--- name: cfn-cerebras-code-generator description: "FAST code generation via Z.ai glm-4.6 model. Use for rapid test generation, boilerplate code, repetitive patterns, and bulk file creation. Ideal when speed matters more than nuance. Do NOT use for complex architectural decisions or security-critical code." version: 2.0.0 tags: [code-generation, fast, zai, glm-4.6, tests, boilerplate] --- # Cerebras Code Generator Skill ## Description Generates code using Z.ai glm-4.6 model for **fast test and code generation**. Use this for rapid iteration when generating tests, boilerplate, and repetitive code patterns. ## When to Use - ✅ **Test generation** - unit tests, integration tests, test fixtures - ✅ **Boilerplate code** - CRUD operations, API endpoints, data models - ✅ **Repetitive patterns** - similar components, migration scripts - ✅ **Bulk file creation** - generating multiple similar files quickly - ❌ **NOT for** complex architecture, security-critical code, or nuanced logic ## Configuration ```bash # Required environment variables export ZAI_API_KEY="your-api-key" # or CEREBRAS_API_KEY for legacy export ZAI_MODEL="glm-4.6" # Fast, cost-effective model # Optional settings export CEREBRAS_BASE_URL="https://api.cerebras.ai/v1" export CONTEXT_DB_PATH="./.claude/skills/cfn-cerebras-code-generator/contexts.db" ``` ## Usage ```bash # Basic code generation ./generate-code.sh \ --file-path "/path/to/file.ext" \ --prompt "Create a REST API endpoint" \ --context-files "src/models.py,src/utils.py" # With explicit model ./generate-code.sh \ --model "llama-3.1-70b" \ --file-path "/path/to/file.py" \ --prompt "Implement authentication middleware" ``` ## Implementation Details ### Context Tracking - Stores generation history in SQLite database - Tracks what worked and what didn't - Maintains conversation context - Provides examples of successful patterns ### OpenAI Compatibility - Uses OpenAI-compatible request/response format - Supports streaming responses - Handles token limits and rate limiting - Automatic retry logic ### Features - ✅ Visual diff generation - ✅ Context file inclusion - ✅ Error handling and validation - ✅ Generation history tracking - ✅ Success pattern learning - ✅ Multiple model support