「health」の検索結果

Serve and visually inspect a converted LeRobot dataset in the browser. Use for videos and state/action timelines; do not use for raw workflow HDF5 or incomplete conversion output.

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。

Grade or filter workflow HDF5 episodes with an OpenAI-compatible vision model. Use for visual success labels; do not use for replay, policy evaluation, or recordings without frames.

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。

Record demonstrations through a workflow's teleop Task into workflow HDF5. Use for keyboard, leader, VR, or bus input; do not use for policy evaluation or autonomous rule-based Tasks.

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 a minimal blank Workflow scaffold with a Scene containing ground and light plus an idle run mode. Use for fast new Workflow scaffolding.

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。

Fine-tune a manifest-backed GR00T or openpi remote Task on compatible LeRobot data. Use for training; do not use for inference-only Tasks or checkpoint rollout.

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。

Run the maintained workflow data-to-policy pipeline from recording through checkpoint validation. Use for full end-to-end requests; do not use for one individual stage.

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 workflow HDF5 recordings to LeRobot datasets for training or browser inspection. Use for conversion; do not use for replay, augmentation, or raw-data repair.

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。

Replay a workflow HDF5 episode through its original Scene. Use for visual trajectory and recording verification; do not use for policy evaluation or LeRobot data.

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。

Expand workflow HDF5 demonstrations with action jitter, optionally scoped to node segments. Use for synthetic variants; do not use to collect data, alter state directly, or generate new images.

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 dealing with the DOCA environment around their workload — verifying an install is healthy, preparing the build env (pkg-config, headers, LD_LIBRARY_PATH, hugepages, devlink, representors), debugging env-class failures, deciding container-vs-bare-metal deployment shape, or reaching a DOCA install from a host that doesn't have one yet via the NGC DOCA container Stage-1 fallback. Trigger even when the user does not explicitly mention "DOCA setup" — typical implicit phrasings include "just got a BlueField, what now", "code is built, how to run it", "pkg-config can't find doca-flow", "no free 2048 kB hugepages", "representor X not found", "on a Mac and want to learn DOCA". Refuse and route elsewhere for library API specifics (Flow pipes, RDMA queues), the modify-a-sample first-app workflow or DOCA_ERROR_* program-side debugging, and "where is X documented" knowledge-map questions — those belong to other skills.

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。

Running a DOCA-linked binary directly on the hardware, with no container or kubelet: on host x86 with a BlueField NIC over PCIe, or bare-metal on BlueField Arm. Use for launching and supervising it (direct, tmux, systemd), and when it "exits 1 right after launch", "systemd keeps restarting it", reports "no matching device on the BF", or the BlueField itself is unhealthy: "bfb-install exited 0 but DPU is dead", "ping 192.168.100.2 works but ssh fails", "host PFs aren't showing netdevs". Service containers under kubelet belong to doca-container-deployment. Covers PCI/NUMA/CPU/IRQ binding, co-tenant isolation (cgroup-v2/netns/numactl), a seven-layer error taxonomy, platform lifecycle (BFB install, RShim/TMFIFO, host PF rebind, post-BFB recovery) and a six-state BlueField lifecycle classifier. Destructive firmware burn / mlxconfig set requires explicit confirmation via doca-hardware-safety; library APIs, env prep, and build use other skills.

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。

Stage 1 of Clinical ASR Flywheel. Use when bootstrapping a cycle: NVCF+MW disclosure, NVIDIA_API_KEY check, deps install, TTS+ASR smoke test.

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。

NOTE: your protein sequence and the retrieved MSA alignment are transmitted to external NVIDIA-hosted APIs (health.api.nvidia.com) on every call. Use local NIM containers for confidential or proprietary sequences. Run a complete protein structure prediction pipeline using NVIDIA BioNeMo NIMs: search for MSA alignments with MSA-Search (ColabFold), then predict the structure with OpenFold3 using the retrieved alignments. Use this skill whenever the user wants to predict a protein structure with maximum accuracy using MSA context, run the full AlphaFold3-style pipeline, generate MSA-informed structure predictions, or improve structure prediction accuracy by providing evolutionary information. Triggers on: MSA structure prediction pipeline, structure prediction pipeline, MSA-informed prediction, OpenFold3, ColabFold MSA, AlphaFold3 pipeline, protein structure, homology search, a3m alignment, UniRef30, NIM microservice. This pipeline chains MSA-Search and OpenFold3.

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。

Stage 4 of the Clinical ASR Flywheel. Use when priority KER is above 0.3 to run stock NeMo SFT on Parakeet TDT v2 and offline cycle N+1 re-eval. NOT for generic word boosting (use /finetune-asr).

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。

Stage 3 of Clinical ASR Flywheel. Score a NeMo manifest, produce the five-section KER leaderboard (by-ipa_source diagnostic). Not for ASR auth (/riva-asr).

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。

Recall past facts, preferences, and decisions from ReMe; inspect memory delivery, service health, and the consolidation schedule; or consolidate memory when requested.

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。

Interactive knowledge graph analysis. Routes natural language questions to graph scripts, interprets results in domain vocabulary, and suggests concrete actions. Triggers on "/graph", "/graph health", "/graph triangles", "find synthesis opportunities", "graph analysis".

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。

Research-backed evolution advice for your knowledge system. Analyzes health reports, friction patterns, and derivation history to propose specific changes with research justification. Never auto-implements — proposals require your approval.

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。

Surface the most valuable next action by combining task stack, queue state, inbox pressure, health, and goals. Recommends one specific action with rationale. Triggers on "/next", "what should I do", "what's next".

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。

Show vault statistics and knowledge graph metrics. Provides a shareable snapshot of vault health, growth, and progress. Triggers on "/stats", "vault stats", "show metrics", "how big is my vault".

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。

Schema validation for notes. Checks against domain-specific templates. Validates required fields, enum values, description quality, and link health. Non-blocking — warns but doesn't prevent capture. Triggers on "/validate", "/validate [note]", "check schema", "validate note", "validate all".

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。

Dependency injection in Golang using samber/do — service containers, lifecycle management, scopes, health checks, graceful shutdown, and module organization. Apply when using or adopting samber/do, when the codebase imports github.com/samber/do or github.com/samber/do/v2, or when refactoring manual constructor injection into a DI container.

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。

Analyze a startup from three perspectives — VC investor, job applicant, and CEO/founder — covering market, traction, unit economics, team, defensibility, runway, equity value, culture, and competitive position, with a verdict for each. Use this skill whenever the user wants to evaluate a startup or private tech company: due diligence, whether to invest in it or join it, weighing a startup job offer, company health, outlook, or competitive position, or open-ended asks such as "what do you think of [company]" or "research [company] for me".

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。

[omh] Tracking cost, tokens, latency, or service health: prepare an operations command-board for wrapper-safe token, cost, latency, run history, queue, failure-mode, external metric-provider, and service-quality evidence boundaries. Use when the user says: ops-observability-card, observability card, operations command board, ops command board, service quality board, service quality, external metric provider, metric provider.

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。