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cfn-cerebras-code-generator

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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.

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How to use this skill

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I want to install this Agent Skill for this project in Codex.

Source SKILL.md: https://github.com/majiayu000/claude-skill-registry/blob/HEAD/skills/data/cfn-cerebras-code-generator/SKILL.md

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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

# 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

# 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