「search run」の検索結果

動画・音声をVideoDBに取り込み、発話や映像から場面を検索して切り抜きを作成。字幕や音声の追加、画面比率の変換、ライブ映像の通知設定も支援します。

• 発話や映像から場面を探して切り抜きたいとき
• 字幕・BGM・吹き替えの追加
• 動画の解像度や画面比率の変更

遺伝子情報や塩基・アミノ酸配列をggetで検索し、配列の類似性や遺伝子群の特徴を調べながら、結果と検索条件を再確認できる記録にまとめるスキル。

• 遺伝子IDや転写産物を調べたいとき
• 塩基・アミノ酸配列の取得
• BLASTやBLATでの簡易配列検索

1Passwordに保存したパスワードやAPIキーを、コマンド実行や設定テンプレートで使うためのスキル。CLIの導入、認証、秘密情報の取得・受け渡しを案内します。

• 1Password CLIの導入と設定
• 保存済みAPIキーでコマンド実行
• 設定テンプレートへの秘密情報の埋め込み

Runs maximum-saturation research with a cooperating team, claim-graph gating, and a cited, QA'd deliverable. Use when the user explicitly asks for research or a deep investigation, including any 'ulw' research wording.

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。

Finds new job postings matching your profile via installed portal-search CLIs (LinkedIn, local job boards, and any skills added with /add-portal). Deduplicates across runs. Triggers on: job scrape, find jobs, search jobs, new jobs, job search, scrape jobs, /scrape

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。

Two related workflows for a locally-cloned codebase, in one skill. Documentation mode produces a single, comprehensive, verifiable architecture document primarily by reading files on disk (local-first) — use it whenever the user wants to understand, map, document, research, or onboard onto a codebase ("research this repo", "write up the architecture", "do an architecture deep dive", "document how this codebase works", "map the system design", "create an onboarding doc"). Modernization mode generates a phased plan to modernize, migrate, upgrade, or rewrite a legacy system ("modernize this", "plan the migration", "how would we rewrite this", "how do we get off this legacy stack"); if no architecture document exists yet it first runs Documentation mode, then continues straight through to the plan. It assumes the legacy stack may be dead, runs a time-boxed feasibility spike, and picks the highest achievable rung on a safety ladder instead of demanding a fully-green legacy CI gate up front.

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。

Autonomous iterative experimentation loop for any programming task. Guides the user through defining goals, measurable metrics, and scope constraints, then runs an autonomous loop of code changes, testing, measuring, and keeping/discarding results. Inspired by Karpathy's autoresearch. USE FOR: autonomous improvement, iterative optimization, experiment loop, auto research, performance tuning, automated experimentation, hill climbing, try things automatically, optimize code, run experiments, autonomous coding loop. DO NOT USE FOR: one-shot tasks, simple bug fixes, code review, or tasks without a measurable metric.

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。

A short self-bias checklist to run at the START of any investment research task (stock screen / sector study / company deep-dive). Four biases that systematically warp AI research — leader-bias (only big caps), English-bias (miss JP/KR/TW players), narrative-bias (chase concept labels), confirmation-bias (only bullish evidence) — plus recency-bias. Load this first, then research with the corrections in mind. Not a workflow, just a 60-second attitude reset that materially improves coverage and honesty.

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。

Audits and fixes both halves of being found — classic search and AI answers. Search side: rankings, site structure, page speed, titles and descriptions, and content that answers what customers search for. AI side: whether assistant crawlers can reach the site, pages render without JavaScript, an llms.txt file, schema.org data, and presence in the directories assistants read. For online stores it also runs a bulk catalog cleanup, rewriting product titles, descriptions, alt text, and SEO fields in approved batches. Works on any public website with no connector. Use this whenever the owner talks about being found online, including "nobody finds us on Google," "how do I show up in ChatGPT," "make my website AI friendly," "SEO audit," "GEO," "AEO," "AI search visibility," "my competitor ranks above me," "fix my product listings," or "I asked an AI about my industry and we weren't in the answer."

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 an end-to-end workflow that chains `research-refine` and `experiment-plan`. Use when the user wants a one-shot pipeline from vague research direction to focused final proposal plus detailed experiment roadmap, or asks to "串起来", build a pipeline, do it end-to-end, or generate both the method and experiment plan together.

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。

NVIDIA's runtime safety framework for LLM applications. Features jailbreak detection, input/output validation, fact-checking, hallucination detection, PII filtering, toxicity detection. Uses Colang 2.0 DSL for programmable rails. Production-ready, runs on T4 GPU.

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-to-end data analysis workflow in R or Python — from exploration through regression to publication-ready tables and figures. Make sure to use this skill whenever the user wants to run any empirical analysis, write analysis code, or produce output from data. Triggers include: "analyze this data", "run a regression", "write R code for this", "write Python code for this", "I have a dataset", "help me with this regression", "run a DiD", "run an RDD", "event study", "IV regression", "fit a model", "produce a table", "make a figure", "explore my data", or any request involving a dataset path or empirical estimation.

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。

This skill covers applied microeconomic empirical methods and research design. Use when the user is selecting an identification strategy, comparing estimators, running diagnostics, designing a research study, or evaluating an empirical strategy. Triggers on "which method", "what estimator", "how to choose", "method comparison", "empirical strategy", "research design", "applied micro", "identification strategy", "power analysis", "design-based", "model-based", "minimum detectable effect", "specification".

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。

Runs maximum-saturation research with a cooperating team, claim-graph gating, and a cited, QA'd deliverable. Use when the user explicitly asks for research or a deep investigation, including any 'ulw' research wording.

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 the PAIDF Orchestration Image Attribute Augmentation DAG on Kubernetes - person-crop clothing augmentation, attribute search, and augmented dataset generation. Select for requests about image attribute augmentation, person attribute search, person re-identification data, clothing augmentation, attribute captions, augmentation payloads, run status, or result retrieval. Runs environment setup first when controller readiness is unknown. Not for video or defect-image generation.

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。

Auto-grow a pro-workflow wiki by running a budget-capped BFS research loop over pluggable source fetchers (web, arXiv, GitHub). Each iteration pops a seed from the queue, fetches sources, drafts a wiki page, dedupes claims against existing pages, enqueues follow-up seeds. Halts on budget cap, depth cap, or convergence. Use when the user says "research <topic>", "grow the <slug> wiki", "auto-research", or wants a knowledge base that builds itself overnight.

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 repeated, measured Git experiments toward a numeric target; keep improvements and revert failures. Use for autonomous optimization or managing an autoresearch run, not one-shot edits.

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。

Designs, reviews, and debugs DynamoDB data layers from design axioms — enumerates access patterns, chooses partition/sort keys and GSIs, decides single-table vs. multi-table, configures Streams, Global Tables, TTL, vector indexes for similarity search, and zero-ETL integrations to OpenSearch/Redshift/SageMaker Lakehouse, and produces a defensible data-layer design with a monthly cost estimate and optional live validation. Applies whenever a user is designing, reviewing, or refactoring anything backed by DynamoDB — schemas, access patterns, GSIs, single- vs. multi-table choices, Streams consumers, transactional outboxes, Global Tables, zero-ETL pipelines, or storing embeddings and running semantic/vector similarity search with SearchVectors on items already in DynamoDB — even when they don't say "axioms" or "design review." Also applies when debugging hot partitions, throttling, unbounded Scans, LWW conflicts, or surprise bills on DynamoDB workloads.

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。

SkillsBench task PR review — classifies the task track (standard / research / multimodal), runs static policy checks against the track-specific rubric, benchmarks the task across oracle plus Claude and Codex (with and without skills), audits trajectories for cheating and skill invocation, and produces a `pr-N-task-timestamp-run.txt` review report alongside a `prN.zip` bundle of trajectories. Use when reviewing a SkillsBench task PR (by number, branch, or local task path), when the user asks to review a task, run benchmarks on a PR, audit a submission, classify a task as research or multimodal track, or prepare a comment to post on a SkillsBench PR.

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 ONLY when the user asks to check, validate, review or audit analysis code for scientific correctness, or when you are the reviewer agent running its checks. Never run it unprompted as part of ordinary analysis work. Covers physics, earth/geo, biology, chemistry, social science and bioprocess/fermentation. Runs a deterministic domain-correctness gate that catches code which runs but is scientifically wrong (unit/dimension mismatch, Euclidean distance on lat/lon without a CRS, 0-based/1-based coordinate and strand errors, impossible SMILES valence, uncorrected multiple comparisons, averaging a categorical code, unconstrained kinetic-parameter fits, kLa computed from raw DO instead of the driving force, arithmetic mean of raw CFU counts, ANOVA/Tukey with no assumption check, a curvature DOE design fit with a first-order model, an ML model fit across process scales with no correction). Surfaces structured findings; never claims the code is correct.

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。

Gated checklist to choose or review a library, framework, storage, data format, model, build-vs-buy or architecture. Run favorites-search before external research when installed. Compare candidates and trade-offs; select within authorization or return user-owned choices. Use for 用哪个 / 选什么框架 / 要不要自建 / A 还是 B / 这个方案行不行 and before committing to one. Research reports → deep-research.

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 a brand-new SearchCLI/vs user from first-time usage questions to sign-up, purchase of Viking AI Search, AK/SK setup, and a working authenticated CLI. Use when the user says they downloaded or installed SearchCLI/vs and asks how to use it, how to start, sign up, buy, or onboard.

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。

This skill should be used when user asks to "upload my model to Ultralytics Platform", "push this run to the platform", "upload a dataset to platform", "download a dataset from platform", "search platform datasets", "start cloud training", "train on platform GPUs", "export a model on platform", "deploy a model endpoint", "run Moondream on Platform", "auto-annotate a Platform dataset", "run hosted AI inference", "why is my run not showing on platform", or mentions platform.ultralytics.com, ul:// URIs, ultralytics-platform, or ULTRALYTICS_API_KEY.

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。

Continuous product discovery research operations — picking the method that fits the question, recruiting and screening participants, building interview guides, and scoring evidence into insights. Use when planning or running discovery.

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。