「discovery」の検索結果

Find, evaluate, and download common purchasable CAD parts from step.parts, including named off-the-shelf actuators, servos, motors, electronics boards, connectors, screws, bolts, nuts, washers, bearings, standoffs, and other catalog components. Use when Codex needs to search the hosted step.parts catalog before creating simplified placeholder geometry, resolve fuzzy part names, standards, aliases, or dimensions, choose a matching part, fetch a canonical .step file, verify checksums, or use the step.parts API/OpenAPI/catalog endpoints for standard part 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。

Find useful Plugin Index matches; inspect, scan, install, update and manage Agent Zero plugins.

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。

Evolutionary explanation-discovery engine for hard-to-vary causal theories of unknown phenomena. USE WHEN develop a new theory, explain a mystery, solve an explanatory gap, discover a mechanism, consciousness, origin, or scientific breakthrough. Also use for multi-cycle creative exploration. NOT FOR a single-pass divergent brainstorm (use BeCreative) or delivery validation alone (use Evals).

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。

Interactive voice-narrated tutorial on whatever the current conversation is about — teaches the topic beat by beat with ASCII diagrams in the terminal while speaking the actual lesson content aloud, asking one question per beat and quizzing until the ideas land. USE WHEN teach me, /teach-me, tutorial, teach me this, walk me through this interactively, explain this like a lesson, learn mode, quiz me, help me actually understand what we just did. NOT FOR plain written explanations with no dialogue (just answer directly), authored/published content, or discovery interviews that shape a half-formed idea (use grill-me).

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。

Audit and fix agent readiness: the Lighthouse Agentic Browsing fraction, accessibility tree for agents, robots.txt and Content-Signal for AI agents, WAF treatment of agent traffic, llms.txt, Markdown delivery, ai-catalog.json, /.well-known discovery files, and WebMCP tools. Exclude AI citability and brand signals (seo-geo) and commerce protocol depth (seo-ecommerce).

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 agent work needs file-backed context: durable scratchpads, tool-output offloading, just-in-time discovery, cross-agent handoff files, filesystem memory, or cleanup policies for context stored outside the prompt.

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。

Generate a Wren MDL project by exploring a database with available tools (SQLAlchemy, database drivers, MCP connectors, or raw SQL). Guides agents through schema discovery, type normalization, and MDL YAML generation using the wren CLI. Use when: user wants to create or set up a new MDL, onboard a new data source, or scaffold a project from an existing database.

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。

Wren CLI for AI agents — a semantic SQL layer over 22+ databases (Postgres, MySQL, BigQuery, Snowflake, Spark, …). The actual workflow guides live inside the `wren` CLI itself; this is just a discovery stub. Use whenever the user asks a data question (how many, show me, top N, compare, trend, breakdown, metric, revenue, customers, orders), wants to install / set up Wren Engine, connect a new database, connect SaaS data via dlt (HubSpot, Stripe, Salesforce, GitHub, Slack), generate or regenerate an MDL project from a database schema, enrich a project with business context (enum meanings, units, cubes like ARR / DAU / churn), or turn a project's context layer into a shareable GenBI web app / dashboard and deploy it to Vercel or Cloudflare. Triggers: 'install wren', 'set up wren engine', 'connect database to wren', 'connect SaaS to wren', 'load hubspot / stripe / salesforce data', 'generate mdl', 'scaffold wren project', 'enrich wren context', 'augment my project', 'add cubes', 'build a dashboard', 'make a shareable analytics app', 'deploy my context layer as a web app', 'genbi app', 'wren onboarding', 'wren usage', 'wren generate mdl', 'wren dlt connector', 'wren enrich context', 'wren genbi'.

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。

数学问题发现与证据检索。用于界定研究问题、查定义/定理谱系、检索 arXiv/Scholar/OpenAlex/Crossref、建立来源账本、证据图、查新或从证据缺口生成可证伪猜想。

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。

AI-powered tools for Web3 bug bounty automation. Use when you want to automate recon, run autonomous audits, or use AI agents for vulnerability 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。

Full end-to-end research pipeline: from a broad research direction through idea discovery, experiments, and review all the way to a polished paper PDF. Use when user says "全流程", "full pipeline", "从找idea到投稿", "end-to-end research", or wants the complete autonomous research lifecycle.

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。

Search GitHub, Stack Exchange, Chinese technical communities, official documentation, and general developer web sources for software errors, compatibility problems, API usage questions, and real-world workarounds. Use for debugging and discovery only; results are not paper-citation evidence.

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。

Workflow 1.5: Bridge between idea discovery and auto review. Reads EXPERIMENT_PLAN.md, implements experiment code, deploys to GPU, collects initial results. Use when user says "实现实验", "implement experiments", "bridge", "从计划到跑实验", "deploy the plan", or has an experiment plan ready to execute.

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。

KubeSphere extension management Skill. Use when user requests to install, configure, upgrade, uninstall extensions, or query extension info/troubleshoot issues. Includes extension discovery, dependency management, install configuration, version 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。

A skill that creates new Claude skills and automatically shares them on Slack using Rube for seamless team collaboration and skill 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。

Show the app connection picker or setup widget when users ask to connect apps, reopen setup, or need an app that isn't connected yet. Use QM’s authenticated backend for app discovery, consent, and execution without exposing project keys.

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。

Guides researchers through structured ideation frameworks to discover high-impact research directions. Use when exploring new problem spaces, pivoting between projects, or seeking novel angles on existing 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。

Provides guidance for automatically evolving and optimizing AI agents across any domain using LLM-driven evolution algorithms. Use when building self-improving agents, optimizing agent prompts and skills against benchmarks, or implementing automated agent evaluation loops.

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。

Battle-tested PyTorch training recipes for all domains — LLMs, vision, diffusion, medical imaging, protein/drug discovery, spatial omics, genomics. Covers training loops, optimizer selection (AdamW, Muon), LR scheduling, mixed precision, debugging, and systematic experimentation. Use when training or fine-tuning neural networks, debugging loss spikes or OOM, choosing architectures, or optimizing GPU throughput.

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 guidance for training and analyzing Sparse Autoencoders (SAEs) using SAELens to decompose neural network activations into interpretable features. Use when discovering interpretable features, analyzing superposition, or studying monosemantic representations in language models.

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。

Discover the user's local AWS context (active profile, region, account ID, caller identity) at the start of any AWS task. Use this skill before any other AWS work — deploying to SageMaker, creating resources, calling AWS APIs, or anything that touches an AWS account. Use it especially when the user has not specified a region or profile explicitly, when they say things like "use my AWS account", "deploy to AWS", "use my profile", or when about to make any AWS CLI or SDK call. Never guess the region or account ID — always use this skill to read it from the local configuration first.

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。

Triage supplied or imported security findings against a repository using its security policy and static code evidence. Accepts scanner reports, advisories, GitHub findings, and Jira or Linear tickets. Do not use for discovery, duplicate triage, runtime validation, or fixes.

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 deposition outline for a witness — pull their documents from the eDiscovery platform, organize topics around the case theory, and surface impeachment material. Use when the user says "depo prep for [witness]", "build a depo outline", or "prepare for [name]'s deposition".

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 guidance for integrating and building applications with shadcn/ui components, including component discovery, installation, customization, and best practices.

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。