Results for “docker”

Execute ENCODE ATAC-seq processing pipeline from FASTQ to peaks and signal tracks. Child of pipeline-guide. Provides stage-by-stage Nextflow execution with Docker containers and cloud deployment. Handles Tn5 transposase offset correction, mitochondrial read removal, and nucleosome-free fragment selection. Use when users need to process ATAC-seq data following ENCODE standards. Trigger on: ATAC-seq pipeline, run ATAC-seq, process ATAC-seq, chromatin accessibility, open chromatin, Tn5 shift, TSS enrichment.

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Scaffold or migrate a repository onto release-please. Generates release-please-config.json, .release-please-manifest.json, and a release-please.yml workflow, plus an opt-in menu of tag-keyed deploy/publish templates (Docker/ECR matrix, frontend host deploy, npm OIDC publish, GitHub Release asset, Fly.io deploy, generic). Supports monorepo (mutual exclude-paths matrix), single-package, and mixed-language (node/simple/python) repos. Use when user runs /release-please-setup, mentions "release please", "set up release please", "automate releases", "conventional-commit releases", "monorepo release automation", or "migrate to release-please".

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Install Taskfile (task) and scaffold or audit Taskfile.yml configurations with ecosystem-aware templates. Auto-detects Node.js/pnpm, JVM/Gradle, Python/uv, Docker, and recommends single-file or multi-file (includes) patterns based on project complexity. Use when user runs /devtools:taskfile-setup, mentions "taskfile setup", "setup task runner", "audit taskfile", "scaffold taskfile", "add taskfile", "taskfile.yml", or "developer tasks".

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สร้าง Dockerfile CI/CD yaml และ monitoring config ที่ production-ready ไม่พัง

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Orchestrate AI coding agents (Claude Code, Codex, OpenCode) in isolated sandboxes using the @ai-hero/sandcastle SDK. Use when the user needs to (1) run agents AFK in Docker/Podman containers, (2) build multi-agent pipelines with plan-execute-review patterns, (3) run parallel agents on separate worktrees, (4) create iterative agent loops with maxIterations, (5) extract structured output from agent runs, (6) set up sandcastle in a new or existing project, or (7) write prompt files with template args and shell expressions.

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Set up and run local LLMs with Harbor. Use when the user wants to run models locally, install Harbor, start Open WebUI, llama.cpp, Ollama, vLLM, Docker Model Runner, MLX, or oMLX, pull GGUF or HuggingFace models, add SearXNG web search, Speaches TTS/STT, or Open Terminal code execution, launch Codex/Claude/Grok/OpenCode against a Harbor backend, or troubleshoot GPU, VRAM, and service startup.

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Detect project stack and generate GitHub Actions CI/CD workflows — PR checks with lint/test/build/security, deploy pipelines for Vercel/AWS/GCP/Fly/Docker, dependency caching, matrix testing, preview deploys, and Dependabot config

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Ship an app from code to production-ready infrastructure: containerize with Docker and multi-stage builds, set up GitHub Actions CI/CD with automated testing and deployment, add monitoring with health checks and alerting, then run preflight verification..

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High Availability control plane for Yuzu — the canonical entry point for any work on ADR-2002 (active-active server tier, gateway-fronted routing, Postgres-backed coordination seam, HA Postgres storage). Use when the user says "/ha", "/high-availability", "/ha-matrix", asks to plan or implement an HA workstream (WS-0…WS-14), asks "what's our HA gap" or "is it safe to run a second replica yet", asks to audit HA readiness, or starts work touching the coordination seam (leader election / fenced epochs / transactional outbox), gateway intra-cluster routing, shared `AgentRegistry` presence, PKI/CA HA, or the `deploy/docker/*ha*` / `scripts/ha/` surface. The skill bundles the two-axis model, the safe-to-scale gate, the delivery matrix (current-state + reviewers + status), the per-workstream workflow, and the load order for the routed reference docs.

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Work with Runpod GPU infrastructure and documentation. Use when Codex needs to create, deploy, manage, debug, or review Runpod Pods, Serverless workers/endpoints, templates, network volumes, Public Endpoints, Flash apps, runpodctl commands, Runpod Python SDK usage, REST/GraphQL API usage, Runpod MCP setup, Docker images for Runpod, endpoint request payloads, logs, billing/cost checks, or Runpod deployment troubleshooting.

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Deployment workflows, CI/CD pipeline patterns, Docker containerization, health checks, rollback strategies, and production readiness checklists for web applications.

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Design, configure, debug, and optimize OpenClaw AI agent deployments. Master guide for gateway configuration, openclaw.json settings, model routing and fallback chains, skills development and publishing, cron job scheduling, memory systems (Qdrant, Neo4j, SQLite), Docker infrastructure, and Tailscale VPN networking. Includes config analyzer that audits your openclaw.json and suggests improvements, plus health checker that validates all OpenClaw subsystems. Built for AI agents — Python stdlib only, no dependencies. Use for OpenClaw setup, gateway debugging, skill building, cron management, model optimization, cost reduction, and infrastructure troubleshooting.

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Deep knowledge about deploying applications to Fly.io (MicroVMs, Docker).

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Generate deployment configuration files (Dockerfile, vercel.json, wrangler.toml, etc.) based on target type.

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Deep knowledge about deploying applications to Railway (PaaS, Docker, Nixpacks).

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Auto-activate for pytest_databases, Docker DB fixtures, PostgreSQL/pgvector/ParadeDB, MySQL/MariaDB, Oracle/SQL Server, CockroachDB, MongoDB, Redis/Valkey, BigQuery/Spanner, MinIO, or RustFS. Not for mocked DBs.

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Auto-activate for Dockerfile, compose, Railway, Cloud Run, GKE, systemd, Kubernetes, Terraform, deploy scripts, or granian/litestar run at runtime. Not for packaging artifacts.

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环境安装与准备：把部署环境从零安装就绪——mindiesd 编译安装（本地昇腾直装 / SSH 推远端容器 / Docker 镜像直装）与三方推理框架全栈安装（vLLM-Omni 源码构建、DiffSynth-Engine 部署、 LightX2V editable 部署），并负责模型权重确认与下载（下载前先确认远端是否已存在）。不含特性使能与验证 （framework-integration）与 profiling（profiling-collect）；SSH 工具由 remote-access 提供。 当用户需要安装 MindIE-SD、源码构建/直装 vLLM-Omni 或 LightX2V（editable + PLATFORM=ascend_npu）、 或确认/下载模型权重时使用此技能； 即使用户只提到"把代码推到服务器""在容器里装 vllm 全栈""准备 lightx2v 调优环境"而未说昇腾， 只要上下文涉及环境安装与准备都应触发。由 model-auto-optimization S0 与 dev-workflow 部署阶段指引加载。

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Expert guide for Backend.AI distributed computing platform. Automatically activates when users ask about: - Backend.AI architecture, components (Manager, Agent, Storage Proxy, Webserver, App Proxy) - Features (session scheduling, Sokovan orchestrator, multi-tenancy, resource allocation) - APIs (REST, GraphQL), authentication, RBAC authorization - Container runtime (kernels, jail sandbox, hook library, virtual folders) - Accelerator support (CUDA, ROCm, TPU, NPU, Graphcore IPU) - Client SDKs (Python, Java, JavaScript, PHP) - Setup, requirements (Python 3.13+, Docker, PostgreSQL, Redis, etcd) - How WebUI connects to/interacts with Backend.AI backend - Plugin interfaces, development setup, infrastructure Use when user mentions "Backend.AI", "backend.ai", "Sokovan", component names, or asks about the backend platform this WebUI connects to. allowed-tools: WebFetch, Read

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Plan and implement infrastructure, containers, CI/CD, deployment safety, observability, and rollback changes. Use for Docker, IaC, cloud runtime, Kubernetes, pipeline, deployment, or operational reliability work. Does not read dotenv files or approve/release deployments.

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DraftForge test infrastructure for writing backend unit tests, Playwright E2E tests, and managing test data population. This skill should be used when writing new tests, adding test fixtures, creating populate functions for new features, adding Playwright E2E specs, using login/auth fixtures, creating test users/orgs/leagues/tournaments, or understanding the test data architecture. Covers backend Django tests via Docker, Playwright config (projects, workers, sharding), test auth endpoints that expose backend login to frontend, and feature-isolated test data patterns.

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Use when explicitly authorized lab traffic must be captured, searched, or exported from the project's mitmproxy-backed Docker lab recorder.

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End-to-end smoke against the local rembric dev server (`pnpm run dev`, host Next.js dev on :3000). Apply when the user says "smoke", "probar contra docker", "dev:up", or after applying an OpenSpec change that touches HTTP (`apps/web/src/app/api/`), MCP tools (`packages/mcp/src/`), or DB migrations (`packages/db/src/migrations/`). Encodes bring-up, readiness verification, probe pattern and teardown — not the probes themselves.

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Runnable end-to-end validation playbook for the Rembric TUI installer — catch breakage before merging/deploying, AND a sandboxed manual walkthrough to evaluate/iterate its UI/UX without polluting your real env. Apply before shipping any change touching `apps/plugin/install.sh`, the repo-root `install.sh` shim, any per-client `install.sh`/`uninstall.sh`, or distribution docs, or when you want to preview/try/demo the installer and polish how it looks and feels. Layered: CI-safe headless (vitest + `sh -n`), local install round-trips, operator interactive pty smoke, an optional full Docker `up` smoke, plus a sandboxed manual UX walkthrough. For the installer contract/what-not-to-break, see `rembric-tui-installer`.

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