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tenki-labs
GitHub 제작자 프로필

tenki-labs

2개 GitHub 저장소에서 수집된 6개 skills를 저장소 단위로 보여줍니다.

수집된 skills
6
저장소
2
업데이트
2026-06-27
저장소 탐색

저장소와 대표 skills

reflect
경영 분석가

Use when the user types /reflect or asks to "think this through", "weigh the pros and cons", "consider all angles", "challenge my thinking", "stress-test this decision", "second opinion on", "hva er sterkeste argumentet mot dette", "reflekter over". Runs a structured multi-method reflection (first principles + steelmanning + pre-mortem + inversion + stakeholder views + second-order thinking) and surfaces the tensions before landing on a position.

2026-05-09
bokmaal-proof
기술 작가

Use when the user types /bokmaal-proof, /bokmål-proof, or asks to "proof this Norwegian text", "rydd opp i denne teksten", "fix this bokmål", "check the Norwegian", "remove anglicisms", or wants stylistic and register-level review of Norwegian bokmål technical or business writing. Does more than grammar: catches anglicisms, register drift, Språkrådet recommendations, and the typical translation-from-English smell.

2026-05-09
design-ai-experiment
데이터 과학자

Use when the user types /design-ai-experiment or asks to "design an experiment", "set up an A/B test for an AI feature", "evaluate a model rollout", "pre-register a study", "validate an LLM change", "prove this works", or wants to turn a vague AI idea into a falsifiable test with a hypothesis, control, success threshold, and kill criterion. Borrows pre-registration discipline from academia.

2026-05-09
ml-or-not
데이터 과학자

Use when the user types /ml-or-not or asks "should we use ML for this", "do we need a model here", "is this an AI problem", "would ML help", or is scoping a feature where machine learning is being considered. Returns a structured verdict (yes / no / not yet) with reasoning and the cheapest viable alternative if ML is wrong.

2026-05-09
zk-llm-design
정보 보안 분석가

Use when the user types /zk-llm-design or asks how to add zero-knowledge proofs to an LLM system, "prove model output without revealing weights", "ZK-ML", "verifiable inference", "trustless model serving", or wants help choosing between Halo2, Plonk, STARKs, or FHE for an LLM use case. Walks through threat model, what is being proved, scheme selection, and feasibility for a given model size.

2026-05-09
저장소 2개 중 2개 표시
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