'curation' 검색 결과

End-to-end Gaia curation pipeline orchestrator. Routes each phase to its dedicated skill and keeps you on track from first discovery to closed intake. Use when starting a fresh curation run or when you want a single skill to hold the sequence together without duplicating each phase's instructions. Trigger phrases: "/gaia-full-pipeline", "run the full pipeline", "start a curation from scratch", "take this skill through the full pipeline", "curate end-to-end", "pipeline run", "full curation flow". Fused from: gaia-curate + gaia-curate-chain + gaia-curate-dynamic + gaia-curate-trending + gaia-bot-curate + gaia-draft-curate + ev-pipeline + gaia-ingest + gaia-intake-close.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

NVIDIA CDF curation and retrieval. Cosmos Curator video and image pipelines (split, dedup, shard, annotate) with filtering, captioning, and embedding. Cosmos Dataset Search ingest, search, and capability qualification. TAO data mining embeddings and neighbor matching. Ships domain event catalogs, including workplace employee conduct monitoring, which requires legal review before use. Use when a request names Cosmos Curator, Cosmos Dataset Search, CDF, or their Make and CLI surfaces. Not for model training, orchestration, or unrelated dataset questions.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Prepare training datasets for large language models with GPU-assisted cleaning. Covers duplicate removal, quality filtering, and personal information redaction, with workflows for text, images, video, and audio using NeMo Curator.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Clean web data for model training
• Remove exact and near duplicates from large text collections
• Redact personal information from training text

Prepare training datasets for large language models with GPU-assisted cleaning. Covers duplicate removal, quality filtering, and personal information redaction, with workflows for text, images, video, and audio using NeMo Curator.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Clean web data for model training
• Remove exact and near duplicates from large text collections
• Redact personal information from training text

Analyze Neuropixels neural recordings from raw data through quality review. Supports preprocessing, motion correction, spike sorting to separate signals by neuron, quality metrics, visual inspection, reports, and exports for further review or data sharing.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preprocess recordings and correct probe drift
• Separate neural signals with Kilosort4
• Select units using Allen or IBL quality criteria

Create, curate, and grow email newsletters across all formats and strategies. Use when working on newsletters, email newsletters, weekly digests, link roundups, curated content, audience building, subscriber engagement, or content distribution. Covers 6 newsletter formats (curated/link roundup, story-driven, educational, interview, data-driven, personal update), editorial issue structure, content sourcing workflows, commentary writing, sending cadence, subject line optimization, subscriber growth, and engagement metrics. Trigger keywords: newsletter curation, curated newsletter, newsletter writing, newsletter format, newsletter strategy, newsletter template, content curation, subscriber growth.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Review AI Gene Review pull requests and curation changes for GO term quality, evidence support, core function identification, and annotation action justification. Use for PR review comments, review follow-up automation, and QA of gene review YAML.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Evaluate and review GO-CAM (Gene Ontology Causal Activity Model) activities / annotons — molecular-function typing, has-input, causal relations, complexes, evidence — and check their consistency with gene annotation reviews. Use when reviewing cached GO-CAMs under gocams/, filling a GoCamReview YAML (gocams/<id>/<id>-review.yaml), or grounding a module in GO-CAM models.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use this agent when you need to review UniProt annotation rules (ARBA, UniRule) for quality, biological accuracy, and GO annotation appropriateness. This agent performs comprehensive analysis of rule condition sets, evaluates literature support, assesses taxonomic scope, and recommends curation actions.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use this agent when you need to systematically review existing GO annotations for a gene and make curation decisions based on literature evidence and functional analysis. This agent should be called after seeding the SPECIES/GENE/GENE-ai-review.yaml file, which seeds each annotation with `action: PENDING`; these should all be manually reviewed.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Curate, review, or repair ai-gene-review ModuleReview YAML documents under modules/, including pathway/module boundary setting, parts and variant modeling, annoton-level molecular functions, representative UniProt/PTN grounding, module deep-research provenance, validation, rendering, and project batch updates.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Close out a review/meta branch that lands an evidence-ingest / curation batch: create the named nodes, ingest verified evidence, appraise Trust Magnitude, calibrate stars, wire suites, correct upstream naming, stage ONLY the intended artifacts (dropping CRLF churn and blocking leaks), validate, and open the PR. Use when a curation/ingest batch is verified and ready to land, or when the user says "close the review/meta branch", "land the ingest batch", "wrap up the intake PR", "/gaia-review-meta-close". Wraps the deterministic hygiene in scripts/review_meta_close.py and gates the judgment calls (calibration, Origin, suite promotion, naming, merge) behind explicit human checkpoints.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Review a Gaia curation PR or branch as a read-only merge gate. Use for canonical registry promotion, schema, evidence, prerequisite, nomenclature, installability, and generated-artifact checks. Route intake batches to /gaia-draft-curate and unrelated PRs to /review.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Inspect or exercise the paused gaia curate orchestration scaffold for one source URL. The command currently persists an INITIALIZED run ledger and reports status; its 25 named workflow states and transition methods are a design skeleton, not an implemented automated curation or merge pipeline. Use only for scaffold development, status inspection, or dry-run contract work. For live curation, use gaia-full-pipeline or gaia-consult. Trigger phrases: "/gaia-quick-curate", "inspect gaia curate", "curate scaffold", "quick curate status". For batch/multi-page runs use gaia-curate-dynamic; for crawler branches use gaia-bot-curate; for pending intake proposals use gaia-draft-curate.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Prepare high-quality datasets for LLM fine-tuning with filtering, deduplication, augmentation, and RLHF data formatting. Activate on: fine-tuning data, training data curation, RLHF dataset, data quality filtering, SFT dataset. NOT for: model training infrastructure (ai-engineer), prompt engineering without fine-tuning (prompt-engineer).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

GPU-accelerated data curation for LLM training. Supports text/image/video/audio. Features fuzzy deduplication (16× faster), quality filtering (30+ heuristics), semantic deduplication, PII redaction, NSFW detection. Scales across GPUs with RAPIDS. Use for preparing high-quality training datasets, cleaning web data, or deduplicating large corpora.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Prepare high-quality datasets for LLM fine-tuning with filtering, deduplication, augmentation, and RLHF data formatting. Activate on: fine-tuning data, training data curation, RLHF dataset, data quality filtering, SFT dataset. NOT for: model training infrastructure (ai-engineer), prompt engineering without fine-tuning (prompt-engineer).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

GPU-accelerated data curation for LLM training. Supports text/image/video/audio. Features fuzzy deduplication (16× faster), quality filtering (30+ heuristics), semantic deduplication, PII redaction, NSFW detection. Scales across GPUs with RAPIDS. Use for preparing high-quality training datasets, cleaning web data, or deduplicating large corpora.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

NeMo Curator로 LLM 학습 데이터의 중복과 저품질 내용을 제거하고 개인정보를 삭제하거나 대체합니다. 처리 흐름 구성과 이미지, 영상, 음성 정리도 다룹니다.

• 웹 수집 문장을 학습용으로 정리합니다
• 완전 일치, 유사, 의미 기반 중복을 제거합니다
• 텍스트의 개인정보를 삭제하거나 대체합니다

지정한 주제의 뉴스와 자료를 여러 웹 출처에서 조사해 출처 링크와 해설을 담은 뉴스레터를 작성합니다. 독자와 발행 주기에 맞춰 구성과 문체를 조정하고 Markdown으로 완성합니다.

• 특정 주제의 주간 뉴스레터 작성
• 경영진을 위한 업계 동향 정리
• 일반 독자를 위한 최신 뉴스 요약

Analyze Neuropixels neural recordings with SpikeInterface, from loading and preprocessing to motion correction and spike sorting, which separates signals by neuron. Calculate quality metrics, review candidate units, and export plots and data for further inspection.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Inspect and preprocess SpikeGLX, Open Ephys, or NWB recordings
• Check and correct movement-related drift before spike sorting
• Evaluate and select units using quality metrics

Helps organize biological datasets and models in LaminDB. Covers data registration, table validation, annotation with standardized biological terms, metadata searches, and tracking analysis inputs and outputs so researchers can preserve the context needed to repeat their work.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Register and organize biological datasets
• Validate DataFrame or AnnData metadata
• Standardize annotations with biological vocabularies

Prepare datasets for language model training by formatting examples, checking conversation templates, and packing examples into training sequences. Document data sources, synthetic data ratios, and validation results in a dataset card before starting a run.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Convert raw examples into training-ready JSONL
• Check conversation templates and assistant-only training targets
• Review example boundaries after sequence packing

Turn graded execution traces and production logs into model training examples and preferred-versus-rejected response pairs. Select useful records, remove sensitive information, exclude evaluation examples, and preserve links to the original runs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Convert high-scoring successful runs into training examples
• Use expert-corrected failures as training examples
• Pair passing and failing responses to the same task