1,891 件 ・ 関連度順
概要と使いどころ
Browser automation with persistent page state. Use when users ask to navigate websites, fill forms, take screenshots, extract web data, test web apps, or automate browser workflows. Trigger phrases include "go to [url]", "click on", "fill out the form", "take a screenshot", "scrape", "automate", "test the website", "log into", or any browser interaction request.
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。
Turn a feature idea into a clear PRD — problem, users, requirements, and success metrics.
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。
Train or fine-tune language and vision models using TRL (Transformer Reinforcement Learning) or Unsloth with Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV scripts with PEP 723 format, dataset preparation and validation, hardware selection, cost estimation, Trackio monitoring, Hub authentication, model selection/leaderboards and model persistence. Use for tasks involving cloud GPU training, GGUF conversion, or when users mention training on Hugging Face Jobs without local GPU setup.
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。
Build and publish a Gradio demo on Hugging Face Spaces for a user-provided LoRA. Use when someone asks to create, generate, ship, or publish a Space, demo, Gradio app, or playground for a LoRA — including LoRAs for Qwen-Image, Qwen-Image-Edit, LTX-Video, Wan, FLUX, SDXL, or other diffusion base models. Also triggers when someone describes a LoRA they trained or hosts on the Hub and wants to share it. Covers picking the right base pipeline and `diffusers` inference recipe, designing a UI tailored to the LoRA's task and inputs (Union/multi-task control, edit, video, image, etc.), respecting model-card recommendations (trigger words, steps, guidance, LoRA scale, example inputs), and shipping to ZeroGPU hardware as a private Space by default.
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 users /learn, save, remember, or distill lessons.
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。
Sets up Convex auth, identity mapping, and access control. Use for login, auth providers, users tables, protected functions, or roles in a Convex app.
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。
Prove a Convex feature works — seed, drive as multiple mocked users via convex-test, assert behavior including the negative authz cases (wrong user refused, data-scope enforced).
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 for ClawHub staff moderation actions with the repo-local ClawHub admin tool: skills, users, org publishers, plugin packages, trusted publishers, official publishers, and guarded staff email.
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。
Performs comprehensive C/C++ security review for memory corruption, integer overflows, race conditions, and platform-specific vulnerabilities. Use when auditing native C/C++ applications, reviewing daemons or services for memory safety, or hunting integer overflow / use-after-free / race conditions in userspace code.
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。
Time-of-Check / Time-of-Use (TOCTOU) race condition exploitation methodology across binary, kernel, filesystem, web, and container layers. Covers symbolic-link races (open/access/stat split), file-descriptor races, fopen/realpath traversal races, /proc and procfs races, FUSE-backed slow-fs races to widen the window, ptrace and signal races, kernel double-fetch / userspace pointer races, container/runc/symlink escape primitives, kubernetes admission/authz TOCTOU, web auth-vs-authz TOCTOU, JWT-claim TOCTOU at gateway vs service, payment/idempotency races, and modern race-amplification techniques (single-packet attack, slow loris, FUSE pause, cgroup freeze, scheduler shaping). Use when you've identified a 'check then act' pattern in code, when fuzzing for race conditions, or when exploiting concurrency bugs in privileged binaries / kernel / orchestrators.
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 fast, source-grounded UX research on the highest-signal problems users are experiencing with a user-specified digital product. Use when the user asks to research user pain, UX friction, onboarding issues, docs/help problems, developer experience friction, support pain, product workflow issues, or current user complaints for a named product.
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。
Base rules for end users calling NVIDIA cuOpt (routing/LP/MILP/QP/install/server). Not for cuOpt internals — use cuopt-developer for those.
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 product or business data to support a decision or recommendation. Use when a decision depends on metric-backed evidence, such as choosing a direction, prioritizing an opportunity, evaluating a change, segmenting users, sizing tradeoffs, or deciding what to do 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。
Optimize CircleCI configuration for speed, reliability, and maintainability. Use when users ask to improve `.circleci/config.yml`, reduce CI runtime, tune caching/workspaces/parallelism, remove pipeline waste, or fix flaky pipeline behavior caused by configuration choices.
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。
Translate all text in a Canva design to another language, creating a translated copy. Faster than manually copying and editing each text box in Canva's editor. Use when users say "translate my design to [language]", "make a Spanish/French/etc version", or "localize my Canva design".
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。
Resize a Canva design into multiple social media formats (Facebook post, Facebook story, Instagram post, Instagram story, LinkedIn post). Use this skill when users want to resize Canva designs specifically for multiple social media platforms in one operation, rather than resizing to a single format manually.
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。
Bulk-create Canva designs from tabular data using a brand template with autofill fields, producing one design per row. Use when users say "bulk create designs from this CSV", "generate one design per row", "create a design for each product", "batch generate from a template", or "autofill a template from a spreadsheet". Accepts any tabular data source — uploaded files, pasted tables, JSON, or URLs.
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 fix failing CircleCI builds quickly and safely. Use when users ask to investigate failed CircleCI jobs, triage flaky pipelines, identify root causes from logs, and implement minimal fixes in configuration, test setup, or build-related code paths.
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 CircleCI Chunk for AI-assisted CI/CD work through either the Chunk web UI or the chunk-cli. Trigger this skill when users ask to set up Chunk, troubleshoot or fix failing builds with Chunk, configure Chunk environments, schedule/proactively run Chunk tasks, or use chunk-cli commands such as init, validate, build-prompt, auth, sandbox, task, and skill install.
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。
Build a structured PRD that connects problem, users, solution, and success criteria. Use when turning discovery notes into an engineering-ready document for a major initiative.
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。
Expert Mermaid diagram creation, validation, and rendering with dual-engine output (SVG/PNG/ASCII). Supports all 20+ diagram types including C4 architecture, AWS architecture-beta with service icons, flowcharts, sequence, ERD, state, class, mindmap, timeline, git graph, sankey, and more. Features code-to-diagram analysis, batch rendering, 15+ themes, and syntax validation. Use when users ask to create diagrams, visualize architecture, render mermaid files, generate ASCII diagrams, document system flows, model databases, draw AWS infrastructure, analyze code structure, or anything involving "mermaid", "diagram", "flowchart", "architecture diagram", "sequence diagram", "ERD", "C4", "ASCII diagram". Do NOT use for non-Mermaid image generation, data plotting with chart libraries, or general documentation writing.
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。
Guide for creating AI subagents with isolated context for complex multi-step workflows. Use when users want to create a subagent, specialized agent, verifier, debugger, or orchestrator that requires isolated context and deep specialization. Works with any agent that supports subagent delegation. Triggers on "create subagent", "new agent", "specialized assistant", "create verifier". Do NOT use for Cursor-specific subagents (use cursor-subagent-creator instead).
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。