Claude-skill-registry cfn-cerebras-code-generator

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.

install

source · Clone the upstream repo

git clone https://github.com/majiayu000/claude-skill-registry

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data/cfn-cerebras-code-generator" ~/.claude/skills/majiayu000-claude-skill-registry-cfn-cerebras-code-generator && rm -rf "$T"

manifest: skills/data/cfn-cerebras-code-generator/SKILL.md

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