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概要と使いどころ
Customize resume for specific job postings while maintaining truthfulness. Use when the user wants to tailor, customize, or target their resume for a specific job, company, or role.
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。
Takes a pile of applications and turns it into a ranked shortlist, scored only against the job's stated rubric, with drafted replies to every candidate, scheduled interviews, and an onboarding checklist for whoever gets hired. Picks up where job-post-builder leaves off and carries the funnel through to first day, including the payroll setup handoff. Runs on Gmail and Google Calendar, deepens with Gusto, DocuSign, and Drive or M365, and works entirely from uploaded resumes with drafted replies the owner sends by hand. Use this whenever the owner has applicants to deal with — including phrasings like "I've got 40 resumes and no time," "who should I interview," "screen these applications," "help me narrow this down," "set up interviews for the top three," or "I need to tell the rest no." Reach for it when the owner mentions applicants, resumes, candidates, or being buried in a hiring inbox.
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。
Phase 1 of building a Claude Managed Agent — interview the founder about the one job the agent should do, then produce a build sheet (CMA primitives table + v1/v2 deferrals + eval plan) WITHOUT needing their API key yet. Use when the user says "help me scope an agent", "I have an idea for an agent", "what should this agent be", or when the orchestrator routes phase=interview. Drives the six intake slots (job, trigger, inputs, actions, definition-of-done, recurrence) via AskUserQuestion, maps them to primitives with interview_planner.py, assembles build-sheet.json with build_sheet_builder.py, and validates limits with primitives_validator.py. Connectors are mockable in v0 (schema-true custom tools); real MCP servers become v1 deferrals. Distinct from stage-launch (which turns the sheet into payloads).
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。
Comprehensive PM resume review and tailoring against 10 best practices including XYZ+S formula, keyword optimization, job-specific tailoring, and structure. Use when reviewing a PM resume, preparing for job applications, or improving resume impact.
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 GKE Cloud TPU multi-slice training hangs (Megascale `HANG_DETECTED` logs and hang monitored events) using the ML Diagnostics `Megascale XLA (MXLA) Hang Analyzer` (`gcloud alpha mldiagnostics monitored-events`) and 1-minute Cloud Monitoring multi-slice latency metrics (`kubernetes.io/container/multislice/*`). Distinguishes XLA compiler/HLO launch divergence and host data-input stalls from TPU chip, SparseCore, ICI, or network fabric faults. Use when multi-slice TPU training jobs freeze without progressing steps, emit `HANG_DETECTED`, or stall in collective operations. Don't use for gradual step-time throughput drops without hangs (use gke-ai-troubleshooting-tpu-performance-degradation) or pod preemption/eviction restarts (use gke-ai-troubleshooting-jobset-interruption).
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。
Provides data-retrieval best practices, tool selection guidance, and performant SQL query syntax for BigQuery telemetry across INFORMATION_SCHEMA, Cloud Monitoring, and the REST API. Use when the telemetry to fetch is already known, selecting telemetry tools, writing performant INFORMATION_SCHEMA queries, retrieving telemetry for diagnosing single-job performance bottlenecks, investigating slot contention, job concurrency and queue latency, analyzing reservation capacity, utilization and autoscaling saturation, or auditing capacity-based and on-demand compute and storage resource billable usage. Don't use for root-cause diagnosis or symptom troubleshooting when the cause is unknown (use bigquery-troubleshooting first), or for writing or optimizing business logic SQL (use bigquery-optimization).
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。
Manages Cloud Run services, jobs, and worker pools. Use when you need to deploy applications responding to HTTP requests (services), run event-triggered or scheduled tasks (jobs), or handle always-on pull-based background processing (worker pools).
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。
Configures best-practice, high-signal alerting policies for Cloud Run resources on Google Cloud (services, jobs, and worker pools) based on seasoned SRE practices. Use when analyzing, recommending, writing, or deploying Terraform PromQL alerting policies to monitor Cloud Run error rates (4xx/5xx), request latency, container instance saturation (warning/critical), container CPU/memory utilization and allocation, billable instance time, job execution status, and worker pool queue backlog. Don't use for GKE workloads (use gke-alert-configuration) or Compute Engine VMs.
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。
Manages GenAI tuning jobs in Agent Platform. Use this to list, get, or cancel ongoing model tuning jobs. Don't use for fine-tuning models (use `agent-platform-tuning`), deploying models to endpoints (use `agent-platform-deploy`), or managing serving endpoints (use `agent-platform-endpoint-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。
SSH job queue for multi-seed/multi-config ML experiments with OOM-aware retry, stale-screen cleanup, and wave-transition race prevention. Use when user says "batch experiments", "队列实验", "run grid", "multi-seed sweep", "auto-chain experiments", or when /run-experiment is insufficient for 10+ jobs that need orchestration.
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。
Operate FrontendExtension (FE) resources in frontend-forge: create, update, rebuild, inspect package artifacts, download packages, publish, unpublish, delete, and debug package/publish controller behavior. Use this skill whenever the user mentions FE operations, FrontendExtension lifecycle, extension package/download/publish/unpublish, artifact ConfigMaps, package Jobs, publisher Jobs, publish target ConfigMaps or Secrets, rebuild-token, package-state/publish-state labels, or troubleshooting FE status in a Kubernetes cluster.
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。
Automate Drip Jobs tasks via Rube MCP (Composio). Always search tools first for current schemas.
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。
Rails 7+ specialist that optimizes Active Record queries with includes/eager_load, implements Turbo Frames and Turbo Streams for partial page updates, configures Action Cable for WebSocket connections, sets up Sidekiq workers for background job processing, and writes comprehensive RSpec test suites. Use when building Rails 7+ web applications with Hotwire, real-time features, or background job processing. Invoke for Active Record optimization, Turbo Frames/Streams, Action Cable, Sidekiq, RSpec Rails.
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。
Trains and fine-tunes vision models for object detection (D-FINE, RT-DETR v2, DETR, YOLOS), image classification (timm models — MobileNetV3, MobileViT, ResNet, ViT/DINOv3 — plus any Transformers classifier), and SAM/SAM2 segmentation using Hugging Face Transformers on Hugging Face Jobs cloud GPUs. Covers COCO-format dataset preparation, Albumentations augmentation, mAP/mAR evaluation, accuracy metrics, SAM segmentation with bbox/point prompts, DiceCE loss, hardware selection, cost estimation, Trackio monitoring, and Hub persistence. Use when users mention training object detection, image classification, SAM, SAM2, segmentation, image matting, DETR, D-FINE, RT-DETR, ViT, timm, MobileNet, ResNet, bounding box models, or fine-tuning vision models on Hugging Face 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。
Hugging Face Hub CLI (`hf`) for downloading, uploading, and managing models, datasets, spaces, buckets, repos, papers, jobs, and more on the Hugging Face Hub. Use when: handling authentication; managing local cache; managing Hugging Face Buckets; running or scheduling jobs on Hugging Face infrastructure; managing Hugging Face repos; discussions and pull requests; browsing models, datasets and spaces; reading, searching, or browsing academic papers; managing collections; querying datasets; configuring spaces; setting up webhooks; or deploying and managing HF Inference Endpoints. Make sure to use this skill whenever the user mentions 'hf', 'huggingface', 'Hugging Face', 'huggingface-cli', or 'hugging face cli', or wants to do anything related to the Hugging Face ecosystem and to AI and ML in general. Also use for cloud storage needs like training checkpoints, data pipelines, or agent traces. Use even if the user doesn't explicitly ask for a CLI command. Replaces the deprecated `huggingface-cli`.
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。
Comprehensive persistence tradecraft for authorized red team engagements covering Windows and Linux mechanisms. Windows techniques include registry Run/RunOnce keys, scheduled tasks, WMI event subscriptions, DLL search order hijacking, COM object hijacking, Startup folder drops, service creation, Security Support Provider (SSP) DLL injection, and Active Directory persistence (AdminSDHolder abuse, DCShadow, Golden Ticket, Silver Ticket, Skeleton Key, SID History injection). Linux techniques include cron and at jobs, systemd timers and services, SSH authorized_keys injection, shell profile backdoors (.bashrc/.bash_profile), PAM module backdoors, LD_PRELOAD hijacking, kernel module rootkits, web shells, and Git hook abuse. Provides operator-ready command sequences for SharPersist, Impacket ticketer, schtasks, sc.exe, crontab, and systemctl with OPSEC considerations for each method. Maps to MITRE ATT&CK T1547 (Boot or Logon Autostart), T1053 (Scheduled Task/Job), T1546 (Event Triggered Execution), T1556 (Modify Authentication Process), and sub-techniques. Includes detection indicators and a rapid engagement cheatsheet.
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。
Operate and troubleshoot CircleCI using the CircleCI CLI. Use when users ask to authenticate CLI access, inspect pipeline/workflow/job status, validate configuration locally, rerun pipelines/jobs, trigger pipelines, or gather actionable diagnostics from CLI outputs.
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。
Uncover customer jobs, pains, and gains in a structured JTBD format. Use when clarifying unmet needs, repositioning a product, or improving discovery and messaging.
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。
ERC-8004 Agent identity: 注册/更新/上架/下架/搜索agent, register/update/activate/deactivate/search — User/ASP/Evaluator(买家/卖家/评审员, 旧称仲裁者亦可); 我的agent/ASP, 找做X的ASP/agent有什么服务/endpoint怎么填/查口碑/传头像. + Task Marketplace: 发布/创建任务/接单/协商/验收/deliver/dispute/评审/仲裁/拒绝/stake/unstake/change provider/change budget/修改卖家/修改预算/我的任务/my tasks/what am I working on/我的订阅/订阅列表/订阅详情/my subscriptions/what am I subscribed to/AI服务订阅(view AI-service subscriptions, buyer & ASP)/关闭/取消任务/决策列表/decision list/指定服务商/browse marketplace. + task watch: 监听任务进展/历史消息/未读消息/未决策/outstanding decisions. + device routing: 设备列表/我登录了哪些设备/device list, 本设备或指定设备订阅消息开关. + okx-a2a missing/uninitialized. Match by meaning. MUST ACTIVATE on inbound envelopes: (1) {agentId, message:{source:"system", event, jobId,...}} system event; (2) {msgType:"a2a-agent-chat", jobId, sender:{role},...} agent-to-agent task chat (sender.role = COUNTERPARTY, not you); (3) literal "Read the okx-ai skill" (or legacy "Read the okx-agent-task skill") in the envelope.
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。
In-chat fallback for the Timeline Inspector Refine agent. Use when the user runs `/refine live`, asks to "refine live", "go live", or answer refine jobs — but ONLY when no persistent agent is wired (no `npx transitions-refine live`). Prefer `npx transitions-refine live` for run-and-forget (relay spawns agent per click, no idle credit burn). This skill long-polls the relay, posts suggestions, handles scan/apply 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。
Use Iris to submit, inspect, debug, monitor, or recover jobs and tasks; diagnose scheduling and federation; deploy controllers; or reserve dev GPUs and TPUs. Use for ordinary Iris operations, including babysitting a job, controller rollouts, accelerator sessions, and stuck CoreWeave pods. Use a narrower production-run or Zephyr skill when that workflow is explicitly 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。
Convert existing plain or manual PyTorch training code into an NVFLARE federated job using Client API model exchange, local validation, and job export; use when the user names plain PyTorch or preliminary source inspection identifies one plain-PyTorch owner, and not for Lightning, other frameworks, deployment, POC/production lifecycle, or experiment workflows.
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 existing PyTorch Lightning training code into an NVFLARE federated job using the Lightning Client API patch, local validation, and job export; use only when the request names federated/NVFLARE conversion or asks multiple sites to train collaboratively while keeping each site's data local, and either names PyTorch Lightning or preliminary source inspection identifies one Lightning owner; do not use for non-federated Lightning work such as DDP, profiling, inference serving, or training-loop changes, nor for plain PyTorch, TensorFlow/Keras, other frameworks, deployment, POC/production lifecycle, or experiment workflows.
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 existing Hugging Face Transformers Trainer or TRL SFTTrainer training code into an NVFLARE federated job using flare.patch(trainer), local validation, and job export; use when the user names Hugging Face or preliminary source inspection identifies one Hugging Face owner, and not for manual PyTorch loops, Lightning, inference-only pipelines, deployment, or experiment workflows.
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。