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- Set via `claude --model opus` or `claude --model sonnet` when starting a session
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- Falls back to Sonnet 4.5 if no default specified
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- Falls back to Sonnet 4.6 if no default specified
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- Perfect for frontend/mobile developers who want cost control
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- AI/ML engineers can choose Opus for complex model work
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**Cost Considerations:**
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- **Opus 4.5**: $5/$25 per million input/output tokens - Premium for critical work
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- **Sonnet 4.5**: $3/$15 per million tokens - Balanced performance/cost
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- **Opus 4.6**: $5/$25 per million input/output tokens - Premium for critical work
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- **Sonnet 4.6**: $3/$15 per million tokens - Balanced performance/cost
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- **Haiku 4.5**: $1/$5 per million tokens - Fast, cost-effective operations
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- Opus's 65% token reduction on complex tasks often offsets higher rate
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- Use `inherit` tier to control costs for high-volume use cases
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@@ -1,6 +1,6 @@
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{
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"name": "agent-orchestration",
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"version": "1.2.0",
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"version": "1.2.1",
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"description": "Multi-agent system optimization, agent improvement workflows, and context management",
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"author": {
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"name": "Seth Hobson",
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@@ -146,7 +146,7 @@ class CostOptimizer:
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self.token_budget = 100000 # Monthly budget
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self.token_usage = 0
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self.model_costs = {
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'gpt-5': 0.03,
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'gpt-5.2': 0.03,
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'claude-4-sonnet': 0.015,
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'claude-4-haiku': 0.0025
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}
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@@ -1,6 +1,6 @@
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{
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"name": "code-review-ai",
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"version": "1.2.0",
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"version": "1.2.1",
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"description": "AI-powered architectural review and code quality analysis",
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"author": {
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"name": "Seth Hobson",
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@@ -1,6 +1,6 @@
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# AI-Powered Code Review Specialist
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You are an expert AI-powered code review specialist combining automated static analysis, intelligent pattern recognition, and modern DevOps practices. Leverage AI tools (GitHub Copilot, Qodo, GPT-5, Claude 4.5 Sonnet) with battle-tested platforms (SonarQube, CodeQL, Semgrep) to identify bugs, vulnerabilities, and performance issues.
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You are an expert AI-powered code review specialist combining automated static analysis, intelligent pattern recognition, and modern DevOps practices. Leverage AI tools (GitHub Copilot, Qodo, GPT-5.2, Claude 4.6 Sonnet) with battle-tested platforms (SonarQube, CodeQL, Semgrep) to identify bugs, vulnerabilities, and performance issues.
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## Context
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@@ -34,7 +34,7 @@ Execute in parallel:
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### AI-Assisted Review
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```python
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# Context-aware review prompt for Claude 4.5 Sonnet
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# Context-aware review prompt for Claude 4.6 Sonnet
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review_prompt = f"""
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You are reviewing a pull request for a {language} {project_type} application.
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@@ -64,8 +64,8 @@ Format as JSON array.
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### Model Selection (2025)
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- **Fast reviews (<200 lines)**: GPT-4o-mini or Claude 4.5 Haiku
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- **Deep reasoning**: Claude 4.5 Sonnet or GPT-5 (200K+ tokens)
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- **Fast reviews (<200 lines)**: GPT-5-mini or Claude 4.5 Haiku
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- **Deep reasoning**: Claude 4.6 Sonnet or GPT-5.2 (200K+ tokens)
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- **Code generation**: GitHub Copilot or Qodo
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- **Multi-language**: Qodo or CodeAnt AI (30+ languages)
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@@ -92,7 +92,7 @@ interface ReviewRoutingStrategy {
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return new QodoEngine({ mode: "test-generation", coverageTarget: 80 });
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}
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return new AIEngine("gpt-4o", { temperature: 0.3, maxTokens: 2000 });
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return new AIEngine("gpt-5.2", { temperature: 0.3, maxTokens: 2000 });
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}
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}
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```
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@@ -312,13 +312,13 @@ jobs:
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codeql database create codeql-db --language=javascript,python
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semgrep scan --config=auto --sarif --output=semgrep.sarif
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- name: AI-Enhanced Review (GPT-5)
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- name: AI-Enhanced Review (GPT-5.2)
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env:
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OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
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run: |
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python scripts/ai_review.py \
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--pr-number ${{ github.event.number }} \
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--model gpt-4o \
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--model gpt-5.2 \
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--static-analysis-results codeql.sarif,semgrep.sarif
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- name: Post Comments
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@@ -446,7 +446,7 @@ if __name__ == '__main__':
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Comprehensive AI code review combining:
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1. Multi-tool static analysis (SonarQube, CodeQL, Semgrep)
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2. State-of-the-art LLMs (GPT-5, Claude 4.5 Sonnet)
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2. State-of-the-art LLMs (GPT-5.2, Claude 4.6 Sonnet)
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3. Seamless CI/CD integration (GitHub Actions, GitLab, Azure DevOps)
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4. 30+ language support with language-specific linters
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5. Actionable review comments with severity and fix examples
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@@ -1,7 +1,7 @@
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{
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"name": "llm-application-dev",
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"description": "LLM application development with LangGraph, RAG systems, vector search, and AI agent architectures for Claude 4.5 and GPT-5.2",
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"version": "2.0.3",
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"description": "LLM application development with LangGraph, RAG systems, vector search, and AI agent architectures for Claude 4.6 and GPT-5.2",
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"version": "2.0.4",
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"author": {
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"name": "Seth Hobson",
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"email": "seth@major7apps.com"
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@@ -5,7 +5,7 @@ Build production-ready LLM applications, advanced RAG systems, and intelligent a
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## Version 2.0.0 Highlights
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- **LangGraph Integration**: Updated from deprecated LangChain patterns to LangGraph StateGraph workflows
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- **Modern Model Support**: Claude Opus/Sonnet/Haiku 4.5 and GPT-5.2/GPT-5.2-mini
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- **Modern Model Support**: Claude Opus 4.6/Sonnet 4.6/Haiku 4.5 and GPT-5.2/GPT-5-mini
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- **Voyage AI Embeddings**: Recommended embedding models for Claude applications
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- **Structured Outputs**: Pydantic-based structured output patterns
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@@ -71,7 +71,7 @@ Build production-ready LLM applications, advanced RAG systems, and intelligent a
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### 2.0.0 (January 2026)
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- **Breaking**: Migrated from LangChain 0.x to LangChain 1.x/LangGraph
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- **Breaking**: Updated model references to Claude 4.5 and GPT-5.2
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- **Breaking**: Updated model references to Claude 4.6 and GPT-5.2
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- Added Voyage AI as primary embedding recommendation for Claude apps
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- Added LangGraph StateGraph patterns replacing deprecated `initialize_agent()`
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- Added structured outputs with Pydantic
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@@ -14,8 +14,8 @@ Expert AI engineer specializing in LLM application development, RAG systems, and
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### LLM Integration & Model Management
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- OpenAI GPT-5.2/GPT-5.2-mini with function calling and structured outputs
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- Anthropic Claude Opus 4.5, Claude Sonnet 4.5, Claude Haiku 4.5 with tool use and computer use
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- OpenAI GPT-5.2/GPT-5-mini with function calling and structured outputs
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- Anthropic Claude Opus 4.6, Claude Sonnet 4.6, Claude Haiku 4.5 with tool use and computer use
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- Open-source models: Llama 3.3, Mixtral 8x22B, Qwen 2.5, DeepSeek-V3
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- Local deployment with Ollama, vLLM, TGI (Text Generation Inference)
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- Model serving with TorchServe, MLflow, BentoML for production deployment
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@@ -76,7 +76,7 @@ Expert AI engineer specializing in LLM application development, RAG systems, and
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### Multimodal AI Integration
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- Vision models: GPT-4V, Claude 4 Vision, LLaVA, CLIP for image understanding
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- Vision models: GPT-5.2, Claude 4 Vision, LLaVA, CLIP for image understanding
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- Audio processing: Whisper for speech-to-text, ElevenLabs for text-to-speech
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- Document AI: OCR, table extraction, layout understanding with models like LayoutLM
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- Video analysis and processing for multimedia applications
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@@ -124,7 +124,7 @@ Expert AI engineer specializing in LLM application development, RAG systems, and
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## Knowledge Base
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- Latest LLM developments and model capabilities (GPT-5.2, Claude 4.5, Llama 3.3)
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- Latest LLM developments and model capabilities (GPT-5.2, Claude 4.6, Llama 3.3)
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- Modern vector database architectures and optimization techniques
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- Production AI system design patterns and best practices
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- AI safety and security considerations for enterprise deployments
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@@ -48,7 +48,7 @@ Expert prompt engineer specializing in advanced prompting methodologies and LLM
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### Model-Specific Optimization
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#### OpenAI Models (GPT-5.2, GPT-5.2-mini)
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#### OpenAI Models (GPT-5.2, GPT-5-mini)
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- Function calling optimization and structured outputs
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- JSON mode utilization for reliable data extraction
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@@ -58,7 +58,7 @@ Expert prompt engineer specializing in advanced prompting methodologies and LLM
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- Multi-turn conversation management
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- Image and multimodal prompt engineering
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#### Anthropic Claude (Claude Opus 4.5, Sonnet 4.5, Haiku 4.5)
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#### Anthropic Claude (Claude Opus 4.6, Sonnet 4.6, Haiku 4.5)
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- Constitutional AI alignment with Claude's training
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- Tool use optimization for complex workflows
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@@ -37,7 +37,7 @@ class AgentState(TypedDict):
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### Model & Embeddings
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- **Primary LLM**: Claude Sonnet 4.5 (`claude-sonnet-4-5`)
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- **Primary LLM**: Claude Sonnet 4.6 (`claude-sonnet-4-6`)
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- **Embeddings**: Voyage AI (`voyage-3-large`) - officially recommended by Anthropic for Claude
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- **Specialized**: `voyage-code-3` (code), `voyage-finance-2` (finance), `voyage-law-2` (legal)
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@@ -158,7 +158,7 @@ from langsmith.evaluation import evaluate
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# Run evaluation suite
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eval_config = RunEvalConfig(
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evaluators=["qa", "context_qa", "cot_qa"],
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eval_llm=ChatAnthropic(model="claude-sonnet-4-5")
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eval_llm=ChatAnthropic(model="claude-sonnet-4-6")
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)
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results = await evaluate(
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@@ -209,7 +209,7 @@ async def call_with_retry():
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## Implementation Checklist
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- [ ] Initialize LLM with Claude Sonnet 4.5
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- [ ] Initialize LLM with Claude Sonnet 4.6
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- [ ] Setup Voyage AI embeddings (voyage-3-large)
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- [ ] Create tools with async support and error handling
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- [ ] Implement memory system (choose type based on use case)
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@@ -150,7 +150,7 @@ gpt5_optimized = """
|
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|
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````
|
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|
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**Claude 4.5/4**
|
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**Claude 4.6/4.5**
|
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```python
|
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claude_optimized = """
|
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<context>
|
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@@ -607,7 +607,7 @@ testing_recommendations:
|
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metrics: ["accuracy", "satisfaction", "cost"]
|
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|
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deployment_strategy:
|
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model: "GPT-5.2 for quality, Claude 4.5 for safety"
|
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model: "GPT-5.2 for quality, Claude 4.6 for safety"
|
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temperature: 0.7
|
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max_tokens: 2000
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monitoring: "Track success, latency, feedback"
|
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|
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@@ -115,8 +115,8 @@ from langchain_core.tools import tool
|
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import ast
|
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import operator
|
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|
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# Initialize LLM (Claude Sonnet 4.5 recommended)
|
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llm = ChatAnthropic(model="claude-sonnet-4-5", temperature=0)
|
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# Initialize LLM (Claude Sonnet 4.6 recommended)
|
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llm = ChatAnthropic(model="claude-sonnet-4-6", temperature=0)
|
||||
|
||||
# Define tools with Pydantic schemas
|
||||
@tool
|
||||
@@ -201,7 +201,7 @@ class RAGState(TypedDict):
|
||||
answer: str
|
||||
|
||||
# Initialize components
|
||||
llm = ChatAnthropic(model="claude-sonnet-4-5")
|
||||
llm = ChatAnthropic(model="claude-sonnet-4-6")
|
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embeddings = VoyageAIEmbeddings(model="voyage-3-large")
|
||||
vectorstore = PineconeVectorStore(index_name="docs", embedding=embeddings)
|
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retriever = vectorstore.as_retriever(search_kwargs={"k": 4})
|
||||
@@ -489,7 +489,7 @@ os.environ["LANGCHAIN_API_KEY"] = "your-api-key"
|
||||
os.environ["LANGCHAIN_PROJECT"] = "my-project"
|
||||
|
||||
# All LangChain/LangGraph operations are automatically traced
|
||||
llm = ChatAnthropic(model="claude-sonnet-4-5")
|
||||
llm = ChatAnthropic(model="claude-sonnet-4-6")
|
||||
```
|
||||
|
||||
### Custom Callback Handler
|
||||
@@ -530,7 +530,7 @@ result = await agent.ainvoke(
|
||||
```python
|
||||
from langchain_anthropic import ChatAnthropic
|
||||
|
||||
llm = ChatAnthropic(model="claude-sonnet-4-5", streaming=True)
|
||||
llm = ChatAnthropic(model="claude-sonnet-4-6", streaming=True)
|
||||
|
||||
# Stream tokens
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||||
async for chunk in llm.astream("Tell me a story"):
|
||||
|
||||
@@ -283,7 +283,7 @@ Provide ratings in JSON format:
|
||||
}}"""
|
||||
|
||||
message = client.messages.create(
|
||||
model="claude-sonnet-4-5",
|
||||
model="claude-sonnet-4-6",
|
||||
max_tokens=500,
|
||||
system=system,
|
||||
messages=[{"role": "user", "content": prompt}]
|
||||
@@ -329,7 +329,7 @@ Answer with JSON:
|
||||
}}"""
|
||||
|
||||
message = client.messages.create(
|
||||
model="claude-sonnet-4-5",
|
||||
model="claude-sonnet-4-6",
|
||||
max_tokens=500,
|
||||
messages=[{"role": "user", "content": prompt}]
|
||||
)
|
||||
@@ -375,7 +375,7 @@ Respond in JSON:
|
||||
}}"""
|
||||
|
||||
message = client.messages.create(
|
||||
model="claude-sonnet-4-5",
|
||||
model="claude-sonnet-4-6",
|
||||
max_tokens=500,
|
||||
messages=[{"role": "user", "content": prompt}]
|
||||
)
|
||||
@@ -605,7 +605,7 @@ experiment_results = await evaluate(
|
||||
data=dataset.name,
|
||||
evaluators=evaluators,
|
||||
experiment_prefix="v1.0.0",
|
||||
metadata={"model": "claude-sonnet-4-5", "version": "1.0.0"}
|
||||
metadata={"model": "claude-sonnet-4-6", "version": "1.0.0"}
|
||||
)
|
||||
|
||||
print(f"Mean score: {experiment_results.aggregate_metrics['qa']['mean']}")
|
||||
|
||||
@@ -81,7 +81,7 @@ class SQLQuery(BaseModel):
|
||||
tables_used: list[str] = Field(description="List of tables referenced")
|
||||
|
||||
# Initialize model with structured output
|
||||
llm = ChatAnthropic(model="claude-sonnet-4-5")
|
||||
llm = ChatAnthropic(model="claude-sonnet-4-6")
|
||||
structured_llm = llm.with_structured_output(SQLQuery)
|
||||
|
||||
# Create prompt template
|
||||
@@ -124,7 +124,7 @@ async def analyze_sentiment(text: str) -> SentimentAnalysis:
|
||||
client = Anthropic()
|
||||
|
||||
message = client.messages.create(
|
||||
model="claude-sonnet-4-5",
|
||||
model="claude-sonnet-4-6",
|
||||
max_tokens=500,
|
||||
messages=[{
|
||||
"role": "user",
|
||||
@@ -427,7 +427,7 @@ client = Anthropic()
|
||||
|
||||
# Use prompt caching for repeated system prompts
|
||||
response = client.messages.create(
|
||||
model="claude-sonnet-4-5",
|
||||
model="claude-sonnet-4-6",
|
||||
max_tokens=1000,
|
||||
system=[
|
||||
{
|
||||
|
||||
@@ -68,7 +68,7 @@ def self_consistency_cot(query, n=5, temperature=0.7):
|
||||
responses = []
|
||||
for _ in range(n):
|
||||
response = openai.ChatCompletion.create(
|
||||
model="gpt-5",
|
||||
model="gpt-5.2",
|
||||
messages=[{"role": "user", "content": prompt}],
|
||||
temperature=temperature
|
||||
)
|
||||
|
||||
@@ -85,7 +85,7 @@ class RAGState(TypedDict):
|
||||
answer: str
|
||||
|
||||
# Initialize components
|
||||
llm = ChatAnthropic(model="claude-sonnet-4-5")
|
||||
llm = ChatAnthropic(model="claude-sonnet-4-6")
|
||||
embeddings = VoyageAIEmbeddings(model="voyage-3-large")
|
||||
vectorstore = PineconeVectorStore(index_name="docs", embedding=embeddings)
|
||||
retriever = vectorstore.as_retriever(search_kwargs={"k": 4})
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
{
|
||||
"name": "performance-testing-review",
|
||||
"version": "1.2.0",
|
||||
"version": "1.2.1",
|
||||
"description": "Performance analysis, test coverage review, and AI-powered code quality assessment",
|
||||
"author": {
|
||||
"name": "Seth Hobson",
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
# AI-Powered Code Review Specialist
|
||||
|
||||
You are an expert AI-powered code review specialist combining automated static analysis, intelligent pattern recognition, and modern DevOps practices. Leverage AI tools (GitHub Copilot, Qodo, GPT-5, Claude 4.5 Sonnet) with battle-tested platforms (SonarQube, CodeQL, Semgrep) to identify bugs, vulnerabilities, and performance issues.
|
||||
You are an expert AI-powered code review specialist combining automated static analysis, intelligent pattern recognition, and modern DevOps practices. Leverage AI tools (GitHub Copilot, Qodo, GPT-5.2, Claude 4.6 Sonnet) with battle-tested platforms (SonarQube, CodeQL, Semgrep) to identify bugs, vulnerabilities, and performance issues.
|
||||
|
||||
## Context
|
||||
|
||||
@@ -34,7 +34,7 @@ Execute in parallel:
|
||||
### AI-Assisted Review
|
||||
|
||||
```python
|
||||
# Context-aware review prompt for Claude 4.5 Sonnet
|
||||
# Context-aware review prompt for Claude 4.6 Sonnet
|
||||
review_prompt = f"""
|
||||
You are reviewing a pull request for a {language} {project_type} application.
|
||||
|
||||
@@ -64,8 +64,8 @@ Format as JSON array.
|
||||
|
||||
### Model Selection (2025)
|
||||
|
||||
- **Fast reviews (<200 lines)**: GPT-4o-mini or Claude 4.5 Haiku
|
||||
- **Deep reasoning**: Claude 4.5 Sonnet or GPT-4.5 (200K+ tokens)
|
||||
- **Fast reviews (<200 lines)**: GPT-5-mini or Claude 4.5 Haiku
|
||||
- **Deep reasoning**: Claude 4.6 Sonnet or GPT-5.2 (200K+ tokens)
|
||||
- **Code generation**: GitHub Copilot or Qodo
|
||||
- **Multi-language**: Qodo or CodeAnt AI (30+ languages)
|
||||
|
||||
@@ -92,7 +92,7 @@ interface ReviewRoutingStrategy {
|
||||
return new QodoEngine({ mode: "test-generation", coverageTarget: 80 });
|
||||
}
|
||||
|
||||
return new AIEngine("gpt-4o", { temperature: 0.3, maxTokens: 2000 });
|
||||
return new AIEngine("gpt-5.2", { temperature: 0.3, maxTokens: 2000 });
|
||||
}
|
||||
}
|
||||
```
|
||||
@@ -312,13 +312,13 @@ jobs:
|
||||
codeql database create codeql-db --language=javascript,python
|
||||
semgrep scan --config=auto --sarif --output=semgrep.sarif
|
||||
|
||||
- name: AI-Enhanced Review (GPT-5)
|
||||
- name: AI-Enhanced Review (GPT-5.2)
|
||||
env:
|
||||
OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
|
||||
run: |
|
||||
python scripts/ai_review.py \
|
||||
--pr-number ${{ github.event.number }} \
|
||||
--model gpt-4o \
|
||||
--model gpt-5.2 \
|
||||
--static-analysis-results codeql.sarif,semgrep.sarif
|
||||
|
||||
- name: Post Comments
|
||||
@@ -446,7 +446,7 @@ if __name__ == '__main__':
|
||||
Comprehensive AI code review combining:
|
||||
|
||||
1. Multi-tool static analysis (SonarQube, CodeQL, Semgrep)
|
||||
2. State-of-the-art LLMs (GPT-5, Claude 4.5 Sonnet)
|
||||
2. State-of-the-art LLMs (GPT-5.2, Claude 4.6 Sonnet)
|
||||
3. Seamless CI/CD integration (GitHub Actions, GitLab, Azure DevOps)
|
||||
4. 30+ language support with language-specific linters
|
||||
5. Actionable review comments with severity and fix examples
|
||||
|
||||
Reference in New Issue
Block a user