「domain」の検索結果

Applies the reasoning of Pieter Abbeel, robotics and reinforcement learning expert, UC Berkeley professor, and co-founder of Covariant. Use this skill whenever you are designing AI systems, tackling Sim2Real transfer, deploying machine learning in the physical world, or evaluating reinforcement learning architectures. Trigger this skill for questions about domain randomization, reward design, bootstrapping real-world AI, robotics hardware assumptions, or shifting from hard-coded rules to data-driven deep learning. It helps ground theoretical AI in physical embodiment and pragmatic deployment.

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Applies the reasoning style of Kaiming He, computer vision pioneer and creator of ResNet. Use this skill whenever you are designing deep learning architectures, debugging neural network optimization, formulating generative AI problems, or bridging AI with other scientific domains. Trigger this skill for discussions on network depth, weight initialization, residual learning, flow matching, or when reframing discriminative tasks as conditional generation. It emphasizes simplicity in complex visual problems, end-to-end optimization, and viewing AI as a universal language for science.

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Applies the reasoning, architectural principles, and AI philosophy of Christopher Manning (natural language processing expert, Stanford University, director of Stanford AI Lab). Use this skill whenever you are discussing natural language processing, LLM architecture, AI research strategy, cognitive science, or the evolution of machine learning. Trigger this skill for questions about AGI timelines, academic vs. industry research trade-offs, linguistic structure in neural networks, modularity in AI design, or evaluating true intelligence versus mere memorization. Channel his pragmatic focus on domain science, adaptability, and competing on ideas rather than raw compute.

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Applies the reasoning style of Daphne Koller (machine learning pioneer, co-founder of Coursera, founder and CEO of Insitro). Use this skill whenever you encounter problems involving AI and machine learning in biology, drug discovery, interdisciplinary collaboration, data generation vs. data mining, or transitioning from academia to industry. Trigger this skill when advising on career trade-offs, building cross-functional teams (especially bridging engineers and domain experts), designing data pipelines, evaluating causality vs. correlation, or applying AI to physical systems ('where bits meet atoms'). Channel her focus on fit-for-purpose data, pragmatism, and disproportionate leverage.

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Suede AI blunt A-F ship grade for a code change across correctness, security and permissions, data and state, domain truth, UX and release behavior, tests and verification, and deploy readiness, with Instant-F triggers and evidence-based grade caps on auth, payment, migration, and public-API surfaces. Use when asked to grade this, give it a letter, is this an A, how ready is this to ship, or should this merge: when the caller wants the verdict without a findings list. NOT FOR: findings, evidence, and fix briefs (use suede-code-review, or suede-code for findings plus grade); enforcing the verdict in CI (use suede-ci-gate); eval coverage for AI behavior (use suede-ai-eval).

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Suede-owned marketing attribution discipline. Use when the user wants to figure out which marketing actually drives conversions and revenue, choose or interpret an attribution model, or reconcile conflicting numbers across tools. Also use when the user mentions 'attribution,' 'attribution model,' 'first-touch vs last-touch,' 'multi-touch,' 'which channel drives revenue,' 'what's my real CAC,' 'my dashboards disagree,' 'Google/Meta says X but GA says Y,' 'MMM,' 'incrementality,' 'geo lift,' 'holdout test,' 'how did you hear about us,' 'self-reported attribution,' 'dark social,' or wants to instrument attribution themselves, 'stitch my bookings to their source,' 'SavvyCal/Calendly attribution,' 'close the identify gap,' 'track conversions on a third-party domain,' 'first-party attribution.' NOT FOR: general analytics instrumentation and reporting (use suede-analytics) or revenue-ops pipeline work (use suede-revops).

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Find web sources with query-relevant page excerpts and optional full-page content, and discover workflows, data APIs, and indexes. Use for web research or finding structured records, listings, transcripts, and datasets. Supports semantic tool discovery, domain matching, and progressive catalogue browsing.

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Create, review, and improve skills for Pi agents. A skill is a folder with a SKILL.md that teaches an agent specialized workflows, domain knowledge, or tool integrations. Use when asked to create a new skill, improve an existing skill, review a skill for quality, scaffold a skill from a workflow, or convert documentation into a skill. Also triggers on "make a skill for", "build a skill", "skill for [topic]", "teach the agent to", or "package this workflow as a skill".

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Automated Planning utilities for loading PDDL domains and problems, generating plans using classical planners, validating plans, and saving plan outputs. Supports standard PDDL parsing, plan synthesis, and correctness verification.

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Use when modelling a rich domain in Java - SOLID principles, encapsulated entities that enforce their own invariants, and value objects over primitives

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Phase B of a WinForms-to-Avalonia migration: extract UI-independent application behavior, state rules, use cases, and boundary contracts with TDD and no WinForms/Avalonia references. Use only when an explicit migration still has business behavior in Forms or UI helpers, or when the orchestrator routes here. Do not build Avalonia XAML in this phase.

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Search the agent skills ecosystem to discover and install skills that extend AI coding agent capabilities. Use when user asks "how do I do X" (X being a common task), "find a skill for X", "is there a skill that can...", or expresses interest in extending agent capabilities. Triggers on "find a skill", "install a skill", "skill for [task]", "can you do X", "I need help with [domain]", "how do I [task]". Do NOT use when user has explicitly asked to proceed without a skill, or when the task is better handled by agent's built-in capabilities (file operations, git, basic coding).

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Creates FastAPI endpoints with layered architecture (Router → Service → Repository). Use when creating new API endpoints, CRUD operations, or scaffolding a new domain module in a FastAPI project.

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SEO, AEO (Answer Engine Optimization), and GEO strategy for search engines and AI visibility. Use when working on "SEO audit," "technical SEO," "on-page SEO," "AI search optimization," "AEO," "GEO," "AI visibility," "optimize for ChatGPT," "optimize for Perplexity," "AI Overviews," "answer engine optimization," "generative engine optimization," "AI citations," "featured snippets," "meta tags," "schema markup," "search ranking," "content optimization," "E-E-A-T," "structured data," "search console," "SEO health check," "why am I not ranking," "AI search readiness," "backlink strategy," "link building," "domain authority," "programmatic SEO," "SEO at scale," "template-based SEO," "AEO monitoring," "AI search monitoring," "JSON-LD," "rich snippets," "schema.org," "SERP analysis," "search intent," "site architecture," "information architecture," "URL structure," "internal linking," or "navigation." For keyword research, see keyword-research-and-clustering.

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Email/SMS lifecycle and deliverability framework for SMB Product-Builder products that send transactional or lifecycle messages (booking reminders, CRM sequences, receipts, win-back). Codifies provider selection (Resend/Postmark/Twilio/SendGrid), domain auth (SPF/DKIM/DMARC), consent and compliance (TCPA, CAN-SPAM, CASL, quiet hours, double opt-in), suppression-list discipline, and the transactional-vs-marketing split. Applied by integrations-engineer and senior-dev whenever a feature sends messages — so deliverability and consent are designed in, not bolted on after the first spam complaint.

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Domain knowledge for SMB industry products — the vocabulary, the entities a spec must model, the incumbent to route around, and what a naive build gets wrong — one file per industry, plus local SEO and money-on-a-phone. Read the one file that applies before speccing or building the product.

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Decide when AutoCode acts on its own and when it must stop and ask the user. Runs before any consequential action — deleting files, spending money, buying a server, deploying to production, force-pushing, binding a domain, sending or publishing anything. Ordinary reversible work proceeds silently; only costly or hard-to-undo actions pause for the user. Powered by I-Lang v5.0 vector judgment.

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 web sources with query-relevant page excerpts and optional full-page content, and discover workflows, data APIs, and indexes. Use for web research or finding structured records, listings, transcripts, and datasets. Supports semantic tool discovery, domain matching, and progressive catalogue browsing.

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。

Deep research on a topic — use when investigating unfamiliar domains, answering complex questions requiring multiple sources, or when an evaluation flags something for deeper analysis

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Builds production AI bots with Python for support, sales, and custom domains. Use when designing conversation flows, personas, escalation, or LangGraph state.

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Adds per-skill learnings loops for dated patterns, mistakes, and domain facts. Use when wiring skill memory, consolidation, or drift audits.

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Drive Blender from natural language: create or edit 3D models, materials, lighting, cameras, renders, animation, and exports through Blender MCP. Route complete scenes to domain skills and wireframe/reference requests to reconstruction skills. Also use for 3D requests that do not name Blender. Requires a configured official or ahujasid MCP backend.

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Refracting thinking by challenging assumptions, combining cross-domain knowledge, and shifting perspectives to reframe problems. Use for stuck situations or paradigm shifts. Does not write code.

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Orchestrating multi-specialist task chains and scope-adaptive product delivery: classifies intent, selects and executes the minimum viable chain, aggregates results, and verifies acceptance criteria. For multi-domain tasks, build-first delivery, and product lifecycle execution.

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。