「features」の検索結果

NVIDIA App MCP: drivers, games, laptops, overlay. Check drivers, manage and optimize games, configure laptop features.

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Resiliency features in Megatron Bridge including fault tolerance, straggler detection, in-process restart, preemption, and re-run state machine.

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 contemplating a DOCA upgrade or downgrade — moving a host to a newer DOCA release, refreshing the BlueField BFB, bumping the NGC DOCA container tag, or rolling back. The discipline is detect → report → ASK → only-then guided upgrade: detect what is installed, discover what newer release exists, report the gap, then STOP and ask for explicit confirmation — never upgrade automatically. Trigger even without the word "upgrade": "is there a newer DOCA", "should I move to the next release", "I want the latest features", "my component is being deprecated, what now", or "roll me back". Route elsewhere for version detection (doca-version), first-time install (doca-setup), any hardware/firmware/reboot step (doca-hardware-safety), and public-docs / sunset routing (doca-public-knowledge-map).

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Use this skill for hands-on DOCA Arg Parser CLI work on a shipped sample or new DOCA-using app — adding / removing / renaming flags; wiring `doca_argp_init` → register params → `doca_argp_start` → `doca_argp_destroy` in order; picking a parameter type from the full public enum (`DOCA_ARGP_TYPE_STRING`, `_INT`, `_BOOLEAN`, `_DEVICE`, `_DEVICE_REP`, `_DOUBLE` — six values, not three); preserving the standard `--device` / `--representor` / `--json` (`-j`; real flag is `--json`, NOT `--json-config`) / `--sdk-log-level` surface; or debugging `DOCA_ERROR_BAD_STATE` / `INVALID_VALUE` / `NOT_SUPPORTED` / `IO_FAILED` from `doca_argp_*`. Trigger on implicit phrasings: "add a custom flag to a DOCA sample", "should I use getopt here", "BAD_STATE registering a new param", "my JSON config key is rejected", or "my sample's --json is ignored". Refuse and route elsewhere for variadic-flag / subcommand / shell-completion features, DOCA Core context, or DOCA Log internals.

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 wants to invoke the read-only doca_caps CLI to ask what DOCA sees on this host — listing DOCA devices and PCIe addresses, listing representor devices, asking which DOCA libraries are available on the current OS, checking per-device per-library capabilities, scoping output to a specific PCIe address, or capturing a side-effect-free capability snapshot for a debug session or install smoke-test. Trigger even when the user does not explicitly mention "doca_caps" or "capabilities print tool" — typical implicit phrasings include "what does DOCA actually see on this box", "is my BlueField PF visible to DOCA", "is Flow available on my RHEL host", "enumerate VF representors for pf0", "doca_caps: command not found", or "empty output for RDMA, is the tool broken". Refuse and route elsewhere for DOCA installation, library-internal capability matrices (Flow pipe creation, RDMA verbs features), streaming telemetry / DTS, or modifying the shipped binary — those belong to other skills.

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Finetune a pretrained KERMT encoder on a labeled CSV. Validate the checkpoint and data, prepare features, and run containerized training. Use a local checkpoint or optionally download a pinned Hugging Face model bundle using HF_TOKEN if configured. Write model bundles, prepared data, logs, and trained models to user-selected host directories.

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Run predictions with a finetuned KERMT checkpoint on a SMILES-only CSV. The skill validates that the input ckpt has task FFN heads (refuses pretrain ckpts with a redirect to kermt-finetune), validates the CSV, prepares the data (clean + rdkit_2d features), then launches main.py predict inside the kermt container (blocking, minutes-scale).

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Modernize Golang code to use recent language features, standard library improvements, and idiomatic patterns. Use when reviewing Go code with old-style patterns, when encountering a deprecation warning, or when the user asks for modernization, a Go version upgrade (e.g. to Go 1.27), or a CI/tooling refresh. Not for structural refactors, extracting functions, or moving code between packages (→ See `samber/cc-skills-golang@golang-refactoring` skill).

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Golang everyday observability — the always-on signals in production. Covers structured logging with slog, Prometheus metrics, OpenTelemetry distributed tracing, continuous profiling with pprof/Pyroscope, server-side RUM event tracking, alerting, and Grafana dashboards. Apply when instrumenting Go services for production monitoring, setting up metrics or alerting, adding OpenTelemetry tracing, correlating logs with traces, migrating legacy loggers (zap/logrus/zerolog) to slog, adding observability to new features, or implementing GDPR/CCPA-compliant tracking with Customer Data Platforms (CDP). Not for temporary deep-dive performance investigation (→ See `samber/cc-skills-golang@golang-benchmark` and `samber/cc-skills-golang@golang-performance` skills).

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Delegate coding tasks to Codex, Claude Code, or Pi agents via background process. Use when: (1) building/creating new features or apps, (2) reviewing PRs (spawn in temp dir), (3) refactoring large codebases, (4) iterative coding that needs file exploration. NOT for: simple one-liner fixes (just edit), reading code (use read tool), thread-bound ACP harness requests in chat (for example spawn/run Codex or Claude Code in a Discord thread; use sessions_spawn with runtime:"acp"), or any work in ~/clawd workspace (never spawn agents here). Claude Code: use --print --permission-mode bypassPermissions (no PTY). Codex/Pi/OpenCode: pty:true required.

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。

You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.

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 when completing tasks, implementing major features, or before merging to verify work meets requirements

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 users need to analyze competitors, monitor market movements, benchmark features/pricing, identify market gaps, or understand competitive positioning. Activates for "what are competitors doing," market analysis, or differentiation strategy.

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Expert SEO content strategy for SaaS and B2B sites. Use when doing keyword research, planning content clusters, creating pillar pages, optimizing on-page SEO, fixing technical SEO issues, building links, refreshing content, implementing programmatic SEO, mapping search intent, optimizing for SERP features, or measuring content performance. Use for SEO audits, content gap analysis, and organic growth strategy.

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Build real-time messaging applications using Azure Web PubSub SDKs for JavaScript (@azure/web-pubsub, @azure/web-pubsub-client). Use when implementing WebSocket-based real-time features, pub/sub messaging, group chat, or live notifications.

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Implement Conversational Language Understanding (CLU) using the azure-ai-language-conversations Python SDK. Use when working with ConversationAnalysisClient to analyze conversation intent and entities, building NLP features, or integrating language understanding into applications.

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Build real-time chat applications with Azure Communication Services Chat Java SDK. Use when implementing chat threads, messaging, participants, read receipts, typing notifications, or real-time chat features.

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Generate AI-powered podcast-style audio narratives using Azure OpenAI's GPT Realtime Mini model via WebSocket. Use when building text-to-speech features, audio narrative generation, podcast creation from content, or integrating with Azure OpenAI Realtime API for real audio output. Covers full-stack implementation from React frontend to Python FastAPI backend with WebSocket streaming.

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Supervised and unsupervised multi-omics integration with mixOmics. Includes sPLS for pairwise integration and DIABLO for multi-block discriminant analysis. Use when performing supervised multi-omics integration or identifying features that discriminate between groups.

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Score and prioritize neoantigens and epitopes for immunogenicity using multi-factor models combining MHC binding, processing, expression, and sequence features. Rank candidates for vaccine design. Use when prioritizing epitopes for vaccine development or identifying the most immunogenic neoantigens.

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Metabolite identification from m/z and retention time. Covers database matching, MS/MS spectral matching, and confidence level assignment. Use when assigning compound identities to detected features in untargeted metabolomics.

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Analyzes cfDNA fragment size distributions and fragmentomics features using FinaleToolkit or Griffin. Extracts nucleosome positioning patterns, fragment ratios, and DELFI-style fragmentation profiles for cancer detection. Use when leveraging fragment patterns for tumor detection or tissue-of-origin analysis.

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Evaluates market bubble risk through quantitative data-driven analysis using the revised Minsky/Kindleberger framework v2.1. Prioritizes objective metrics (Put/Call, VIX, margin debt, breadth, IPO data) over subjective impressions. Features strict qualitative adjustment criteria with confirmation bias prevention. Supports practical investment decisions with mandatory data collection and mechanical scoring. Use when user asks about bubble risk, valuation concerns, or profit-taking timing.

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Use when the user asks to "analyze the SERP" or "SERP分析"; maps SERP features, layout, ranking factors, search intent, AI Overviews, and snippet opportunities for a query. Not for keyword demand discovery — use keyword-research. SERP分析/搜索结果

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。