「rest」の検索結果

Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model.

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Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.

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Comprehensive toolkit for survival analysis and time-to-event modeling in Python using scikit-survival. Use this skill when working with censored survival data, performing time-to-event analysis, fitting Cox models, Random Survival Forests, Gradient Boosting models, or Survival SVMs, evaluating survival predictions with concordance index or Brier score, handling competing risks, or implementing any survival analysis workflow with the scikit-survival library.

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Use when the user asks how to build with OpenAI products or APIs and needs up-to-date official documentation with citations (for example: Codex, Responses API, Chat Completions, Apps SDK, Agents SDK, Realtime, model capabilities or limits); prioritize OpenAI docs MCP tools and restrict any fallback browsing to official OpenAI domains.

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Access NIH Metabolomics Workbench via REST API (4,200+ studies). Query metabolites, RefMet nomenclature, MS/NMR data, m/z searches, study metadata, for metabolomics and biomarker discovery.

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Query FRED (Federal Reserve Economic Data) API for 800,000+ economic time series from 100+ sources. Access GDP, unemployment, inflation, interest rates, exchange rates, housing, and regional data. Use for macroeconomic analysis, financial research, policy studies, economic forecasting, and academic research requiring U.S. and international economic indicators.

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Primary retained Python toolkit for molecular biology sequence work. Preferred for sequence manipulation, FASTA/FASTQ/GenBank parsing, Bio.Entrez, BLAST workflows, alignments, structures, and phylogenetics. For biological database evidence lookup, use bio-database-evidence. For single-cell workflows use scanpy. For direct literature REST API, use pubmed-database.

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Design engineering principles for making interfaces feel polished. Use when building UI components, reviewing frontend code, implementing animations, hover states, shadows, borders, typography, icons, micro-interactions, enter/exit animations, or any visual detail work. Supports quick and full review modes. Triggers on UI polish, design details, "make it feel better", "feels off", stagger animations, border radius, optical alignment, font smoothing, tabular numbers, image outlines, box shadows, icons, icon stroke weight, icon states, motion restraint.

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Design principles and creative direction for building HTML artifacts — pages, reports, plans, landing pages, demos, decks, and small tools. Use when creating or restyling any visual HTML deliverable and deciding its palette, type pairing, layout, theming, or overall register, or when the output must not look generically AI-generated.

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Use whenever the agent creates, writes, moves, or renames a file in a team/delegate (shared) workspace, OR when the user asks to organize, clean up, restructure, audit, or find files in any workspace or the Vault, OR when starting a multi-file task or named project. Enforces a purpose-based folder convention (flat mode: notes/, data/, outputs/, scripts/, archive/; project mode: projects/<slug>/{docs,assets,source,reports,research}/), per-agent namespacing under shared/<agent_key>/, and pre-write discovery via memory_search, vault_search, knowledge_graph_search to surface related files and avoid duplicates. Trigger before any write_file or exec at workspace root, when starting a project, generating reports/assets/exports, delegating, or when the user says "messy", "where did I save", "tổ chức lại", "dọn workspace", "tạo report", "find related", "search vault". Do NOT trigger for read-only ops, edits inside an existing project tree (cloned repo), or short-lived files deleted in the same turn.

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Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.

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Make a website's team or about page photos look like one set — scan the headshot folder locally, flag photos whose aspect ratio, brightness, colour temperature, background or resolution differ from the rest, write a shared photo spec, and optionally export every photo at the same crop and size. Use when building or fixing a team page, "our team photos look inconsistent", "make all headshots the same size", "standardize staff photos", or when adding a new hire's photo to an existing page. Uses the free ProfessionalHeadshot.io MCP for framing guidance and photo scores when connected; the local scan works without it.

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Use when the user asks about a US-listed company or the US market and wants sourced numbers, such as SEC filings and what a 10-K or 10-Q says, income statements and balance sheets, earnings call transcripts, insider or congressional trades, 13F institutional holders, short interest, stock prices, option chains, stock screens or macroeconomic indicators. Calls the Equibles MCP server.

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Full inbox management across all channels — WhatsApp (wacli), Email (Gmail MCP), Slack (MCP), Telegram (user-auth MCP), Discord (webhook + REST read), Notion (MCP — comments, mentions, assigned tasks). Scans FULL inbox (not just unread), identifies messages needing replies, archives handled conversations.

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Social media scheduling and publishing for AI agents. Use when the user wants to schedule posts, connect social accounts, upload media, or publish campaigns to X, LinkedIn, Instagram, Facebook Pages, TikTok, Discord, Telegram, YouTube, Reddit, WordPress, or Pinterest through SocialClaw.

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Submit an AI agent to agentlaunch (a Product Hunt-style directory for AI agents) and read the directory via a public, no-auth REST API. Use when listing, launching, publishing, or registering an agent, or browsing existing ones.

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X (Twitter) data extraction and monitoring via Xquik: tweet search, user lookup, follower extraction, giveaway draws, trending topics, account monitoring with webhooks, reply/retweet/quote extraction, community and Space data, follow checks. 22 MCP tools + REST API.

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Publish and schedule content to 15 social platforms through one Upload-Post API call: TikTok, Instagram, YouTube, LinkedIn, Facebook, X, Threads, Pinterest, Bluesky, Reddit, Discord, Telegram, Mastodon, WordPress, and Google Business Profile. Use when the user wants to post or schedule a video, photo carousel, text, or document across several platforms at once, check upload status, pull analytics, or run Instagram comment-to-DM funnels.

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Query Auto Ontology and validate generated SQL, rows, and answers. Use for MCP or REST access, readiness, authentication, conversations, and grounded questions.

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Use when operating PAIDF Curation and Retrieval or NVIDIA Cosmos Curator pipelines (split, filter, caption, embed, dedup, shard, image annotate) or PAIDF Data Mining nearest-neighbor matching on Curator embeddings. Activate for Make or CLI pipeline config, GPU run preflight, FFmpeg sidecar, SAM3 keys, or Curator-to-TAO handoff. Do not use for generic ETL, vector-database RAG, model training, orchestration, or embeddings outside Cosmos Curator and PAIDF Data Mining.

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Resiliency features in Megatron Bridge including fault tolerance, straggler detection, in-process restart, preemption, and re-run state machine.

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Manage durable working-session memory for coding agents. Use when a user asks to preserve or recover agent context across disconnects, VS Code restarts, long-running work, handoffs, or any session where important state should be written periodically under the repo's session directory. Do NOT use for: simple questions, short tasks, one-off commands, linting, or code review.

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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.

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Running an in-bundle DOCA service as a container on a BlueField (Argus, DMS, Firefly, or UROM service): kubelet standalone watching a static-pod manifests directory, YAML pod-spec drop, kubelet status / ENTRYPOINT logs / per-service liveness. Trigger on "how do I run my built service on the BlueField?", "where do I drop the pod-spec YAML?", "pod stuck in Pending / ImagePullBackOff / CrashLoopBackOff", "container Running but service isn't ready", "pod restart-loops after edit", or "DMS and Firefly together", even without naming container deployment. A DOCA-linked binary launched directly on the host or Arm belongs to doca-bare-metal-deployment. Covers smoke-before-bulk and the layered error taxonomy (pod-spec, scheduling, image pull, runtime, mount, network, version, host). Refuse and route elsewhere for per-service config schemas, DOCA install, library-API questions, external NVIDIA services (BlueMan, HBN, SNAP, Virtio-net), or full Kubernetes-cluster ops.

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。