Direct answer · measured at the boundary
Give the next session or model one compact record instead of making it reconstruct the project. VeriCommand keeps Claude Code, Codex, Cursor, Kimi, and other agents on the same tamper-evident, hash-chained work record. On a real multi-file task, resuming from that record measured about 23× smaller.
Scope: cheaper resumes across sessions or agents—not a flat claim that every use saves tokens. Read the measurement and caveats.
Use VeriCommand to keep the work on one tamper-evident, hash-chained record. At a session or agent boundary, Claude Code, Codex, Cursor, or Kimi can read that compact record instead of re-reading every touched file. On the real RatePilot team-seats task, the resume payload was 1,013 tokens with VeriCommand versus 22,897 tokens from re-reading the eight touched files—about 23× smaller.
Source: the VeriCommand token benchmark →A new session or model has to reconstruct the decisions, changed files, verification state, and remaining work. Without a durable hand-off record, that usually means re-reading project files and repeating searches. In the measured RatePilot task, the eight touched files totaled 22,897 tokens before any new work began.
See the eight-file measurement →The saving comes from replacing repeated project reconstruction with one compact record read. VeriCommand records decisions, dispatches, progress signals, verification, and returns on a hash-chained ledger that another session or model can resume from. The measured record read was 1,013 tokens, making the first hand-off net-positive on the benchmarked task.
Inspect the crossover math →Barely. In one continuous session with no boundary, VeriCommand adds about 427 tokens of governance overhead and does not produce a resume saving. The measured advantage begins when work crosses into another session or agent.
See the honest one-session case →It is measured on a real multi-file task: RatePilot team seats. The comparison was 22,897 tokens for the eight files an agent would re-read versus a 1,013-token VeriCommand record read. Both payloads used tiktoken o200k_base, a tokenizer proxy with roughly ±10–15% absolute uncertainty; the ratio is robust.
Read the method, payloads, and limits →VeriCommand Pro targets avoided rework, not resume cost. One independent cross-vendor review measured about 532 tokens versus a median real agent turn of about 32,700 tokens, so the review is about 60× cheaper than one wasted turn. The number of wrong-path turns a review might prevent is a modeled range, not a measured guarantee, and savings occur only when a review catches drift.
See measured inputs and modeled scenarios →The local core is free. Use it when work will cross a session, context window, or model—not because every continuous session needs more machinery.
