1029 个 · 按相关度排序
Validate Claude Ads data contracts, scoring inputs, run outputs, installation procedures, and release readiness. Return machine-readable pass or fail results with evidence, the most urgent blocker, and recovery steps, while checking source freshness and safe removal boundaries.
中文简介正在准备中。当前显示英语简介。
Validate, prepare, or run public CodonFM Encodon masked-codon variant scoring and review compatibility of its scoring workflows. Use only when the user explicitly requests CodonFM or Encodon, or that context is already established in the conversation. Do not select this skill for a generic variant-scoring request without that context; ask for the variant and intended analysis first.
中文简介正在准备中。当前显示原文说明。
Infer transcription factor regulons from single-cell RNA-seq with pySCENIC by combining GRNBoost2 co-expression, cisTarget motif-enrichment pruning, and AUCell per-cell activity scoring. Covers the motif-pruning-as-directionality principle, regulon specificity scoring, run-to-run stability, and database/species matching. Use when identifying TF regulons, scoring TF activity per cell, finding master regulators of cell identity, or comparing regulon activity across conditions. For enhancer-driven multiomic GRNs see multiomics-grn; for bulk inference and VIPER protein-activity see grn-inference.
中文简介正在准备中。当前显示原文说明。
Provides R wrappers of several on-target and off-target scoring methods for CRISPR guide RNAs (gRNAs). The following nucleases are supported: SpCas9, AsCas12a, enAsCas12a, and RfxCas13d (CasRx). The available on-target cutting efficiency scoring methods are RuleSet1, Azimuth, DeepHF, DeepCpf1, enPAM+GB, and CRISPRscan. Both the CFD and MIT scoring methods are available for off-target specificity prediction. The package also provides a Lindel-derived score to predict the probability of a gRNA to
中文简介正在准备中。当前显示原文说明。
JTBD opportunity scoring and prioritization - outcome statement format, opportunity algorithm, scoring interpretation, feature prioritization, and opportunity matrix template
中文简介正在准备中。当前显示原文说明。
When a founder needs to qualify inbound leads, define their ICP, build a lead scoring model, set MQL criteria, or route prospects through pipeline stages. Activate when the user mentions lead scoring, ICP, MQL, SQL, lead qualification, inbound leads, or pipeline design.
中文简介正在准备中。当前显示原文说明。
Vendor privacy risk tiering methodology for processor management. Covers scoring factors including data volume, sensitivity, transfer locations, certifications, breach history, and control maturity with weighted risk calculation and tier assignment.
中文简介正在准备中。当前显示原文说明。
Guides comprehensive controller self-assessment covering GDPR Articles 5-49 with scoring methodology and reporting format. Activate when conducting internal reviews or benchmarking maturity. Keywords: self-assessment, controller assessment, compliance questionnaire, scoring.
中文简介正在准备中。当前显示原文说明。
Provides combined DPIA and AI Act conformity assessment template with integrated risk scoring matrix. Covers GDPR Art. 35 DPIA elements, AI Act high-risk system requirements, mitigation measures, and human oversight assessment. Keywords: DPIA template, conformity assessment, risk scoring, AI Act, combined assessment, high-risk AI.
中文简介正在准备中。当前显示原文说明。
ESM2 protein language model for sequence scoring, embeddings, and plausibility checks. Use this skill when: (1) Computing pseudo-log-likelihood (PLL) scores, (2) Getting protein embeddings for clustering, (3) Filtering designs by sequence plausibility, (4) Zero-shot variant effect prediction, (5) Analyzing sequence-function relationships. For structure prediction, use chai1-structure-prediction or boltz-structure-prediction. For QC thresholds, use protein-design-qc.
中文简介正在准备中。当前显示原文说明。
Use when scoring leads, qualifying prospects, prioritizing pipeline, deciding which accounts to pursue, or building a lead scoring model. Triggers: 'score this lead', 'qualify this prospect', 'is this a good lead', 'lead scoring', 'prioritize my pipeline', 'qualification framework'.
中文简介正在准备中。当前显示原文说明。
Gate resumption of any multi-day LLM batch-scoring campaign that calls an unpinned model alias (deepseek-chat, gpt-*-latest, gemini-*-preview, any provider alias without a pinned version). Use when: (1) resuming a paused or credit-exhausted scoring run days after its last chunk, (2) topping up credits to finish a campaign, (3) extending a cached scoring pipeline with new items. Prevents silently splicing two model versions or serving revisions into one measure. Verified 2026-07-16: for $0.30 caught a serving-revision drift WITHIN DeepSeek v4-flash (same alias, same family, litigation scores systematically shifted across a 2-day gap) before an $83 resume spend.
中文简介正在准备中。当前显示原文说明。
Infer transcription factor regulons from single-cell RNA-seq with pySCENIC by combining GRNBoost2 co-expression, cisTarget motif-enrichment pruning, and AUCell per-cell activity scoring. Covers the motif-pruning-as-directionality principle, regulon specificity scoring, run-to-run stability, and database/species matching. Use when identifying TF regulons, scoring TF activity per cell, finding master regulators of cell identity, or comparing regulon activity across conditions. For enhancer-driven multiomic GRNs see multiomics-grn; for bulk inference and VIPER protein-activity see grn-inference.
中文简介正在准备中。当前显示原文说明。
Hybrid ML + physics binding affinity prediction. Empirical scoring, MM/GBSA rescoring, multi-method consensus, and batch virtual screening for protein-ligand complexes.
中文简介正在准备中。当前显示原文说明。
スコアリング / ML のドメイン正しさのレビュー。SCORE_WEIGHTS の単一定義と合計1.0・ドキュメント追随、スコアリングと ML の10要素の一致（手書きで並ぶ全箇所の同時更新）、未来情報リーク（as-of カットオフを通していない取得経路の新設）、値域0〜100とゼロ除算、データ欠損時の既定値、学習・予測の決定性と再現性、JRA 枠番割当規則、バックテスト指標（1位的中率・上位3頭精度・順位相関・ROI）の計算を検証。Use when user says 'スコアリングレビュー', 'review scoring', 'ML レビュー', 'スコア計算チェック', '特徴量チェック', or 'バックテスト検証'.
中文简介正在准备中。当前显示原文说明。
Infer transcription factor regulons from single-cell RNA-seq with pySCENIC by combining GRNBoost2 co-expression, cisTarget motif-enrichment pruning, and AUCell per-cell activity scoring. Covers the motif-pruning-as-directionality principle, regulon specificity scoring, run-to-run stability, and database/species matching. Use when identifying TF regulons, scoring TF activity per cell, finding master regulators of cell identity, or comparing regulon activity across conditions. For enhancer-driven multiomic GRNs see multiomics-grn; for bulk inference and VIPER protein-activity see grn-inference.
中文简介正在准备中。当前显示原文说明。
Design structured evaluation rubrics for scoring LLM and agent outputs — defining quality dimensions, scoring scales, hard gates, score descriptions, and edge cases. Load when the user asks to create an eval rubric, define evaluation criteria, design scoring dimensions, write an eval spec, or says "what should I evaluate", "design a rubric", "create eval criteria", "define quality dimensions", "evaluation rubric for", "how do I measure quality of". Sub-skill of eval-output orchestrator.
中文简介正在准备中。当前显示原文说明。
Infer transcription factor regulons from single-cell RNA-seq with pySCENIC by combining GRNBoost2 co-expression, cisTarget motif-enrichment pruning, and AUCell per-cell activity scoring. Covers the motif-pruning-as-directionality principle, regulon specificity scoring, run-to-run stability, and database/species matching. Use when identifying TF regulons, scoring TF activity per cell, finding master regulators of cell identity, or comparing regulon activity across conditions. For enhancer-driven multiomic GRNs see multiomics-grn; for bulk inference and VIPER protein-activity see grn-inference.
中文简介正在准备中。当前显示原文说明。
按九个维度为筛选后的竞争对手评分，整理成附有依据的对比卡片和战略双轴图，查看各公司的优势，以及哪些竞争对手已进入自身目标区域。
将按用户或情境选择优先候选项的推荐与排序流程，拆分为获取、补充信息、过滤、评分、选择和记录六个阶段，并生成可运行的代码框架。
用于开发嵌入电子病历的临床决策支持，涵盖药物相互作用、剂量验证和临床评分，并按警报严重程度设计界面行为及检测漏报警的测试。
帮助构建定期采集公开网站或API中职位、价格、新闻等信息的系统，设计AI摘要、分类、评分和存储流程，并将用户的采纳或拒绝记录用于后续评分。
按共同标准为竞争对手的品牌表达、服务和实绩等评分，生成附有依据的比较卡片及战略双轴图，了解哪些对手已占据客户希望争取的位置。
Compare Claude, GPT, and Gemini on the same skill or prompt. Get a table of response times, token usage, and costs, with optional AI scoring to help choose a model for the task.
中文简介正在准备中。当前显示英语简介。