「va」の検索結果

Performs set operations on genomic intervals - intersect (-wa/-wb/-wo/-wao/-loj/-c/-v/-u), subtract (-A), merge (-d, -c/-o), complement, cluster, multiinter, unionbedg, map, and groupby - with bedtools (CLI) and pybedtools/pyranges/bioframe (Python). Covers the sorted-input contract and the -sorted chromosome-order footgun, reciprocal/fractional overlap (-f/-F/-r/-e) and the A-vs-B asymmetry, -split for spliced/BED12/BAM features, and jaccard/fisher as mechanics only. Use when finding overlapping or unique regions between BED/peak/feature files, building consensus peaksets, removing blacklisted regions, transferring annotation values onto intervals, or computing interval-set similarity; route overlap-significance testing to overlap-significance.

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。

Analyzes species-environment relationships with constrained ordination (CCA, RDA, db-RDA), variance partitioning, indicator species (indicspecies IndVal.g group-equalized), PERMANOVA paired MANDATORILY with PERMDISP (Anderson & Walsh 2013; dispersion confounds centroid tests), Joint Species Distribution Models (HMSC, sjSDM, gjam) with explicit rejection of "residual covariance equals biotic interaction", phylogenetic community ecology (SES_MPD/MNTD), trait-environment via RLQ + fourth-corner with corrected modeltype=6 (Dray 2014), bipartite network metrics (NODF, modularity) with curveball null (Strona 2014), and Mantel-test replacements (dbRDA, GDM) for spatial data. Use when testing how environmental gradients structure communities, identifying habitat indicator taxa, partitioning variance among predictors, deciding whether PERMANOVA significance is location vs dispersion, picking among HMSC/sjSDM/gjam, or replacing Mantel tests for landscape data.

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。

Analyzes base-editing screens for variant function. Covers library design (Sanson 2020 GRACE, Hanna 2021 BRCA1/2 SNV scanning, Cuella-Martin 2021), CBE vs ABE chemistry choice (BE3/BE4 vs ABE7.10/ABE8.20/ABE8e), editing-window math (positions 4-8 from PAM-distal end, wider for ABE8e), bystander-edit quantification and the variant-call ambiguity it creates, sgRNA-efficiency filtering before hit calling, indel byproduct interpretation, the substitution-vs-indel diagnostic, variant annotation against ClinVar / COSMIC, and the Broad be-validation-pipeline. Use when designing a BE variant screen, choosing CBE vs ABE for a specific edit, interpreting bystander-confounded hits, distinguishing functional signal from indel artifact, integrating CRISPResso2 output with screen scoring, or deciding BE vs PE for SNV installation.

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。

Designs and audits data retention and file compaction for an embedded SQLite datastore in a long-lived service — a unified retention registry (one declared policy per table), TTL / absolute-age / row-count-cap policies, a fail-loud coverage guard, WAL/checkpoint tuning, and auto_vacuum/incremental-vacuum so pruning actually shrinks the file. Use when a SQLite DB grows unbounded, a table has an index but no DELETE, a pruned DB never shrinks on disk, metrics are stored as raw events forever, or a new table needs bounding. Keywords retention policy, TTL, reaper, prune, VACUUM, auto_vacuum, incremental_vacuum, WAL checkpoint, freelist, DB bloat, row cap, per-tenant quota. NOT for Postgres/MySQL/server-DB partitioning or TTL, log-line rate-limiting/sampling, backup/replication, schema migrations, or query-performance indexing.

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。

Redis caching patterns, cache-aside, write-through, TTL strategies, and invalidation. Activate on: caching, Redis, cache invalidation, cache-aside, write-through, TTL, CDN cache, stale-while-revalidate. NOT for: CDN/reverse proxy setup (use api-gateway-reverse-proxy-expert), database query optimization (use data-warehouse-optimizer).

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。

Plan, evaluate, and adopt agent infrastructure for a named workload. Use for agent runtime/framework selection, agent evaluation or observability plans, memory and tool boundaries, infrastructure cost controls, and organizational adoption. Covers workload-first comparison, risk and recovery planning, held-out evaluation, cost accounting, and staged rollout. Not a vendor ranking or a production certification. NOT for implementing a specific agent behavior (agentic-patterns), building a specific agent (ai-engineer), prompt optimization (prompt-engineer), or conventional ETL workflow design (jury_rig-architect).

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。

Connect a customer agent to Coval and complete a small first evaluation with inspected voice or chat results. Use when no usable evaluation exists yet; reuse existing recordings and resources when available.

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。

Choose the next useful step for evaluating a voice or chat agent with Coval. Use for getting started or deciding what to do next; use a specific skill directly when the task is already clear.

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。

Execute a bounded Coval evaluation on explicit test cases, then audit conversation, recording and metric evidence. Use when the user requests a run; does not automatically rerun, tune an agent or schedule evaluations.

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。

Locate CCSCustomHudLayout_SetDialogVariableString in CS2 server.dll / libserver.so via IDA Pro MCP and emit a fresh, minimal-unique signature or offset for the local gamedata entry "CCSCustomHudLayout_SetDialogVariableString" (symbol CCSCustomHudLayout_SetDialogVariableString). The gamedata consumer stores a BYTE SIGNATURE for this entry - ALWAYS emit the func schema (func_sig), never vfunc fields. The function may be virtual; locating it via the owning class vtable (RTTI) is a fine strategy, but once found, read its head bytes and emit func_sig. Locate via cross-references, distinctive constants/strings, callers of related symbols, or vtable slots; verify by decompilation before committing. Trigger: CCSCustomHudLayout_SetDialogVariableString, CCSCustomHudLayout_SetDialogVariableString

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。

Locate CCSCustomHudLayout_SetDialogVariableStringForPlayer in CS2 server.dll / libserver.so via IDA Pro MCP and emit a fresh, minimal-unique signature or offset for the local gamedata entry "CCSCustomHudLayout_SetDialogVariableStringForPlayer" (symbol CCSCustomHudLayout_SetDialogVariableStringForPlayer). The gamedata consumer stores a BYTE SIGNATURE for this entry - ALWAYS emit the func schema (func_sig), never vfunc fields. The function may be virtual; locating it via the owning class vtable (RTTI) is a fine strategy, but once found, read its head bytes and emit func_sig. Locate via cross-references, distinctive constants/strings, callers of related symbols, or vtable slots; verify by decompilation before committing. Trigger: CCSCustomHudLayout_SetDialogVariableStringForPlayer, CCSCustomHudLayout_SetDialogVariableStringForPlayer

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。

Collect and submit developer feedback to the LiveAvatar team. Use when: (1) A LiveAvatar integration is done, working, finished, or complete, (2) The user says "it works", "we're done", "that's it", "ship it", (3) The user is frustrated, stuck, or giving up — "this is broken", "this doesn't work", "I'm stuck", "I give up", "this is frustrating", "I want to report an issue", (4) The user explicitly asks to give feedback, report a bug, file an issue, or share a suggestion about LiveAvatar, (5) You've seen 3+ failed attempts at the same integration step, (6) You just finished writing or generating LiveAvatar integration code for the user.

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。

Guide for when to use and when not to use return validators in Convex functions. Use this skill whenever the user is writing Convex queries, mutations, or actions and needs guidance on return value validation. Also trigger when the user asks about Convex type safety, runtime validation, AI-generated Convex code, Convex AI rules, Convex security best practices, or when they're debugging return type issues in Convex functions. Trigger this skill when users mention "validators", "returns", "return type", or "exact types" in the context of Convex development. Also trigger when writing or reviewing Convex AI rules or prompts that instruct LLMs how to write Convex code.

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。

.NET advanced testing skills overview and navigation center. Triggered when users ask about "integration testing", "API testing", "containerized testing", "microservice testing", "test framework migration", "Testcontainers", "Aspire testing", and other advanced testing needs. Recommends appropriate sub-skills based on specific requirements, covering integration testing, Testcontainers, Aspire testing, framework upgrades, and other 8 advanced skills. Keywords: integration testing, integration testing, API testing, advanced testing, advanced testing, testcontainers, aspire testing, WebApplicationFactory, TestServer, database test, database testing, EF Core test, MongoDB test, Redis test, Docker test, container testing, microservice test, microservice testing, .NET Aspire, xUnit upgrade, TUnit, framework migration

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。

Deobfuscates malicious JavaScript code used in web-based attacks, phishing pages, and dropper scripts by reversing encoding layers, eval chains, string manipulation, and control flow obfuscation to reveal the original malicious logic. Activates for requests involving JavaScript malware analysis, script deobfuscation, web skimmer analysis, or obfuscated dropper investigation.

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。

Viteを使う開発で、設定ファイル、環境変数、APIへの接続、プラグイン、ビルド出力を整え、開発サーバーや依存関係の不具合調査を支援するスキル。

• 環境変数と開発用API転送の設定
• 開発サーバーの遅さや更新不良の調査
• 本番用ファイルの最適化と動作確認

開発前に製品の狙いを検証し、企画の実行判断、利用開始時のつまずき、機能の優先順位を整理します。根拠と次の行動を含む企画書やロードマップにまとめます。

• 曖昧なアイデアを企画書にしたいとき
• 開発する機能の優先順位付け
• 公開前の導入体験の点検

NestJSのAPIやサービスを設計・レビューするスキル。機能別のコード構成、入力検証、認証、例外処理、環境設定、テストを整理し、実運用に向けた実装を支援します。

• NestJSのAPI構成を決めたいとき
• 入力検証とアクセス制御の実装
• 例外処理と環境設定のレビュー

サブエージェントの作業に必要なコードや背景情報を、検索と評価を繰り返して絞り込むスキル。見つかった用語や不足情報を次の検索に反映し、関連するファイルを集めます。

• 修正担当に必要なファイルを探したいとき
• 機能追加前に既存実装を集めたいとき
• プロジェクトの用語に合わせた検索

予約済みのItô GPU環境で機械学習の訓練が利用可能かを確認するスキル。現状は訓練を実行せず、未提供の機能と将来の引き渡し条件を整理します。

• GPU予約後に訓練可否を確認したいとき
• 追加学習の引き渡し要件の確認
• 予約・予算・確認手続きの整理

AIエージェントが使うツールの種類や入出力、エラーからの復帰手順を設計・見直します。文脈の情報量も整理し、作業完了率や再試行回数で改善を評価します。

• エージェントのツールや入力形式の設計
• ツールの結果と次の行動を明確にしたいとき
• 安全な再試行と停止条件を定めたいとき

Twilioの電話番号を取得・保存し、SMS・MMSの送信や受信確認、音声通知、AIによる発信を扱います。同じ番号を次のセッションでも使えるスキル。

• エージェント用番号の取得と再利用
• 作業完了をSMSで通知
• 前回以降に届いたSMSの確認

画像の内容を質問したり、説明文を作ったりするために、LLaVAの導入と利用を支援します。画像を見ながら続ける対話や、画像付き文書の理解にも使えるスキルです。

• 写真の説明文を作りたいとき
• 画像内の物や場面について質問したいとき
• 画像について話せるチャットの試作