650 件 ・ 関連度順
概要と使いどころ
Azure Weights & Biases SDK for .NET. ML experiment tracking and model management via Azure Marketplace. Use for creating W&B instances, managing SSO, marketplace integration, and ML observability.
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。
Azure Weights & Biases SDK for .NET. ML experiment tracking and model management via Azure Marketplace. Use for creating W&B instances, managing SSO, marketplace integration, and ML observability.
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。
Balances Hi-C contact matrices (ICE via cooler.balance_cooler, KR/SCALE/VC context), computes distance-decay expected with cooltools (expected_cis per-diagonal P(s), expected_trans scalar), builds observed/expected (O/E) matrices, and diagnoses polymer state from the P(s) log-derivative. Covers the within-matrix-vs-cross-sample distinction (balancing is NOT a normalizer), the equal-visibility assumption that CNV/aneuploidy violates (use raw counts for copy-number), cis-only balancing, mad_max/blacklist masking before balancing, multiplicative cooler weights vs divisive juicer weights, and the resolution-vs-depth budget. Use when balancing a .cool/.mcool, computing expected or P(s), making O/E matrices for compartments/loops, deciding ICE vs KR vs SCALE, choosing a resolution for a given depth, or troubleshooting NaN/all-NaN balanced matrices; route cross-sample comparison to hic-differential.
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。
Loads, converts, and manipulates Hi-C contact matrices in cooler format (.cool/.mcool/.scool) and Juicer .hic, using cooler (Python + CLI), hic2cool, and hictk. Covers the single-resolution mcool URI (file.mcool::/resolutions/<bp>), the load-bearing divisive-vs-multiplicative weight-naming rule (KR/VC/VC_SQRT auto-divisive vs cooler's multiplicative weight), what survives .hic<->.cool conversion (FRAG matrices and norm vectors do not), raw-vs-balanced coarsening, the .pairs upper-triangle/chromsize-order contract, and chrom-naming/bin-table provenance. Use when loading a cooler, converting .hic to .mcool, selecting a resolution, building a cooler from pairs or a matrix, coarsening/zoomifying, importing Juicer norm vectors, or debugging all-NaN balanced matrices and chr1-vs-1 empty fetches.
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 this skill when the user asks about state-specific trucking taxes BEYOND IFTA — NY Highway Use Tax (HUT), KY Kentucky Intrastate Tax (KIT), Oregon Weight-Mile Tax, New Mexico Weight-Distance Tax, Connecticut Highway Use Fee, or other state-level road-use taxes for commercial vehicles. Covers thresholds, rates, registration, filing cadence, and how these differ from IFTA. Reference state DOT/DOR resources.
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 this skill when the user asks how to design or improve a fleet safety scorecard — what KPIs to track, how to weight them, telematics signals vs document compliance vs roadside outcomes, monthly cadence, driver-facing vs admin-facing dashboards, gamification considerations, peer-comparison metrics, and how a scorecard ties to insurance premiums + retention. Reference SMS Methodology v3.20 for industry-standard weights.
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 this skill to understand how FMCSA calculates the Crash Indicator BASIC. Covers what crashes count, severity weighting, time weighting, and the preventability program.
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。
Azure Weights & Biases SDK for .NET. ML experiment tracking and model management via Azure Marketplace. Use for creating W&B instances, managing SSO, marketplace integration, and ML observability.
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。
絞り込んだ競合を九つの観点で採点し、根拠付きの比較カードと戦略の二軸図に整理するスキル。各社の強みや、自社が狙う領域の競合を確認します。
Itôで予約済みのGPUを使ったモデル配信の相談に対し、現状の未対応範囲と将来必要な確認事項を整理するスキル。現在は配信を開始せず、制約を案内します。
競合のブランド表現やサービス、実績などを共通の基準で採点し、根拠付きの比較カードと戦略上の二軸図にまとめ、自社が狙う位置の競合を把握するスキル。
Webページの表示や操作、APIの応答、ビルド・テストの所要時間を計測するスキル。変更前後の結果を比較し、Gitで共有する基準値をもとに性能の悪化を確認します。
W&B: log ML experiments, sweeps, model registry, dashboards.
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。
鍛えたい筋肉や器具に合う運動を探し、食品の栄養成分を調べるスキル。消費カロリーや体脂肪率の推定、減量・増量に向けた栄養配分にも使えます。
Produce a print-grade single-page kami (紙 / 纸) document — warm parchment canvas, ink-blue accent, serif at one weight, no italic, no cool grays. The output reads like a professional white paper or studio one-pager, not an app UI. Multilingual by design (EN · zh-CN · ja). One self-contained HTML file, zero dependencies.
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。
Retro Quarterly Review presentation template in a bold blue + orange editorial language. Use when users ask for a high-impact quarterly review / roadmap deck with heavyweight slab headlines, clean cream paper sections, structured grids, and fast premium motion pacing (3 slides, each hold under 3s in video mode).
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。
Advanced RuView capabilities — RuvSense multistatic sensing (attention-weighted fusion, geometric diversity, persistent field model), cross-viewpoint fusion across multiple nodes, RF tomography (ISTA L1 solver, voxel grids), longitudinal biomechanics drift, pre-movement intention signals, adversarial signal detection, and multistatic mesh security hardening. Use for research-grade or multi-node deployments.
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。
Coordinate supervised Orca workers: threaded messages, blocking ask/reply, task dispatch, worker_done/escalation waits, task DAGs, decision gates, coordinator loops, and decomposing work across agents. Use `orca-cli` for full ownership handoffs — "hand off", "handoff", "handover", "give this to another agent", "another worktree" — unless asked to supervise, monitor, or coordinate a DAG, and for terminal control, lightweight terminal prompts, shell commands, Orca worktree management, and reading or waiting on terminals.
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。
Extract business domain knowledge from a codebase and generate an interactive domain flow graph. Works standalone (lightweight scan) or derives from an existing /understand knowledge graph.
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 and manage routing combos with 19 strategies (priority, weighted, round-robin, Auto-combo, and more). Configure fallback chains, test routing outcomes, and retrieve combo metrics.
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。
SOTA semantic search — hybrid (sparse+dense), Graph RAG multi-hop, MMR diversity reranking, recency weighting
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。
ADR-152 — weighted similarity between two harness fingerprints (genome + score JSON). Returns overall score in [0,1] plus per-component breakdown (cosine over 9 numerics, categorical agreement over 4 enums, jaccard over agent_topology). Unblocks ADR-151 §3.2 Recommender, §3.3 Drift Detection, §3.5 Plugin Compat. Pure-TS, no `@metaharness/*` dep — preserves ADR-150's four architectural constraints.
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 auditing page weight for crawl efficiency, investigating why certain page content is not appearing in Google's index, or reviewing server-rendered pages that embed large JSON payloads in HTML.
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。
Health-check the user's Claude Code setup and fix issues: diagnose installation health — what the `claude doctor` terminal diagnostics cover — from local data (duplicate or leftover installs, PATH, unparseable settings files, broken or colliding agent definitions, skills whose frontmatter fails to parse); find unused skills, MCP servers, and plugins versus their context cost and disable dead weight; deduplicate local CLAUDE.md files against checked-in ones; trim checked-in CLAUDE.md files by cutting content a session could derive from the codebase (directory layouts, tech-stack lists, architecture overviews) while keeping gotchas, rationale, and non-standard conventions; migrate always-loaded CLAUDE.md guidance into lazy skills and nested CLAUDE.md files; flag slow hooks and context-heavy extensions; check the installed version is current; make auto mode the default permission mode; and pre-approve frequently denied read-only commands. Use when the user asks for a doctor run, checkup, audit, tune-up, or cleanup of their Claude Code setup or configuration.
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。