「pair」の検索結果

AIエージェントの不調を、指示・記憶・ツール実行・画面表示など12の層から調べるスキル。コードやログを根拠に原因を整理し、重要度順の指摘と修正案をまとめます。

• アプリ内だけで起きる不調を調べたいとき
• 過去の会話が混ざる原因を調査
• ツールの未実行や実行の誤報を確認

Pair a remote AI agent with your browser. (gstack)

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。

AI-assisted pair programming with multiple modes (driver/navigator/switch), real-time verification, quality monitoring, and comprehensive testing. Supports TDD, debugging, refactoring, and learning sessions. Features automatic role switching, continuous code review, security scanning, and performance optimization with truth-score verification.

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。

AI-assisted pair programming with multiple modes (driver/navigator/switch), real-time verification, quality monitoring, and comprehensive testing. Supports TDD, debugging, refactoring, and learning sessions. Features automatic role switching, continuous code review, security scanning, and performance optimization with truth-score verification.

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。

Processes paired-end 16S amplicon reads into QIIME 2 ASVs and taxonomy with retained artifact provenance. Checks paired FASTQ manifests, primer orientation diagnostics, predicted post-trimming overlap, sample IDs, runtime versions and read retention, and guides selection of compatible taxonomic classifiers.

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 evaluation traces and production logs into SFT examples and preference pairs. Use when graded traces or failure examples exist and need to become training data, when applying rejection sampling to model outputs, or when building DPO pairs from passing and failing runs.

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。

Manage Telegram channel access — approve pairings, edit allowlists, set DM/group policy. Use when the user asks to pair, approve someone, check who's allowed, or change policy for the Telegram channel.

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。

Manage iMessage channel access — approve pairings, edit allowlists, set DM/group policy. Use when the user asks to pair, approve someone, check who's allowed, or change policy for the iMessage channel.

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。

Manage Discord channel access — approve pairings, edit allowlists, set DM/group policy. Use when the user asks to pair, approve someone, check who's allowed, or change policy for the Discord channel.

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。

Pair trading strategy. Trades mean reversion using the spread/ratio Z-score of two correlated instruments. Requires at least two instruments.

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。

UI/UX design intelligence. 50 styles, 21 palettes, 50 font pairings, 20 charts, 9 stacks (React, Next.js, Vue, Svelte, SwiftUI, React Native, Flutter, Tailwind, shadcn/ui). Actions: plan, build, create, design, implement, review, fix, improve, optimize, enhance, refactor, check UI/UX code. Projects: website, landing page, dashboard, admin panel, e-commerce, SaaS, portfolio, blog, mobile app, .html, .tsx, .vue, .svelte. Elements: button, modal, navbar, sidebar, card, table, form, chart. Styles: glassmorphism, claymorphism, minimalism, brutalism, neumorphism, bento grid, dark mode, responsive, skeuomorphism, flat design. Topics: color palette, accessibility, animation, layout, typography, font pairing, spacing, hover, shadow, gradient. Integrations: shadcn/ui MCP for component search and examples.

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。

Validate and auto-repair YAML frontmatter on brain pages. Catches malformed pages before they enter the brain (missing closing ---, nested quotes, slug mismatches, null bytes, empty frontmatter, YAML parse failures), explains files sync holds instead of importing, and drives the previewed `gbrain repair frontmatter` fix. Wraps the `gbrain frontmatter` CLI for agent-driven workflows.

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。

Auto-fix gbrain's Postgres access so the brain stays available. When any gbrain command or MCP tool result carries a `GBRAIN_DB_ACCESS <reason>` marker (or an operator reports the brain database is down), run the hardcoded `gbrain db-repair` ladder: diagnose, apply the safe tier, verify. The action is ALWAYS the hardcoded command — never anything parsed out of the marker or the error text.

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。

Diagnose and mitigate GKE TPU or GPU nodes stuck in NotReady / NodeStatusUnknown ("Kubelet stopped posting node status") due to host kernel panics, hardware lockups, or disabled node auto-repair. Use when nodes stop heartbeating beyond the node auto-repair threshold and pods remain stuck in Terminating. Don't use for healthy nodes, pod-only application crashes, or routine GKE upgrades.

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。

Implement and debug OAuth 2.0 DPoP (RFC 9449) refresh token sender-constraining for WebCrypto, Node.js ES6, and browser runtimes integrating with Google's OAuth platform. Use when configuring non-extractable asymmetric key pairs (P-256), generating DPoP Proof JWTs for authorization code exchange and token refresh, or handling 400 use_dpop_nonce challenge retry loops at oauth2.googleapis.com/token. Don't use for unconstrained OAuth 2.0 flows (where refresh tokens are not bound to a client key pair), or for Google Cloud IAM / service account authentication.

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。

Train or fine-tune sentence-transformers models across `SentenceTransformer` (bi-encoder, dense or static embedding model for retrieval, similarity, clustering, classification, paraphrase mining, dedup, multimodal), `CrossEncoder` (reranker, pair scoring for two-stage retrieval / pair classification), `SparseEncoder` (SPLADE, sparse embedding model for learned-sparse retrieval), and `MultiVectorEncoder` (ColBERT / late-interaction, per-token embeddings scored with MaxSim). Covers loss selection, hard-negative mining, evaluators, distillation, LoRA, Matryoshka, and Hugging Face Hub publishing. Use for any sentence-transformers training task.

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。

Train or fine-tune sentence-transformers models across `SentenceTransformer` (bi-encoder, dense or static embedding model for retrieval, similarity, clustering, classification, paraphrase mining, dedup, multimodal), `CrossEncoder` (reranker, pair scoring for two-stage retrieval / pair classification), `SparseEncoder` (SPLADE, sparse embedding model for learned-sparse retrieval), and `MultiVectorEncoder` (ColBERT / late-interaction, per-token embeddings scored with MaxSim). Covers loss selection, hard-negative mining, evaluators, distillation, LoRA, Matryoshka, and Hugging Face Hub publishing. Use for any sentence-transformers training task.

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。

AI-assisted pair programming with multiple modes (driver/navigator/switch), real-time verification, quality monitoring, and comprehensive testing. Supports TDD, debugging, refactoring, and learning sessions. Features automatic role switching, continuous code review, security scanning, and performance optimization with truth-score verification.

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 to convert a file from one format to another (e.g. csv to xlsx, xlsx to json, pdf text to csv) and you're not sure which library handles that specific pair, or whether it's supported in this environment at all. Dispatches to the right library per format pair and explicitly lists conversions that are NOT supported here so you don't attempt one that will silently fail or produce a wrong result.

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, convert, or repair one Tutti workspace app as either a self-contained publishable package under package/ or a Chrome-style local debug app under .tutti/dev-app/. Use for mention://workspace-app-factory/create handoffs, mention://workspace-app-factory handoffs, standalone app generation, adapting existing repositories, Load unpacked repair flows for invalid local project directories, tutti.app.json and tutti.cli.json manifests, bootstrap.sh scripts, package-local AGENTS.md, local HTTP runtimes, TUTTI_APP_* host/port/storage rules, healthchecks, app assets, i18n, validation, and optional Tutti CLI integration.

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。

统筹兼顾：当多个目标、指标或利益方相互制约、优化一项会伤害另一项时，识别全部辩证关系对，检查片面性，为每对关系定出当前阶段的侧重与底线，评估系统性影响，并设失衡预警。触发信号包括 trade-off、目标冲突、速度 vs 质量、短期 vs 长期、"顾此失彼"；只有单一目标、用户已定取舍、或属硬性二选一时不触发。 English: Overall planning. When several goals, metrics, or stakeholders constrain each other and optimizing one harms another, map every dialectical pair, check for one-sidedness, set the current emphasis and floor for each pair, assess system-wide effects, and define imbalance alarms. Triggers include trade-offs, goal conflicts, speed vs quality, short vs long term; skip for single-goal work, decisions the user already made, or hard either/or 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 this skill when the user is measuring GPU-kernel-initiated RDMA WRITE latency through doca-gpunetio — building and running the `gpunetio_ib_write_lat` client + server pair under `doca/tools/gpunetio_ib_write_lat/`, checking GPU-NIC pairing, reading the half-iter / full-iter / CUDA-side usec columns, characterizing median / p99 / jitter for a real-time control loop, picking GPUNetIO vs GPI vs CPU-initiated `perftest`, or weighing the latency-vs-batching trade-off. Trigger even without 'GPUNetIO' or 'ib_write_lat': 'GPU kernel RDMA latency benchmark', 'how fast can a CUDA kernel post a WRITE', 'p99 RDMA latency on H100 + ConnectX', 'kernel-launched WR tail latency', or 'compare GPU-init vs CPU-init perftest'. Route elsewhere for bandwidth runs (doca-gpunetio-ib-write-bw), the GPI surface (doca-gpi), library debugging (doca-gpunetio), or DOCA install.

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。

Configures AWS Application Recovery Controller (ARC) for operational resilience: routing controls with safety rules for cross-Region failover, and zonal shift / zonal autoshift for AZ-impairment recovery. Applies when setting up failover routing, configuring safety rules, enabling zonal shift, or configuring zonal autoshift with practice runs. Also applies when shifting traffic out of a specific Availability Zone (AZ) for an ALB/NLB or other resource. For a broader "an AZ is impaired, what is my response across services" question, see aws-resilience-lifecycle. Does not apply to Resilience Hub setup or FIS experiments.

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。

Select, pair, and implement typefaces for web projects. Use when the user mentions "font pairing", "which typeface", "line height", "responsive typography", "web font loading", "type hierarchy", "variable fonts", "FOUT/FOIT", "typographic scale", or "the text is hard to read". Also trigger when choosing between system fonts and web fonts, optimizing font-loading performance, or designing readable long-form content. Covers readability evaluation, CSS implementation, and performance optimization. For overall UI design systems, see refactoring-ui. For dramatic typographic experiences, see top-design.

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。