This is a small, open-source addition to the growing category of tools that treat token spend as a first-class concern in agentic coding workflows, rather than an afterthought you only worry about once the bill arrives. Its approach centers on three specific behaviors: compressing verbose command output before it ever reaches the model's context, reading only the specific code symbols an agent actually needs rather than pulling in entire files it will mostly ignore, and persisting decisions the agent has already made across sessions so it doesn't have to re-derive the same context and reasoning from scratch every time a new session starts. Each of those individually sounds like a small optimization, but they compound in a way that matters a lot for long-running or repeatedly-invoked agents. Command output, in particular, is a common silent cost center: a single verbose build log, test run, or linter output can be many times larger than the actual signal the agent needs from it, and if that gets stuffed into context unfiltered on every single tool call, the waste accumulates fast across a long session. The reported result, roughly a thirty-one percent reduction in average token bills without changing output correctness, is a meaningful number for anyone running agents at any real scale, since token costs for long agentic sessions can dwarf the cost of the underlying model calls that produce actual code changes. What makes this particularly easy to adopt is that it's framed as a "skill" rather than a framework rewrite, meaning it's meant to slot into an existing agent setup as an add-on behavior rather than requiring you to restructure how your agent already works. For teams already running agents at meaningful volume, this is the kind of unglamorous efficiency tool that's worth trying specifically because the downside of testing it is low and the potential savings are compounding rather than one-time.