5,188 件 ・ 関連度順
概要と使いどころ
Search tech blogs, developer forums, and IT media (TechCrunch, Hacker News, 36氪, etc.) for software and hardware industry updates with heat ranking and EN↔CN translation. Use this skill only when the topic is clearly about programming, software, hardware, AI, or IT infrastructure.
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 a delivery team's communication system — channel routing, meeting-load reduction, status structure, escalation SLAs, timezone norms. Use when the calendar is full, updates go unread, or blockers surface late.
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。
Synthesize a week of inputs (calendar, tasks, journal, OKR check-ins) into a structured review with wins, learnings, blockers, and next-week priorities. Use for a Friday/Sunday weekly review, GTD review, or OKR check-in.
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。
Scan codebases for technical debt with AST parsing, prioritize by impact, and generate trend dashboards. Use when tracking tech debt, prioritizing refactoring, calculating cost-of- delay, planning sprint debt, or reporting debt to execs.
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。
DevOps for CI/CD, containers, Kubernetes, and Terraform. Use when building pipelines, containerizing apps, managing clusters, provisioning cloud infra, deploying with blue- green/canary, or handling infrastructure incidents.
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。
Audit PowerPoint (.pptx) decks for slide count, text density, embedded images and fonts, hidden slides, speaker notes, and animation density. Use when reviewing a board deck, sales deck, or conference talk before sending.
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。
Free tool marketing strategy covering idea evaluation, tool design, lead capture architecture, SEO landing pages, launch playbook, and ROI measurement for calculators, generators, checkers, graders, and interactive tools.
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。
Verify that Codex Slides changes are durably reflected in project state and visibly reflected in the exact Codex Browser slide, panel, version, checkpoint, or presentation surface. Use after creation, rendering, editing, restyling, mark edits, speaker-note changes, version restores, or export.
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。
Import cookies from your real browser (Comet, Chrome, Arc, Brave, Edge) into the headless browse session for testing authenticated pages. Interactive picker UI or direct domain import. macOS Keychain integration.
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。
Warns before destructive commands (rm -rf, DROP TABLE, force-push, git reset --hard, kubectl delete, docker force-rm). Session-scoped. Exceptions for common temp dirs (node_modules, .next, __pycache__).
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。
Optimizes Active Record queries, implements Turbo Frames and Streams, configures Action Cable WebSockets, sets up Sidekiq workers, and writes RSpec test suites for Rails 7+.
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。
Creates Dockerfiles, configures CI/CD pipelines, writes Kubernetes manifests, and generates Terraform/Pulumi infrastructure templates for deployment automation and GitOps.
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 sequential performance benchmarks with strict duration rules and baseline management
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。
Track and report on progress toward defined goals
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。
Diagnostic-only skill for Slurm scheduler and node-daemon issues on Amazon SageMaker HyperPod Slurm clusters. Scope mirrors the HyperPod troubleshooting guide. Invoke when the user reports a Slurm node stuck in down/drain, "Node unexpectedly rebooted" after auto-repair, slurmd not running, jobs stuck PENDING with REASON=Resources while sinfo shows idle nodes, jobs stuck COMPLETING after node replacement, GRES/GPU counts wrong, scontrol ping failing, slurmctld unresponsive, an Action:Reboot/Replace request that did not trigger HyperPod auto-recovery, or auto-resume not restarting a job. Also triggers on "drain before reboot", "diagnose a Slurm node", "investigate stuck jobs."
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。
Validates dataset formatting and quality for SageMaker model fine-tuning (SFT, DPO, or RLVR). Use when the user says "is my dataset okay", "evaluate my data", "check my training data", "I have my own data", or before starting any fine-tuning job. Detects file format, checks schema compliance against the selected model and technique, and reports whether the data is ready for training or evaluation.
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。
Diagnose and remediate per-node issues on a HyperPod cluster (EKS or Slurm) — a specific node is unhealthy, unresponsive, stuck, or needs replacing. Covers on-node EFA, GPU / accelerator hardware (XID, ECC, NVLink, row-remap, DCGM), Slurm node down/drained, disk and memory pressure, per-node lifecycle-script failures, SSM agent, container runtime, kernel panics, pod networking. Read-only. Not for cluster-wide provisioning (→ hyperpod-cluster-debugger), NCCL (→ hyperpod-nccl), or MFU (→ hyperpod-mfu-debugger).
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。
Remote command execution and file transfer on SageMaker HyperPod cluster nodes via AWS Systems Manager (SSM). This is the primary interface for accessing HyperPod nodes — direct SSH is not available. Use when any skill, workflow, or user request needs to execute commands on cluster nodes, upload files to nodes, read/download files from nodes, run diagnostics, install packages, or perform any operation requiring shell access to HyperPod instances. Other HyperPod skills depend on this skill for all node-level operations.
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。
Generates code that transforms datasets between ML schemas for model training or evaluation. Use when the user says "transform", "convert", "reformat", "change the format", or when a dataset's schema needs to change to match the target format — always use this skill for format changes rather than writing inline transformation code. Supports OpenAI chat, SageMaker SFT/DPO/RLVR/RLAIF, HuggingFace preference, Bedrock Nova, VERL, and custom JSONL formats from local files or S3.
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。
Selects a base model for the user's use case by querying SageMaker Hub. Use when the user asks which model to use, wants to select or change their base model, mentions a model name or family (e.g., "Llama", "Mistral", "Nova"), or wants to evaluate a base model — always activate even for known model names because the exact Hub model ID must be resolved. Queries available models, presents benchmarks and licenses, and confirms selection.
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。
Generates python code that evaluates SageMaker models. Supports two evaluation types: LLM-as-Judge and Custom Scorer. Use when the user says "evaluate my model", "run a benchmark", "test model performance", "how did my model perform", "compare models", or other similar requests.
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。
Generates code that fine-tunes a base model using SageMaker serverless training jobs. Use when the user says "start training", "fine-tune my model", "I'm ready to train", or when the plan reaches the finetuning step. Supports SFT, DPO, RLVR, and RLAIF trainers, including RLVR Lambda reward function and RLAIF custom prompt creation.
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 Amazon Machine Images (AMIs) with Packer using the amazon-ebs builder. Use when creating custom AMIs for EC2 instances.
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 Windows images with Packer using WinRM communicator and PowerShell provisioners. Use when creating Windows AMIs, Azure images, or VMware templates.
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。