Release as a reusable “skill set” (e.g. MCP-compatible tool / package) for integration with platforms like OpenCode and other LLM orchestration systems.

Motivation:The current implementation is useful as a standalone prompt optimizer, but its impact would be significantly higher if exposed as a modular, callable component. Many modern workflows (OpenCode, MCP clients, agent frameworks) rely on composable skills/tools rather than monolithic repos.
Proposal - Package this project as a standardized skill/module with:

• Clear input/output schema (e.g. optimize(prompt, context, constraints) -> optimized_prompt)
• Stateless API design for easy embedding
• Optional streaming / iterative refinement modes
• Provide an MCP (Model Context Protocol) server wrapper or equivalent interface
• Offer lightweight SDK bindings (Python / TypeScript) for integration
• Define configurable optimization strategies (conciseness, reasoning depth, format control, etc.)

Key Benefits

• Enables plug-and-play usage inside OpenCode, VS Code agents, and other toolchains
• Makes prompt optimization composable within larger pipelines (e.g. RAG → optimize → execute)
• Standardizes prompt transformation as a reusable primitive rather than ad-hoc logic

Suggested Additions

• Benchmark suite for evaluating optimization quality across tasks (and improvement to scoring consistancy)
• Preset profiles (e.g. “chain-of-thought compression”, “instruction clarity”, “latency-optimized”)
• Optional telemetry hooks for token usage and effectiveness tracking

Outcome This would elevate the repo from a utility script to an ecosystem-compatible building block, significantly improving adoption and real-world applicability.