How to use Claude to tune CLI performance on a schedule
The author reports Repomix runtime ended up roughly 2.4x faster after trying scheduled Claude Code performance tuning.
dev.to
Suggested prompt
Suggested prompt written by Agents In Use, not the source author. Replace bracketed inputs and review tool permissions first.
Tune performance in [repository] on [working branch] from [base branch]. Investigate bottlenecks and choose one improvement per run. Preserve behavior, benchmark against [threshold], run [lint/tests], revert if the threshold is missed, and prepare a reviewed draft pull request.
Reach people exploring AI agents
See placements →Evidence at a glance
Sourced account- Source author
- yamadashy
- Original account
- Read the source ↗
- Published
- Source link checked
- Reported result
- The author reports Repomix runtime ended up roughly 2.4x faster after trying scheduled Claude Code performance tuning.
- Human involvement
- The registered prompt requires benchmarks, lint, tests, and subagent review. The excerpt does not describe human merge review or individual optimization results.
A linked account supports attribution. It does not mean we independently reproduced its result.
Report an issue with this case
Open correction emailOpens your email app with the case link included. Nothing is sent automatically.
How the agent did it
- Configured a two-hour Routine for the Repomix repository.
- Registered a performance prompt targeting src and a dedicated branch.
- Tried scheduled tuning on Repomix and reported faster runtime.
The maintainer of Repomix used Claude Code Routines to schedule performance-tuning work in the cloud. They configured a two-hour schedule in the web interface, pointed it at the repository, and registered a detailed prompt targeting source code and related tests, configuration, and dependencies. They reported that Repomix runtime ended up roughly 2.4 times faster.
The prompt kept work on a dedicated performance branch based on main. It directed the routine to examine any existing pull request, understand repository flow, and investigate five nonoverlapping scopes through Sonnet subagents. Each run was to choose only one improvement, making the effect easier to attribute.
The verification policy required measurable impact: for a one-to-two-second CLI run, the prompt rejected gains of just a few milliseconds and required at least a two-percent reduction in total execution time. It instructed the agent to benchmark, run lint and tests, and revert changes that missed the threshold. Further instructions covered local subagent review, a single commit, and creating or updating a draft pull request.
These are the registered operating instructions; the supplied excerpt does not enumerate individual optimization diffs or human merge reviews.
Who it's for: CLI maintainers with representative benchmarks and tests who want scheduled proposals for isolated performance improvements.
Prompt
Suggested prompt (written by Agents In Use, not the author's)
Source: dev.to · yamadashy
Frequently asked questions
How many improvements should one run make?
The author’s registered prompt specifies one improvement per run, selecting the highest-impact candidate so the measured change can be attributed.
What happens below the improvement threshold?
The prompt directs the agent to revert changes and report findings if benchmarking does not meet the threshold. It also requires lint and tests.
More details
About this account
Routines ran scheduled prompts in the cloud; the registered policy delegated investigation, implementation, verification, and draft PR preparation.
Platform: dev.to. Attribution: https://dev.to/yamadashy/letting-claude-codes-routines-continuously-tune-my-clis-performance-2kk0. Prompt: Suggested (written by Agents In Use, not the author's). Sourced by the Agents In Use pipeline.
Credit: yamadashy on dev.to
Source published
Added to Agents In Use
Source checked
Tools and topics
Claude Code Routines · Repomix · GitHub · Benchmarks
Scheduled tuning · CLI performance · Benchmarking
Editorial completeness: 100/100. Measures documented fields, not agent performance or verified outcomes.