「across」の検索結果

Publish and schedule content to 15 social platforms through one Upload-Post API call: TikTok, Instagram, YouTube, LinkedIn, Facebook, X, Threads, Pinterest, Bluesky, Reddit, Discord, Telegram, Mastodon, WordPress, and Google Business Profile. Use when the user wants to post or schedule a video, photo carousel, text, or document across several platforms at once, check upload status, pull analytics, or run Instagram comment-to-DM funnels.

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Publish and schedule to TikTok, Instagram, YouTube, LinkedIn, X, Bluesky, Mastodon, Facebook, Threads, Reddit, Telegram and Discord through one PostWire API call — writing a separate, native version of the post for each network instead of sending identical text everywhere. Use when the user wants to publish or schedule one idea across several platforms, wants per-network captions, or wants to know why a post was rejected before it is sent.

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AI news tracking skill that monitors 80+ entities across 6 free sources (Reddit, HN, GitHub, HuggingFace, arXiv, X/Twitter). Generates scored daily reports with infographics and message digests. Invoke via /morning-ai.

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Automate Google Drive file operations (upload, download, search, share, organize) via Rube MCP (Composio). Upload/download files, manage folders, share with permissions, and search across drives programmatically.

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Access DeFi data from DexPaprika: token prices, liquidity pools, OHLCV, transactions across 34+ blockchains and 30M+ pools. Free, no API key needed. Install MCP: add https://mcp.dexpaprika.com/sse as SSE server, or install plugin: /plugin marketplace add coinpaprika/claude-marketplace

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Generates creative domain name ideas for your project and checks availability across multiple TLDs (.com, .io, .dev, .ai, etc.). Saves hours of brainstorming and manual checking.

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Intelligently organizes your files and folders across your computer by understanding context, finding duplicates, suggesting better structures, and automating cleanup tasks. Reduces cognitive load and keeps your digital workspace tidy without manual effort.

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Fetch a real product's current price, stock, rating, or images from retailer pages over the Apify MCP server, and return them as typed fields rather than prose. Use when someone asks what a product costs right now, whether it is in stock, how it is rated, to compare a product across retailers, or to check a product URL they pasted. Also use when an agent would otherwise answer a product question from training data. Not for managing your own store's catalog, not for payments, and not for questions answerable without live retailer data.

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Analytics your AI agent can actually use. Track, analyze, run A/B experiments, and optimize across all your projects via CLI. Includes a growth playbook so your agent knows HOW to grow, not just what to track.

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Schedule, publish and analyze social posts across 15 networks via BulkPublish (MCP or REST) — Facebook, Instagram, X, TikTok, YouTube, Threads, Bluesky, Pinterest, Google Business, LinkedIn, Mastodon, Discord, Telegram, Tumblr, Snapchat. Use when the user wants to post, schedule, or bulk-publish to social media, check connected channels, or read post analytics. Always check platform rules before composing.

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Audit whether a website can be found, crawled, and cited by AI answer engines such as ChatGPT Search, Perplexity, Google AI Overviews, and Microsoft Copilot. Use when someone asks why their brand is missing from AI answers, whether AI crawlers can read their site, how to get cited by ChatGPT or Perplexity, or asks for a GEO or AEO (generative / answer engine optimization) review. Produces a citation baseline across buyer-intent prompts, a crawler-access check, a citability review of named pages, and a ranked fix list. Not for keyword rank tracking, paid search, or pages behind a login.

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Compute federated statistics over tabular data (count, sum, mean, stddev, var, histogram, quantile, noise-protected min/max) and image data (count, failure_count, pixel-intensity histogram) across NVFLARE sites via FedStatsRecipe — automatic and non-interactive from the dataset, feature names (header or supplied), and optionally a README or notes declaring which statistics to compute; do not use for model training conversion, hierarchical statistics, deployment, POC/production lifecycle, or failed-job diagnosis.

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Use when the user wants to set up, scale, validate, or harden NVIDIA physical AI infrastructure for synthetic data generation workflows across local MicroK8s or Azure AKS, including Kubernetes clusters, inference endpoint deployment, OSMO deployment, workload submission readiness, and infrastructure failure recovery. Trigger keywords: physical ai infrastructure, resilient scaling, SDG infrastructure, microk8s, azure aks, NVCF deployment, NIM Operator, OSMO deploy, workflow scaling. Don't trigger for: OSMO log summarization or workload-only operations unless infrastructure setup, scaling, validation, or recovery is requested.

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Use this skill when migrating applications, examples, integrations, documentation, manifests, or repository code from NeMo Flow to NeMo Relay across Python, Rust, Node.js, Go, C FFI, CLI, configuration, and observability surfaces.

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Manage durable working-session memory for coding agents. Use when a user asks to preserve or recover agent context across disconnects, VS Code restarts, long-running work, handoffs, or any session where important state should be written periodically under the repo's session directory. Do NOT use for: simple questions, short tasks, one-off commands, linting, or code review.

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How to launch distributed Megatron-LM training jobs on a SLURM cluster. Covers a minimal sbatch skeleton, environment-variable setup for torch.distributed.run, CUDA_DEVICE_MAX_CONNECTIONS rules across hardware and parallelism modes, container conventions, monitoring, and per-rank failure diagnosis.

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Benchmark Jetson LLM/VLM serving performance across vLLM, llama.cpp, and Ollama with structured JSON output.

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Reclaim DRAM by disabling unused subsystems across MB1 BCT, MB2 BCT, kernel reserved-memory, and SWIOTLB. Use for headless or no-camera Jetson deployments; not for CPU/GPU frequency tuning.

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DOCA version handling: detect the installed release and validate the four-way match across pkg-config doca-common, applications/VERSION, doca_caps --version, and bfver/mlnx-release on BlueField; reason about NGC tags; look up whether a capability exists on this release; diagnose build-vs-runtime drift. Load it alongside doca-debug when a symptom smells of version skew: "built but nothing on the wire", "undefined reference to a symbol the docs claim exists", "DOCA_ERROR_NOT_SUPPORTED at runtime", "what does latest mean for this tag", or "is my LTS still supported". Once all four sources match, the component's build / run workflow belongs to its own library, service or tool skill. Refuse install / package choice (doca-setup), per-library API questions (matching library skill), DOCA_ERROR_* taxonomy (doca-programming-guide), and the general debug ladder (doca-debug).

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Hardware-offloaded erasure coding with DOCA Erasure Coding (Reed-Solomon, doca_ec) on a BlueField DPU, ConnectX NIC, or host: generating parity for data blocks, rebuilding lost blocks from the survivors, and refreshing parity after a change. Trigger on "one data block changed, how do I refresh parity without re-encoding", "a disk failed and 2 parity blocks are gone, can I rebuild", "RAID-6 resilience across 12 disks", "my doca_ec_task_create returns NOT_PERMITTED", or "is this N+K layout still recoverable", even without naming DOCA Erasure Coding. Covers bringing up a doca_ec context, the create / recover / update tasks, matrix type / N / K / block size, doca_ec_cap_* queries before sizing, doca_mmap src/dst permissions, and DOCA_ERROR_* from doca_ec_task_*. Refuse and route elsewhere for non-Reed-Solomon codes (fountain / LDPC / raptor), pure-replication designs, network FEC, or other DOCA accelerator libraries (SHA / Compress / AES-GCM / DMA).

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Use this skill whenever the user is doing hands-on DOCA programming on a BlueField DPU or ConnectX NIC and needs the foundation primitives every per-library context rests on — walking the doca_ctx lifecycle, discovering doca_dev / doca_devinfo and gating on doca_*_cap_* before trusting a feature, wiring doca_mmap / doca_buf_inventory / doca_buf for zero-copy I/O across libraries, driving doca_pe for completions, or DOCA Log's two-tier (--sdk-log-level vs app-side) model. Trigger even when the user does not say "DOCA Common" — typical implicit phrasings include "my tasks submit but nothing completes", "DOCA_ERROR_BAD_STATE from doca_ctx_start", "--sdk-log-level does nothing for my DOCA_LOG_DBG lines", "share a buf between doca_dma and doca_rdma", or "crashes far from the offending line". Refuse and route elsewhere for per-library questions in isolation (load doca-flow / doca-rdma / doca-eth alongside), installing DOCA (doca-setup), or doc lookup (doca-public-knowledge-map).

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Trace, complete, and interpret the Pareto frontier across competing objectives using repeated single-objective cuOpt solves (weighted-sum and ε-constraint).

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Find recurring concept combinations across the wiki that lack an explicit synthesis page, then create cross-cutting synthesis pages. Use for knowledge synthesis after the vault has accumulated enough material.

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Idiomatic context.Context usage in Golang — propagation through API boundaries, cancellation, timeouts and deadlines, request-scoped values, context.WithoutCancel for background work outliving requests. Apply when designing context propagation across layers, debugging leaked or unexpired contexts, choosing between context.Background/TODO/WithoutCancel, or storing values in context. Not for code that merely accepts ctx as first parameter.

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。