1.1万 件 ・ 関連度順
概要と使いどころ
Convert evaluation traces and production logs into SFT examples and preference pairs. Use when graded traces or failure examples exist and need to become training data, when applying rejection sampling to model outputs, or when building DPO pairs from passing and failing runs.
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。
Debug LLM applications using the Phoenix CLI. Fetch traces, analyze errors, structure trace review with open coding and axial coding, inspect datasets, review experiments, query annotation configs, and use the GraphQL API. Use whenever the user is analyzing traces or spans, investigating LLM/agent failures, deciding what to do after instrumenting an app, building failure taxonomies, choosing what evals to write, or asking "what's going wrong", "what kinds of mistakes", or "where do I focus" — even without naming a technique.
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。
Examine NTFS slack space, MFT entries, the USN Change Journal, and Alternate Data Streams (ADS) to recover hidden or residual data, reconstruct deleted-file metadata, and reconstruct available file-system change activity from USN records. Use during deep forensic analysis of an NTFS image when standard file recovery is insufficient, such as hunting for data hidden in ADS.
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。
State-space model with O(n) complexity vs Transformers' O(n²). 5× faster inference, million-token sequences, no KV cache. Selective SSM with hardware-aware design. Mamba-1 (d_state=16) and Mamba-2 (d_state=128, multi-head). Models 130M-2.8B on HuggingFace.
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 Google Meet meeting space and share the join link.
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。
Google Workspace Events: Renew/reactivate Workspace Events subscriptions.
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。
Google Workspace Events: Subscribe to Workspace events and stream them as NDJSON.
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 one, multiple, or all explicitly requested FastGPT i18next namespace JSON files from the completed Simplified Chinese source into every supported target locale, with product-language research and structural validation. Use only when the user explicitly invokes `$i18n-translate` and identifies namespace names, files under `packages/web/i18n/zh-CN/`, or all namespaces; never trigger implicitly for ordinary i18n, copywriting, or documentation work.
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 Cua Volume, the one volume every Space and agent of this user shares. Inside a Space it is mounted as a folder (/volume on Linux, ~/Cua Volume on macOS), so any program reads and writes it directly. Use it for anything that must outlive this Space - your memory and outputs in your agent home, shared reference in public/ - and to hand files to the user or to other agents. Use when the user mentions the volume, Cua Volume, shared files, your home or memory, or saving results.
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。
Find the expansion whitespace in an account or a book - what they own vs. what they could own, the evidence for each play, and the open opps to create. Use when the user asks "what's the whitespace at [account]", "where can I expand [account]", "upsell opportunities in my book", or "which customers should I be growing".
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。
Atlassian Confluence expert for creating and managing spaces, knowledge bases, and documentation. Configures space permissions and hierarchies, creates page templates with macros, sets up documentation taxonomies, designs page layouts, and manages content governance. Use when users need to build or restructure a Confluence space, design page hierarchies with permission structures, author or standardise documentation templates, embed Jira reports in pages, run knowledge base audits, or establish documentation standards and collaborative 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。
End-user control of cmux topology and routing (windows, workspaces, panes/surfaces, focus, moves, reorder, identify, trigger flash). Use when automation needs deterministic placement and navigation in a multi-pane cmux layout.
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 headless CMS operations in Contentful -- list spaces, retrieve space metadata, and update space configurations through the Composio Contentful 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。
Coordinate several coding agents in parallel through Superset, each in its own workspace, with follow-ups, progress reads, dependency tracking, and structured results. Use when the user asks to delegate, parallelize, fan out, or split up coding work across agents, workspaces, or hosts, hand work between agents, or monitor workers, including "spin up agents for each of these", "run these in parallel", "what are my workers doing". Not for ordinary single-agent workspace, task, or automation 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。
State-space model with O(n) complexity vs Transformers' O(n²). 5× faster inference, million-token sequences, no KV cache. Selective SSM with hardware-aware design. Mamba-1 (d_state=16) and Mamba-2 (d_state=128, multi-head). Models 130M-2.8B on HuggingFace.
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。
Parses error messages, traces execution flow through stack traces, correlates log entries to identify failure points, and applies systematic hypothesis-driven methodology to isolate and resolve bugs. Use when investigating errors, analyzing stack traces, finding root causes of unexpected behavior, troubleshooting crashes, or performing log analysis, error investigation, or root cause 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。
Use when the user asks about finding the best, top, or recommended model for a task, wants to know what AI model to use, or wants to compare models by benchmark scores. Triggers on: "best model for X", "what model should I use for", "top models for [task]", "which model runs on my laptop/machine/device", "recommend a model for", "what LLM should I use for", "compare models for", "what's state of the art for", or any question about choosing an AI model for a specific use case. Always use this skill when the user wants model recommendations or comparisons, even if they don't explicitly mention HuggingFace or benchmarks.
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 for Hugging Face Dataset Viewer API workflows that fetch subset/split metadata, paginate rows, search text, apply filters, download parquet URLs, and read size or statistics.
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 build tool/scripts or achieve a task where using data from the Hugging Face API would help. This is especially useful when chaining or combining API calls or the task will be repeated/automated. This Skill creates a reusable script to fetch, enrich or process 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。
Hugging Face CLI to estimate the required memory to load Safetensors or GGUF model weights for inference from the Hugging Face Hub
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 evaluations for Hugging Face Hub models using inspect-ai and lighteval on local hardware. Use for backend selection, local GPU evals, and choosing between vLLM / Transformers / accelerate. Not for HF Jobs orchestration, model-card PRs, .eval_results publication, or community-evals automation.
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。
Publish and manage research papers on Hugging Face Hub. Supports creating paper pages, linking papers to models/datasets, claiming authorship, and generating professional markdown-based research articles.
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 visualize ML training experiments with Trackio. Use when logging metrics during training (Python API), firing alerts for training diagnostics, or retrieving/analyzing logged metrics (CLI). Supports real-time dashboard visualization, alerts with webhooks, HF Space syncing, and JSON output for automation.
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。
Manage GSD workspaces — create, list, or remove isolated workspace environments
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。