The data analyst also outlined three distinct use cases where the markdown file could be most effective. First, high-volume automation pipelines, such as resume bots, agent loops, and code generation, where verbosity compounds across repeated calls.
Second, repeated structured tasks, where Claude’s default expansiveness can add up over hundreds of interactions. Third, team environments that require consistent, parseable output formats across sessions, where tighter control over responses improves reliability and downstream usability.
In his own simulations on Claude Sonnet, Reddy said the file could save close to 9,600 tokens a day at 100 prompts, translating to roughly $0.86 in monthly savings. At 1,000 prompts a day, the savings rise to about 96,000 tokens, or $8.64 a month, while across three projects combined, he estimates reductions of nearly 288,000 tokens, equivalent to around $25.92 monthly.
However, the data analyst also warned that the file might be really ineffective, even counterproductive, in certain use cases, such as single one-off queries, fixing deep failures, or exploratory work where feedback is required, as the file itself consumes input tokens on every message.