token-saver

name

token-saver

description

Automatically delegates routine coding tasks to free AI models (GLM, GitHub Copilot, and others) via OpenCode MCP, preserving Claude's context window and rate limits for complex reasoning. Use when generating boilerplate, CRUD operations, simple refactoring, test scaffolding, documentation, type definitions, or any well-defined low-judgment task. Triggers automatically when Claude recognizes routine patterns.

Token Saver

Offload routine tasks to free models. Save Claude for complex reasoning.

Automatic Delegation Rules

DELEGATE (routine, well-defined):

• Boilerplate code generation
• TypeScript/Python interfaces and types
• CRUD operations and database models
• Simple data validation (Zod schemas, validators)
• Test scaffolding and fixtures
• Code formatting and style cleanup
• Docstrings and inline comments
• README sections and documentation
• Repetitive refactoring (early returns, destructuring)
• Form field mappings
• Standard API endpoint scaffolding

KEEP IN CLAUDE (requires judgment):

• Architectural decisions
• Security-sensitive code
• Complex business logic
• Error handling strategy
• Performance optimization
• Code review and analysis
• Debugging and root cause analysis
• Domain-specific logic requiring context
• Anything touching auth, payments, PII

How to Delegate

1. Identify routine task — Match against delegation rules above
2. Gather context — Read relevant files if needed
3. Invoke OpenCode — Use configured free model
4. Validate output — Check for correctness before using
5. Apply or return — Write to file or pass back to conversation

Invocation Pattern

# Basic invocation
opencode run --model <model-name> "<prompt>"

# With file context
opencode run --model <model-name> "$(cat src/types.ts)" "<prompt>"

# Recommended free models
opencode run --model glm/glm-4 "<prompt>"
opencode run --model github-copilot/gpt-4.1 "<prompt>"

Example Delegations

Generate TypeScript interface:

opencode run --model glm/glm-4 "Generate TypeScript interface for a User with id, email, name, createdAt, updatedAt fields"

Create Zod schema:

opencode run --model glm/glm-4 "Create Zod validation schema for email, password (min 8 chars), and optional phone number"

Refactor to early returns:

opencode run --model github-copilot/gpt-4.1 "Refactor this function to use early returns: $(cat src/utils/validate.ts)"

Generate test fixtures:

opencode run --model glm/glm-4 "Generate 5 test fixtures for User objects with realistic fake data"

Model Selection

Choose model based on task:

Task Type	Recommended Model	Why
TypeScript/JS	github-copilot/gpt-4.1	Strong TS inference
Python	glm/glm-4	Good Python patterns
Documentation	glm/glm-4	Natural language strength
General boilerplate	Any free model	All handle well

See model-capabilities.md for detailed comparison.

Configuration

Requires OpenCode MCP server. See mcp-config-template.json for setup.

Fallback Chain

If primary model fails:

1. Try next model in configured list
2. If all fail, escalate back to Claude
3. Log failure for debugging

Validation Checklist

Before using delegated output:

• Syntax is correct (no obvious errors)
• Types are accurate (if TypeScript)
• Follows project conventions
• No security issues introduced
• Imports are correct

When NOT to Delegate

Stop and keep in Claude if task involves:

• Decisions about what to build (not just how)
• Security boundaries or auth logic
• Complex conditional logic
• Domain-specific business rules
• Code that touches sensitive data
• Anything you'd want a senior engineer to review