'loops' 검색 결과

Expert in designing and optimizing growth loops - self-reinforcing systems where the output of one cycle becomes the input for the next. Covers viral loops, content loops, paid loops, sales loops, and product loops. Knows how to identify, measure, and accelerate the specific loop mechanics that drive sustainable growth. Use when "growth loop, viral loop, content loop, flywheel, compounding growth, acquisition loop, referral loop, " mentioned.

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Calls significant loops from protein-directed and targeted 3C assays (HiChIP, PLAC-seq, Capture Hi-C/PCHi-C, ChIA-PET) where the contact background is peak-anchored and coverage-biased, so generic Hi-C loop callers (cooltools dots, Juicer HiCCUPS) use the wrong null. Covers FitHiChIP (config-driven coverage+distance-decay spline regression, peak-to-peak vs peak-to-all foreground, loose vs stringent background, coverage vs ICE bias), MAPS (positive Poisson regression on bias factors for PLAC-seq/HiChIP), hichipper (restriction-site-distance bias model + library QC), CHiCAGO (Delaporte two-component Brownian+technical background for asymmetric bait x other-end Capture Hi-C), the with/without separate-ChIP anchor decision, and differential loops via diffloop. Use when calling loops from HiChIP/PLAC-seq/Capture Hi-C, choosing FitHiChIP/MAPS/CHiCAGO, picking peak-to-all vs peak-to-peak, setting the loop FDR, supplying ChIP peaks as anchors, QCing a HiChIP library, or comparing loops between conditions.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Calls significant loops from protein-directed and targeted 3C assays (HiChIP, PLAC-seq, Capture Hi-C/PCHi-C, ChIA-PET) where the contact background is peak-anchored and coverage-biased, so generic Hi-C loop callers (cooltools dots, Juicer HiCCUPS) use the wrong null. Covers FitHiChIP (config-driven coverage+distance-decay spline regression, peak-to-peak vs peak-to-all foreground, loose vs stringent background, coverage vs ICE bias), MAPS (positive Poisson regression on bias factors for PLAC-seq/HiChIP), hichipper (restriction-site-distance bias model + library QC), CHiCAGO (Delaporte two-component Brownian+technical background for asymmetric bait x other-end Capture Hi-C), the with/without separate-ChIP anchor decision, and differential loops via diffloop. Use when calling loops from HiChIP/PLAC-seq/Capture Hi-C, choosing FitHiChIP/MAPS/CHiCAGO, picking peak-to-all vs peak-to-peak, setting the loop FDR, supplying ChIP peaks as anchors, QCing a HiChIP library, or comparing loops between conditions.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Calls significant loops from protein-directed and targeted 3C assays (HiChIP, PLAC-seq, Capture Hi-C/PCHi-C, ChIA-PET) where the contact background is peak-anchored and coverage-biased, so generic Hi-C loop callers (cooltools dots, Juicer HiCCUPS) use the wrong null. Covers FitHiChIP (config-driven coverage+distance-decay spline regression, peak-to-peak vs peak-to-all foreground, loose vs stringent background, coverage vs ICE bias), MAPS (positive Poisson regression on bias factors for PLAC-seq/HiChIP), hichipper (restriction-site-distance bias model + library QC), CHiCAGO (Delaporte two-component Brownian+technical background for asymmetric bait x other-end Capture Hi-C), the with/without separate-ChIP anchor decision, and differential loops via diffloop. Use when calling loops from HiChIP/PLAC-seq/Capture Hi-C, choosing FitHiChIP/MAPS/CHiCAGO, picking peak-to-all vs peak-to-peak, setting the loop FDR, supplying ChIP peaks as anchors, QCing a HiChIP library, or comparing loops between conditions.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Compare five growth loops, mechanisms where user activity brings in more users, to find a suitable approach for a product. Develop an implementation roadmap, measurement plan, and growth estimates based on sharing, collaboration, content, or referrals.

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• Designing product features that attract new users
• Planning growth with less reliance on paid acquisition
• Comparing sharing, collaboration, and referral mechanisms

Turn an approved character still into animated game assets, with a separate video for each action. Process generated or supplied clips into transparent, aligned frames, adjust loops and timing, and export images, video, and playback metadata.

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• Build a character animation set from one still
• Convert a supplied clip into transparent game assets
• Adjust walking loops and attack timing

Detects focal chromatin loops (point interactions / corner-dots) in balanced Hi-C and Micro-C contact maps and aggregates/validates a loop set. Covers de-novo calling with cooltools dots (HiCCUPS-style 4-background local enrichment with lambda-chunked FDR), chromosight (template-correlation), and Mustache (scale-space blob detection); aggregate peak analysis (APA) via cooltools pileup for confirmation; the depth/resolution prerequisite (de-novo needs ~5-10kb resolution = hundreds of millions to billions of valid pairs); consensus across callers and convergent-CTCF support as validation; and differential loops via union anchors plus chromosight quantify. Use when calling chromatin loops or dots from a cooler, deciding whether a map is deep enough to call de-novo vs running APA on known CTCF/cohesin anchors, building an aggregate peak pileup, comparing loops across conditions, or validating loop calls. For HiChIP/PLAC-seq/PCHi-C protein-anchored data use FitHiChIP/MAPS, not dots.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Detects focal chromatin loops (point interactions / corner-dots) in balanced Hi-C and Micro-C contact maps and aggregates/validates a loop set. Covers de-novo calling with cooltools dots (HiCCUPS-style 4-background local enrichment with lambda-chunked FDR), chromosight (template-correlation), and Mustache (scale-space blob detection); aggregate peak analysis (APA) via cooltools pileup for confirmation; the depth/resolution prerequisite (de-novo needs ~5-10kb resolution = hundreds of millions to billions of valid pairs); consensus across callers and convergent-CTCF support as validation; and differential loops via union anchors plus chromosight quantify. Use when calling chromatin loops or dots from a cooler, deciding whether a map is deep enough to call de-novo vs running APA on known CTCF/cohesin anchors, building an aggregate peak pileup, comparing loops across conditions, or validating loop calls. For HiChIP/PLAC-seq/PCHi-C protein-anchored data use FitHiChIP/MAPS, not dots.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Detects focal chromatin loops (point interactions / corner-dots) in balanced Hi-C and Micro-C contact maps and aggregates/validates a loop set. Covers de-novo calling with cooltools dots (HiCCUPS-style 4-background local enrichment with lambda-chunked FDR), chromosight (template-correlation), and Mustache (scale-space blob detection); aggregate peak analysis (APA) via cooltools pileup for confirmation; the depth/resolution prerequisite (de-novo needs ~5-10kb resolution = hundreds of millions to billions of valid pairs); consensus across callers and convergent-CTCF support as validation; and differential loops via union anchors plus chromosight quantify. Use when calling chromatin loops or dots from a cooler, deciding whether a map is deep enough to call de-novo vs running APA on known CTCF/cohesin anchors, building an aggregate peak pileup, comparing loops across conditions, or validating loop calls. For HiChIP/PLAC-seq/PCHi-C protein-anchored data use FitHiChIP/MAPS, not dots.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

AI 에이전트의 반복 작업을 설계·점검합니다. 기계로 판정할 완료 조건, 변경 금지 경계, 독립 검증과 재시도 상한을 정해 헛돌거나 통제를 벗어나는 일을 방지합니다.

• 반복 작업의 완료 조건 정의
• 헛도는 반복과 검증 우회 점검
• 계획·구현·검증의 역할 분담

GitHub·Discord·X의 커뮤니티 성장 방안을 게시 계획과 성과로 이어지는 수치와 함께 정리하여 코럴 색상의 HTML 슬라이드로 만듭니다.

• 커뮤니티 성장 계획 제안
• GitHub·Discord·X 게시 계획
• 성장 구조와 관련 수치 설명

Design loops that improve an agent’s own prompts, workflows, or execution code from failure records. Define bounded edits, preserve candidate histories, and accept changes through independent tests that check improvements and regressions.

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• Designing agent self-improvement from failure records
• Defining acceptance checks for self-modifying workflows
• Diagnosing score gaming and stalled improvement

Design the controls around autonomous agents: what they can edit, how results are evaluated, how progress is logged, and when human approval is required. Build research and experiment loops with clear recovery and review rules.

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• Designing autonomous research and experiment loops
• Separating editable work from fixed evaluation rules
• Preserving progress and failed attempts across long runs

Optimize .NET loops with SIMD, which processes multiple values in one operation, or review existing SIMD code. Check behavioral equivalence, memory safety, platform fallbacks, and benchmarks to establish whether the optimization is correct and worthwhile.

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• Optimize numeric loops with SIMD
• Review existing vectorized code for correctness
• Test boundary lengths and overlapping buffers

Investigate fast-agent session files and logs to diagnose unexpected endings, tool loops, and mismatched tool responses. Inspect conversation history, summarize model and tool execution times, and connect sub-agent traces with the corresponding main-session activity.

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• Investigate an unexpectedly ended session
• Check tool loops and missing tool responses
• Identify slow model calls and tool executions

Run, monitor, resume, merge, and debug Ralph loops. Use this skill whenever the user asks to operate `ralph run` or `ralph loops`, inspect loop state, recover suspended loops, analyze diagnostics, or unblock merge queue issues.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Detect chromatin loops and point interactions from Hi-C data using cooltools, chromosight, and HiCCUPS-like methods. Identify CTCF-mediated loops and enhancer-promoter contacts. Use when detecting chromatin loops from Hi-C data.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Read and write Loops: find and manage email contacts; send events and transactional emails. Trigger phrases: loops, email contacts.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Expert en agent loops (ARIS, Ralph, recherche autonome, build-score-budget, guardrails)

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

PyTorch 모델과 학습 코드 작성 및 검토를 돕습니다. 데이터 로딩, 실험 재현성, 학습 상태 저장과 복원, GPU 메모리 및 속도 개선을 다룹니다.

• PyTorch 학습 코드 작성과 검토
• 실험 결과가 달라지는 원인 조사
• 로딩 속도와 GPU 메모리 개선

Claude Code에서 개발 작업을 반복하는 자율 루프 설계를 안내합니다. 순차 실행부터 병렬 작업까지 진행 상황 인계, 품질 확인, 변경 사항 통합과 중단 조건을 정리합니다.

• 순차적으로 실행할 개발 절차를 설계할 때
• 반복 실행 사이에 진행 상황을 인계할 때
• 병렬 작업과 변경 사항 통합을 조율할 때

인공지능 에이전트의 문제를 지시, 기억, 도구 실행, 화면 표시 등 12개 계층에서 조사합니다. 코드와 로그를 근거로 원인을 정리하고 심각도에 따른 지적 사항과 수정안을 제시합니다.

• 앱 안에서만 발생하는 문제 조사
• 이전 대화가 섞이는 원인 확인
• 도구 실행 누락과 허위 실행 보고 확인

제품 공개, 예고편, 배경 반복 영상에 쓰는 3~10초 동영상을 생성합니다. 피사체, 움직임, 조명을 정리하고 지정한 길이와 화면 비율로 MP4를 저장합니다.

• 제품 공개 영상 제작
• 움직임이 있는 짧은 예고편 제작
• 분위기를 전달하는 배경 반복 영상 제작

RTK의 Rust 코드에서 장황한 반복문, 불필요한 복사, 과도한 설계를 정리합니다. 동작과 오류 처리, 종료 코드 등 필수 요구 사항을 유지하며 간결하게 만듭니다.

• 장황한 반복문과 문자열 처리를 정리할 때
• 불필요한 문자열 복사를 줄일 때
• 조건 분기와 오류 처리의 중첩을 정리할 때