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Roger-luo
GitHub 제작자 프로필

Roger-luo

6개 GitHub 저장소에서 수집된 16개 skills를 저장소 단위로 보여줍니다.

수집된 skills
16
저장소
6
업데이트
2026-07-18
저장소 탐색

저장소와 대표 skills

dogfooding-ion
소프트웨어 개발자

Use when hardening Ion's user experience by driving the ion CLI end-to-end against a real project and improving what feels rough — i.e. "dogfood ion on <project>", "use ion in X and fix the UX gaps", "is ion actually pleasant to use", "does ion guide the agent to the next step", or any shakedown of the CLI from a user's seat. Dogfooding judges three things at once: (1) human terminal UX — is plain (non-JSON) output friendly and readable; (2) progressive prompting — does every command's output (human text AND the --json envelope) make the next step obvious so a user or agent never stalls; (3) workflow ergonomics — does a real goal take too many commands, calling for a new or consolidated command rather than another hint. Covers driving the full skill lifecycle on a scratch repo and an established one, logging each rough edge, and FIXING the CLI (next-step hints, richer action_required envelopes, friendlier output, new/merged commands) with a failing-test-first discipline using the scenario crate + integration

2026-07-18
name
기타 컴퓨터 관련 직업

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2026-03-29
alpha-name
기타 컴퓨터 관련 직업

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2026-03-29
beta-name
기타 컴퓨터 관련 직업

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2026-03-29
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기타 컴퓨터 관련 직업

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2026-03-29
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기타 컴퓨터 관련 직업

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2026-03-29
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기타 컴퓨터 관련 직업

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2026-03-29
name
기타 컴퓨터 관련 직업

A test skill

2026-03-29
autotune
소프트웨어 개발자

Use when running autotune — the autonomous, metric-driven code-tuning CLI (assumed already installed) — on a project to improve a measurable metric such as benchmark speed, test coverage, binary size, or model accuracy (e.g. "use autotune to speed up X", "tune this project with autotune", "have autotune improve my benchmark/coverage"). Also use when an autotune run misbehaves (crashes, hangs, banks no real improvement, scores real wins as noise) or lacks a config option / adaptor / behavior your project needs.

2026-06-24
dogfooding-autotune
소프트웨어 개발자

Use when hardening the autotune CLI by running it end-to-end against a real target repo — i.e. "try autotune on <project> and fix what breaks", "run autotune against X and watch for malfunctions", "can autotune actually improve X", or any dogfooding/shakedown of the tune loop. Dogfooding has two goals: (1) robustness — the loop doesn't crash, hang, orphan processes, fill the disk, or record the wrong outcome; and (2) efficacy — autotune produces meaningful, correct improvements (good hypotheses, real wins that clear the noise envelope, not gamed/overfit). Covers launching a traced unattended run, watching the trace/logs/ledger, diagnosing via trace `phase.decision`, fixing bugs OR improving autotune itself (research prompts, scoring, specialization) with a failing-test-first discipline, and rerunning until N iterations complete cleanly and the wins are judged real. Not for plain unit changes — just run nextest.

2026-06-24
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