「ops」の検索結果

Maps GWAS-implicated loci to candidate effector (causal) genes by integrating variant-to-gene (V2G) features via Open Targets L2G (Mountjoy 2021), MAGMA gene-based association (de Leeuw 2015), FUMA SNP2GENE, cS2G combined SNP-to-gene scores (Gazal 2022), Polygenic Priority Scores (PoPS, Weeks 2023), FLAMES, INQUISIT, DEPICT, and enhancer-gene predictors (ABC, ENCODE-rE2G). Use when narrowing a GWAS lead locus to a candidate causal gene, picking between proximity, eQTL-based, and similarity-based prioritizers, integrating multi-evidence streams (fine-mapping, colocalization, ABC enhancer-gene, distance, chromatin), reconciling discordant L2G vs PoPS calls, prioritizing tissue-specific eQTL evidence, or triangulating across at least three independent lines of evidence for a publication-grade effector-gene nomination.

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。

Hugging Face Hub CLI (hf) — search, download, and upload models and datasets, manage repos, query datasets with SQL, deploy inference endpoints, manage Spaces and buckets.

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 Docker containers, images, volumes, networks, and Compose stacks — lifecycle ops, debugging, cleanup, and Dockerfile optimization.

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。

Space/aerospace mission monitoring: dark command center, amber telemetry on navy, monospace precision. Functional clarity above all. For ops dashboards, build/deploy status, and real-time monitoring.

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。

Read-only consolidated snapshot of an SDD Composy workspace: lifecycle stage, task contracts, trace integrity, loops, fleets, and the next action, without changing any file. Use for 'em que pé está o SDD', 'status do fluxo', 'qual a próxima ação', 'where are we in the workflow'. Do NOT use for git, CI, infrastructure, or other plugins' status, or to change state.

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 reusable, composable, and maintainable React/Vue/Angular components following established design patterns like compound components, render props, custom hooks, and HOCs. Use when creating component libraries, implementing component composition, building reusable UI elements, designing prop APIs, managing component state patterns, implementing controlled vs uncontrolled components, creating compound components, using render props or children as functions, building custom hooks, or establishing component architecture standards.

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。

When the user wants help with revenue operations, lead lifecycle management, or marketing-to-sales handoff processes. Also use when the user mentions 'RevOps,' 'revenue operations,' 'lead scoring,' 'lead routing,' 'MQL,' 'SQL,' 'pipeline stages,' 'deal desk,' 'CRM automation,' 'marketing-to-sales handoff,' 'data hygiene,' 'leads aren't getting to sales,' 'pipeline management,' 'lead qualification,' or 'when should marketing hand off to sales.' Use this for anything involving the systems and processes that connect marketing to revenue. For cold outreach emails, see cold-email. For email drip campaigns, see email-sequence. For pricing decisions, see pricing-strategy.

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。

Set up CI/CD pipelines for Adobe App Builder projects. Generates GitHub Actions workflows using adobe/aio-cli-setup-action@3 and adobe/aio-apps-action@3.3.0, plus patterns for Azure DevOps and GitLab CI. Handles OAuth S2S secrets injection, multi-workspace promotion (stage → prod), deploy gating with manifest validation. Use this skill whenever the user mentions CI/CD for App Builder, GitHub Actions for aio deploy, automated deployment pipelines, continuous integration, continuous delivery, deploy automation, multi-environment promotion, aio app add ci, or wants to automate their App Builder build and release process. Also trigger when users mention deploy workflows, release pipelines, or GitHub secrets for App Builder.

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。

Deploy with unified build/test/deploy intent across providers using .agent-loom/deploy.yml and per-provider adapters. Load when the user asks to deploy, ship to preview, release to production, or run a provider-agnostic deploy flow. Also triggers on "deploy anywhere", "deploy to Vercel", "deploy with GitHub Actions", "preview deploy", or "ship this". Runs preflight before any deploy — stops on missing secrets. Pairs with ci-cd-and-automation for pipeline design. Ships vercel and github-actions adapters; extensible adapter interface.

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。

Renders Hi-C contact matrices honestly and reproducibly with matplotlib, cooltools, HiCExplorer, pyGenomeTracks, FAN-C, CoolBox, and plotgardener. Covers the raw/ICE-balanced/observed-over-expected transform choice, LogNorm vs symmetric-diverging colormaps with vmax/percentile clipping, resolution-to-feature matching (compartments 100-500kb, TADs 10-40kb, loops 5-10kb), square vs rotated-triangle track-stacking, NaN/white-stripe handling, virtual 4C, APA/saddle/on-diagonal pileups, two-condition side-by-side and log2-ratio maps, and interactive (HiGlass) vs scripted-static publication figures. Use when plotting a contact matrix, choosing a normalization or color scale, building a multi-track Hi-C figure, making a virtual 4C profile, piling up loops/boundaries, or comparing two conditions.

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。

Compares Hi-C contact maps between conditions across the right scale -- differential bin-pair contacts (multiHiCcompare, diffHic), differential A/B compartments (dcHiC), differential TAD boundaries (delta insulation), and differential loops (diffloop, DiffHiChIP) -- with distance-stratified between-sample normalization, replicate-aware NB-GLM FDR, HiCRep SCC reproducibility gating, and CNV correction for cancer/aneuploid samples. Use when comparing Hi-C between treatment and control, finding differential contacts/compartments/boundaries/loops, normalizing two maps of unequal depth, choosing a replicate-aware test, gating replicates with SCC, or correcting copy-number artifacts before a tumor-vs-normal comparison.

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。

Maps GWAS-implicated loci to candidate effector (causal) genes by integrating variant-to-gene (V2G) features via Open Targets L2G (Mountjoy 2021), MAGMA gene-based association (de Leeuw 2015), FUMA SNP2GENE, cS2G combined SNP-to-gene scores (Gazal 2022), Polygenic Priority Scores (PoPS, Weeks 2023), FLAMES, INQUISIT, DEPICT, and enhancer-gene predictors (ABC, ENCODE-rE2G). Use when narrowing a GWAS lead locus to a candidate causal gene, picking between proximity, eQTL-based, and similarity-based prioritizers, integrating multi-evidence streams (fine-mapping, colocalization, ABC enhancer-gene, distance, chromatin), reconciling discordant L2G vs PoPS calls, prioritizing tissue-specific eQTL evidence, or triangulating across at least three independent lines of evidence for a publication-grade effector-gene nomination.

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。

Environment configuration manager for 12-factor config, feature flags, and multi-environment management. Activate on: environment variables, ConfigMap, feature flags, 12-factor config, dotenv management, config per environment, runtime config. NOT for: secret storage (use secret-management-expert), IaC provisioning (use terraform-module-builder), CI/CD variables (use github-actions-pipeline-builder).

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。

Safe database migration manager for zero-downtime DDL changes and rollback plans. Activate on: database migration, schema change, DDL, rollback plan, zero-downtime migration, Prisma migrate, Drizzle kit, Flyway, column rename, table alter. NOT for: query optimization (use database-optimizer), ORM modeling (use data-pipeline-engineer), backup/restore (use devops-automator).

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 daemon/background processes that start on boot, run continuously, and manage their own lifecycle. Covers macOS launchd (plist files, agents vs daemons), Linux systemd (unit files), Windows services, process supervision, logging, health checks, graceful shutdown, auto-restart, and AI-powered daemons that manage LLM API connections and rate limits. Activate on: "daemon", "background process", "launchd", "systemd", "service file", "plist", "launch agent", "launch daemon", "auto-start", "always running", "process supervisor", "pm2", "background service", "boot service", "AI daemon", "long-running process". NOT for: container orchestration (use devops-automator), cron jobs that run and exit (use task-scheduler), web server deployment (use backend-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。

Cloudflare Pages CI/CD with preview environments, edge functions, and Wrangler automation. Activate on: Cloudflare Pages, Wrangler deploy, preview environment, edge function, Pages project, Cloudflare Workers integration, custom domain on Pages. NOT for: Worker-specific development (use cloudflare-worker-dev), DNS management (use devops-automator), full-stack app frameworks (use vercel-deployment).

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。

AWS CDK infrastructure builder using TypeScript with L2/L3 constructs and Well-Architected patterns. Activate on: AWS CDK, CDK construct, CDK stack, CDK pipeline, AWS infrastructure as code TypeScript, L2 construct, CDK patterns. NOT for: Terraform IaC (use terraform-module-builder), Kubernetes manifests (use kubernetes-manifest-generator), serverless framework (use devops-automator).

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。

Blue-green and canary deployment orchestrator with traffic shifting and automated rollback. Activate on: blue-green deployment, canary release, rolling deployment, traffic shifting, rollback automation, progressive delivery, Argo Rollouts, Flagger. NOT for: K8s manifest generation (use kubernetes-manifest-generator), CI/CD pipeline setup (use github-actions-pipeline-builder), monitoring (use monitoring-stack-deployer).

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 RL simulation sandboxes, trajectory datasets, QLoRA/LoRA adaptation plans, eval harnesses, and rollback unhooks for agents learning specific coding behaviors beyond the base model. Use when building or reviewing tool-use training loops, offline trajectories, reward functions, self-improvement harnesses, or Port Daddy agent behavior curricula. NOT for generic ML tutorials, full model training operations, or production deployment without eval gates and safety unhooks.

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。

このマシンに溜まった Claude Code の履歴・痕跡・キャッシュを，何を消すか列挙して承認を得たうえで消し，ログイン・設定・導入済みプラグインは残す．「Claude Code の履歴を消して」「痕跡を掃除して」など，Claude Code がローカルに残したものの削除を明示的に求められたときだけ使う——「クリーンアップして」等の一般の整理の依頼では発動しない．実行中ジョブのログを見たいだけなら /ops:progress を使う．

実行中・実行済みのプログラムやジョブの状況を知りたいとき，自分が起動したものか無関係のものかを問わず調べて答える．「どうなってる？」「学習状況は？」「進捗見せて」など，実行そのものを頼んでいるわけではなく既存の何かの様子を尋ねられたときに使う．新しく実行を頼まれたときは /ops:exec を使う．

数十秒を超えて走るプログラム・スクリプト（学習・評価・データ処理・サーバ等）をログを残す形で起動し，異常の早期検知と完了報告まで担う．「実行して」「動かして」「学習を回して」など，明示の有無に関わらず自動適用する．数十秒で終わる実行（テスト・ビルド・lint 等）は対象外．既に動いているものの状況を知りたいだけなら /ops:progress，UI を見た目で確かめたいだけなら組み込みの run スキルを使う．

Runs Docker Desktop and its WSL2 layer, checking RAM and disk before container work. Trigger when Docker Desktop or a container won't start, vmmem eats RAM or the Docker disk keeps growing; to set a WSL memory cap in .wslconfig; to list or install WSL distros; to bring a compose stack up or down; to prune images and build cache; to run a command in a container; to log in to a registry; or say dockerrunner (audit, check, up, down, prune, status, refresh). Local LLMs (Docker Model Runner too) are lmstudiorunner's; images comfyrunner's; VMs and sandboxes hypervrunner's; Docker's MCP catalog its MCP Toolkit's; registry tokens keywarden's.

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。

Drives OBS Studio through its WebSocket, read-only by default, and turns recordings into clips, VOD cuts and chapters with FFmpeg. Trigger to audit an OBS setup; when a stream drops frames or lags; to switch a scene, mark a moment or save the replay buffer; to cut clips, burn in captions, normalize loudness or write chapters; when OBS shares a GPU; or say obsrunner (audit, check, set, mark, post, sheet, status, hold, release, refresh). Live work needs the user-installed obsws-python package. Never goes live, uploads or moderates chat. Caption files are whisperrunner's; thumbnails comfyrunner's; titles commscribe's; analytics duckrunner's.

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。