1,259 件 ・ 関連度順
概要と使いどころ
Use Luma AI's first-party Photon and Photon Flash image API for text-to-image, image/style/character references, image modification, asynchronous generation, secure artifact handling, production prompting, cost control, and policy-aware delivery. Trigger for Luma Photon API integration or production work; do not use for Ray video, UNI image models, the Luma consumer app, or third-party Photon gateways.
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。
Segments the genome into chromatin states from combinatorial histone modification and chromatin factor ChIP-seq data. Uses ChromHMM (multivariate HMM on binarized signal, v1.27), Segway (Dynamic Bayesian Network on continuous signal), EpiSegMix (flexible-distribution HMM with duration modeling, 2024), EpiLogos (multi-biosample visualization), IDEAS (cell-type-aware joint), and full-stack ChromHMM (Vu Ernst 2022) for cross-cell-type segmentations. Handles state-count selection (15 vs 18 vs 25 states), binarization choice, OverlapEnrichment / NeighborhoodEnrichment downstream analysis, and cross-biosample integration. Use when learning chromatin states from a histone mark panel, characterizing learned states by genomic feature enrichment, or comparing chromatin landscapes across cell types.
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。
Trains and applies base-resolution deep learning models on ChIP-seq / ChIP-nexus / CUT&RUN data. Uses BPNet (Avsec 2021 Nat Genet 53:354; soft motif syntax from ChIP-nexus), chromBPNet (Pampari A et al 2025 Nat Genet; bias-factorized base-resolution profiles), EnFormer (Avsec 2021 Nat Methods 18:1196; 196 kb input, ~100 kb effective receptive field), DeepSEA (Zhou 2015; multi-task CNN), and JASPAR 2026 deep-learning collection (1259 BPNet ChIP models). Performs in silico mutagenesis for variant-effect prediction, DeepLIFT/Grad attribution, and TF-MoDISco motif discovery from attribution scores. Use when predicting variant effects on TF binding, discovering soft motif syntax / cooperativity, integrating ChIP-seq with sequence-only predictions, or applying precomputed JASPAR Deep Learning models to new variants.
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。
Detects allele-specific transcription factor or histone modification binding from heterozygous-variant ChIP-seq using WASP (reference-bias filter; mandatory upstream), RASQUAL (joint QTL + bias-corrected testing), BaalChIP (Bayesian beta-binomial with copy-number-aware overdispersion), and AlleleSeq (personalized diploid genome). Handles imprinted-locus awareness, X-inactivation artifacts, cancer copy-number imbalance, and integration with downstream caQTL / bQTL mapping. Use when identifying variants with allelic effects on TF binding, fine-mapping causal regulatory variants, validating deep-learning variant predictions, or characterizing cis-acting regulatory effects.
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。
Convert raw Nanopore signal data (FAST5/POD5) to nucleotide sequences using Dorado basecaller. Covers model selection, GPU acceleration, modified base detection, and quality filtering. Use when processing raw Nanopore data before alignment. Note: Guppy is deprecated; use Dorado for all new analyses.
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。
Use when adding or modifying generator scripts that write files; keeps generated artifacts out of docs/ and aligns script defaults with ownership paths plus CI gate updates.
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。
Use when implementing or modifying behavior that must stay consistent across TUI and Web (mode/input/tool/replay/order). Require canonical semantics first, then TUI/Web adapters, then renderer-specific UI.
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。
Use when adding/modifying Formax slash commands (/permissions /agents /hooks /todos etc), especially output rendering (command sublines), overlay dismiss messages, and whether outputs are injected into the next LLM turn.
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。
Build Rust TUI apps with SuperLightTUI v0.23 (immediate-mode terminal UI). Use this skill when the user asks to create, modify, or debug terminal UI code in this repo, or asks "how do I X in SLT / TUI / terminal", or types Korean triggers like "터미널 UI", "TUI 만들어줘", "SLT로", "ratatui 대신". Read REFERENCES.md for feature flags and doc pointers; grep `src/context/` and `src/widgets/` before inventing any API.
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。
Build Rust TUI apps with SuperLightTUI v0.20 (immediate-mode terminal UI). Use this skill when the user asks to create, modify, or debug terminal UI code in this repo, or asks "how do I X in SLT / TUI / terminal", or types Korean triggers like "터미널 UI", "TUI 만들어줘", "SLT로", "ratatui 대신". Read REFERENCES.md for feature flags and doc pointers; grep `src/context/` and `src/widgets/` before inventing any API.
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。
Use trace-mcp tools for code navigation, impact analysis, and framework-aware queries instead of Read/Grep/Glob/Bash. Activate whenever the agent needs to explore, understand, or modify a codebase that has trace-mcp indexed.
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。
Apply the changes from all open Renovate bot pull requests of a GitHub repository into the local working tree. Use this skill when asked to apply, merge locally, consolidate, batch, or try out Renovate/dependabot-style dependency update PRs so the combined upgrade can be built and tested in one go. This skill only modifies local files; it never commits, pushes, merges, or closes anything on GitHub.
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。
Review Java code for bugs, duplicate code, correctness risks, maintainability improvements, and missing tests. By default review files modified in git; when the user explicitly names files, classes, packages, or a diff, review that scope instead. Generate a detailed review.md report with actionable comments and fixes.
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。
Create or modify a konsistent.json configuration file that enforces structural conventions in a TypeScript codebase. Use when the user wants to enforce consistent code structure, validate exports/imports across files, ensure directories contain required files, enforce naming patterns, add/remove/update convention rules, configure case map overrides for acronyms or special casing (kebabToPascalMap, kebabToCamelMap), or troubleshoot why konsistent is reporting violations. Triggers on: "konsistent", "konsistent.json", "enforce conventions", "structural consistency", "consistent exports", "consistent structure", "code conventions config", "add convention", "update convention", "fix konsistent errors", "case map", "case override", "acronym casing".
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。
Run the konsistent CLI, review all reported violations, and fix them in the codebase. Use when the user wants to "fix konsistent violations", "resolve konsistent errors", "clean up konsistent diagnostics", "make konsistent pass", "address structural convention violations", or asks to run konsistent and act on the results. The skill is interactive: it requires explicit user decisions when many violations stem from the same rule (rule vs. code question), explicit user confirmation before modifying any code, and a triage step for non-trivial violations. For editing `konsistent.json`, this skill defers to the `konsistent-config` skill.
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。
Writing documents for agents. Use when creating or editing skills, or modifying AGENTS.md or CLAUDE.md.
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。
Run and debug Python scripts in the project. Use when the user says "run python", "execute this script", "debug this py file", or wants to run/modify a .py file. Handles dependency checks, linting, execution, and error analysis.
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。
Reviews and adjusts PRD plans based on research findings. Use after completing research to evaluate story modifications. Triggers on: review plan, adjust stories, update prd based on 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。
Create 2D art and animation in Blender using Grease Pencil and Python. Use when the user wants to draw strokes programmatically, create 2D animations, build Grease Pencil objects from code, manage GP layers and frames, apply GP modifiers, set up drawing guides, or script any Grease Pencil workflow in Blender.
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。
Write and run Blender Python scripts for 3D automation and procedural modeling. Use when the user wants to automate Blender tasks, create 3D models from code, run headless scripts, manipulate scenes, batch process .blend files, build geometry with bmesh, apply modifiers, generate procedural shapes, or import/export 3D models using the bpy API.
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。
Alpha Vantage — Financial Market Data workflow skill. Use this skill when the user needs Access 20+ years of global financial data: equities, options, forex, crypto, commodities, economic indicators, and 50+ technical indicators and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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。
Alpha Vantage — Financial Market Data workflow skill. Use this skill when the user needs Access 20+ years of global financial data: equities, options, forex, crypto, commodities, economic indicators, and 50+ technical indicators and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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。
REQUIRED for all skill development. Automatically version control every skill file modification for rollback/comparison. Use after init_skill.sh, after every str_replace/create_file, and before packaging.
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。