2,346 件 ・ 関連度順
概要と使いどころ
Sequence-based deep learning for ATAC-seq using chromBPNet, BPNet, scBasset, or EnFormer. Use when correcting Tn5 bias with neural networks beyond k-mer models, predicting per-base accessibility profiles, scoring in silico variant effects at GWAS or rare-variant SNPs, discovering motifs via DeepLIFT/TF-MoDISco from a trained model, or generating cell-type-specific accessibility predictions for unobserved cell states.
日本語の概要は準備中です。原文の説明を表示しています。
Shared vocabulary for designing deep modules. Use when the user wants to design or improve a module's interface, find deepening opportunities, decide where a seam goes, make code more testable or AI-navigable, or when another skill needs the deep-module vocabulary.
日本語の概要は準備中です。原文の説明を表示しています。
Targeted deep research to fill a specific gap in an existing thesis. Use when user says "deepen", "flesh out", "expand on", "fill in", or specifies a thesis section to improve. Also supports --sync-metrics mode — detects stale financial metrics across every in-scope analytical section of a thesis (not just Key Metrics or Summary) and coherently updates every sentence that depends on them, using a Key-Metrics-table / FMP / web-search waterfall. Use when user says "sync metrics", "update the numbers throughout the note", or "fix stale figures across the whole thesis". The batch form `--sync-metrics --all-flagged` syncs every thesis flagged by the latest `/numbers --all-open` in one operation — detection fanned out to capped READ-ONLY agents, one consolidated confirmation gate, direct sequential writes (agents never write).
日本語の概要は準備中です。原文の説明を表示しています。
Targeted deep research to fill a specific gap in an existing thesis. Use when user says "deepen", "flesh out", "expand on", "fill in", or specifies a thesis section to improve. Also supports --sync-metrics mode — detects stale financial metrics across every in-scope analytical section of a thesis (not just Key Metrics or Summary) and coherently updates every sentence that depends on them, using a Key-Metrics-table / FMP / web-search waterfall. Use when user says "sync metrics", "update the numbers throughout the note", or "fix stale figures across the whole thesis". The batch form `--sync-metrics --all-flagged` syncs every thesis flagged by the latest `$numbers --all-open` in one operation — detection fanned out to capped READ-ONLY agents, one consolidated confirmation gate, direct sequential writes (agents never write).
日本語の概要は準備中です。原文の説明を表示しています。
Visualises RNA-modification data with transcript-feature metagene plots (Guitar GuitarPlot with 5'UTR / CDS / 3'UTR scaling; MetaPlotR; deepTools `computeMatrix scale-regions`), peak-centred heatmaps (ComplexHeatmap; deepTools plotHeatmap), IP-vs-input paired browser tracks (bigWig of log2 IP/input via deepTools `bamCompare`; ggcoverage; pyGenomeTracks; Gviz; IGV / UCSC track hubs), DRACH sequence-logo plots (ggseqlogo; MEME), 5'UTR / CDS / 3'UTR stacked-bar feature-distribution summaries, and volcano / MA plots for differential modification. Establishes stop-codon enrichment in the metagene plot as the biological QC anchor for any MeRIP dataset (Dominissini 2012 *Nature* 485:201; Meyer 2012 *Cell* 149:1635 — concurrent founding papers from different labs both showed this independently). Use when producing the canonical metagene plot with stop-codon enrichment as a QC anchor, building paired IP/input genome-browser tracks at single-locus resolution, plotting peak-centred heatmaps clustered by condition, summarising peak distribution across transcript features for figure 1, generating DRACH motif logos as sanity checks on the peak set, rendering volcano plots of differential m6A, or reproducing the Dominissini 2012 / Meyer 2012 stop-codon enrichment plot.
日本語の概要は準備中です。原文の説明を表示しています。
Sequence-based deep learning for ATAC-seq using chromBPNet, BPNet, scBasset, or EnFormer. Use when correcting Tn5 bias with neural networks beyond k-mer models, predicting per-base accessibility profiles, scoring in silico variant effects at GWAS or rare-variant SNPs, discovering motifs via DeepLIFT/TF-MoDISco from a trained model, or generating cell-type-specific accessibility predictions for unobserved cell states.
日本語の概要は準備中です。原文の説明を表示しています。
Predicts whether a DNA variant alters mRNA splicing using sequence-based deep-learning tools — SpliceAI (10kb context dilated CNN, clinical default), Pangolin (multi-tissue), MMSplice (modular per-region CNN with calibrated ΔPSI), SpliceTransformer/TrASPr (tissue-aware transformers), SpliceVault (empirical 300K-RNA lookup of likely mis-splicing outcomes), CADD-Splice (composite score). Applies the ClinGen SVI 2023 framework for ACMG/AMP variant interpretation (PVS1, PP3, BP4 evidence codes), HGVS splicing nomenclature (c.123+1G>A, c.123-3T>G, r.spl?), extended-window scoring for deep-intronic pseudoexons, tissue-specific predictions, branchpoint variant detection (BPHunter, LaBranchoR), and splice-switching ASO design. Use when interpreting splice impact of clinical variants, prioritizing VUS, identifying deep-intronic pathogenic variants, or designing ASOs.
日本語の概要は準備中です。原文の説明を表示しています。
[31] ANALYZE. Universal deep thinking methodology for any situation requiring quality reasoning. Use when solving problems, debugging, making decisions, analyzing code, planning, reviewing, or anytime you need thorough thinking instead of surface-level responses. Triggers on "think deeply", "analyze thoroughly", "reason carefully", "deep thinking", "understand completely", or any task requiring careful thought.
日本語の概要は準備中です。原文の説明を表示しています。
Specialized skill for handling complex object comparison and deep validation. Use when you need to compare deep objects, exclude specific properties, handle circular references, or validate DTOs/Entities. Covers BeEquivalentTo, Excluding, Including, custom comparison rules, etc. Keywords: object comparison, deep comparison, BeEquivalentTo, DTO comparison, Entity validation, excluding properties, circular reference, Excluding, Including, ExcludingNestedObjects, RespectingRuntimeTypes, WithStrictOrdering, ignore timestamp, exclude timestamp
日本語の概要は準備中です。原文の説明を表示しています。
Deep Research Agent specializes in complex, multi-step research tasks that require planning, decomposition, and long-context reasoning across tools and files by we-crafted.com/agents/deep-research
日本語の概要は準備中です。原文の説明を表示しています。
TypeScriptのパッケージ内部への直接参照や循環依存を検出し、公開ファイルだけを入口にする構成を、設定・検証・文書化まで整えるスキル。
Deep agentic-flow@alpha integration implementing ADR-001. Eliminates 10,000+ duplicate lines by building claude-flow as specialized extension rather than parallel implementation.
日本語の概要は準備中です。原文の説明を表示しています。
Conduct multi-round deep research on any GitHub Repo. Use when users request comprehensive analysis, timeline reconstruction, competitive analysis, or in-depth investigation of GitHub. Produces structured markdown reports with executive summaries, chronological timelines, metrics analysis, and Mermaid diagrams. Triggers on Github repository URL or open source projects.
日本語の概要は準備中です。原文の説明を表示しています。
Automate Deepgram tasks via Rube MCP (Composio). Always search tools first for current schemas.
日本語の概要は準備中です。原文の説明を表示しています。
Orchestrate multi-phase deep research with web search, memory retrieval, pattern matching, and synthesis into structured findings
日本語の概要は準備中です。原文の説明を表示しています。
Deep agentic-flow@alpha integration implementing ADR-001. Eliminates 10,000+ duplicate lines by building claude-flow as specialized extension rather than parallel implementation.
日本語の概要は準備中です。原文の説明を表示しています。
Deep research harness — fan-out web searches, fetch sources, adversarially verify claims, synthesize a cited report.
日本語の概要は準備中です。原文の説明を表示しています。
Research a topic to support a decision, three ways: draft a research prompt for the user to run in their own tool (ChatGPT, Gemini, Grok, Perplexity, …), turn a finished research report into a short summary with cited sources that other skills can use directly, or run the research here with parallel web searches. Built-in research types: market, domain, technical, competitive, user-voice, academic-lit; also supports choosing between candidates, and custom types via overrides. Use when the user says "deep recon", "research this", "draft a research prompt", "process this research report", "market research", "domain research", "technical research", "competitor research", "literature review", or "help me choose between"
日本語の概要は準備中です。原文の説明を表示しています。
Push the LLM to reconsider, refine, and improve its recent output. Use when user asks for deeper critique or mentions a known deeper critique method, e.g. socratic, first principles, pre-mortem, red team
日本語の概要は準備中です。原文の説明を表示しています。
Deep learning framework (PyTorch Lightning / lightning package). Organize PyTorch code into LightningModules, configure Trainers for multi-GPU/TPU, implement data pipelines, callbacks, logging (W&B, TensorBoard, MLflow), distributed training (DDP, FSDP, DeepSpeed), for scalable neural network training.
日本語の概要は準備中です。原文の説明を表示しています。
Generates transcriptome-wide virtual spatial transcriptomics from H&E histology with DeepSpot-M. Used for predicted log1p-CPM expression from 224x224 tiles at about 20x, querying the released protein-coding gene panel by symbol, and whole-slide prediction after resolution-aware tiling with histolab.
日本語の概要は準備中です。原文の説明を表示しています。
Looks up precomputed AlphaGenome Atlas effects for any GRCh38 single-nucleotide variant (AVI score with Phred and 18 SHAP feature attributions, plus raw and quantile scores for RNA-seq, DNase, ATAC, ChIP-TF, ChIP-histone, CAGE, PRO-cap, splicing, polyadenylation and contact-map tracks), scores variants or scans windows on demand with the AlphaGenome model for human and mouse (variant scoring, in silico mutagenesis, REF-versus-ALT track prediction), and builds Atlas website deep links. Use when the user mentions AlphaGenome, AlphaGenome Atlas, AVI or AlphaGenome Variant Impact, DeepMind variant effect prediction, or wants to prioritise or mechanistically interpret non-coding, regulatory, splicing, enhancer, promoter, or chromatin-accessibility effects of SNVs from a VCF, credible set, or region. Research use only; not a clinical tool.
日本語の概要は準備中です。原文の説明を表示しています。
Design and author DeepTutor skills (SKILL.md packages). Use when the user wants to create a new skill, improve an existing skill, or asks how skills work.
日本語の概要は準備中です。原文の説明を表示しています。
Deep Interactive courses. Plans the course so the learner manipulates something on most pages — simulations, diagrams they explore, code they run, games, 3D scenes — with slides reserved for the opening frame and the closing consolidation. Use when the topic has a mechanism, a variable to vary, a process to trace, or a structure to inspect.
日本語の概要は準備中です。原文の説明を表示しています。