Was looking to speed up coding and reduce token usage. Theory is an LLM can provide a plan to a deterministic local coder faster and with less token use than writing the code itself, and so far that's panned out. Revisions and teaching the coder new local skills can take time and tokens in the short run, but Sif remembers the new skills and doesn't have to be taught those skills again. Name: Sif 1.0 License: Apache 2.0 Developed and tested on Windows. Most of my testing involved converting Python to C++, though I have experimented with general coding tasks. Python to C++ conversion has worked well so far. Takes about 250-300 tokens for a frontier model, 400 to 500 tokens for a flash open source model to produce the plan. If Sif has experienced all requirements before, can produce one shot conversion with no repairs needed. Several test/benchmark reports can be found in the repository highlighting successes and failures along the way if interested. For those who are interested, I would greatly appreciate any feedback offered.