「based」の検索結果

Comprehensive UI design guidance based on Refactoring UI principles covering design personality, color theory, layout strategy, visual hierarchy, typography, spacing, mobile-first responsive design, and writing copy that doesn't read like AI wrote it. Use this skill whenever the user is creating new pages, building components, making design additions or changes, reviewing UI code, asking about design decisions (colors, fonts, spacing, layout), writing or editing interface copy, or discussing visual improvements. Trigger for any design-related conversation including "how should this look", "what color", "font size", "spacing", "layout", "responsive", "mobile", "this copy", "headline", "button text", "microcopy", or when reviewing/critiquing existing interfaces.

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Use when the user asks for journal recommendation, 选刊, 投稿期刊筛选, 期刊匹配, 适合投稿的期刊, 投哪个期刊, or wants 3-5 suitable journals for a manuscript based on an abstract, discipline, CAS/JCR ranking, impact factor, Aims & Scope, review speed, OA/APC, and publication-risk checks.

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Semantic code understanding with IDE-like symbol operations. Use when: (1) Large codebase analysis (>50 files), (2) Symbol-level operations (find, rename, refactor), (3) Cross-file reference tracking, (4) Project memory and session persistence, (5) Multi-language semantic navigation. Triggers: "find symbol", "rename function", "find references", "symbol overview", "project memory". IMPORTANT: Prioritize Serena's symbolic tools over file-based grep/read for code exploration.

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Protocol-based dependency injection for testable Swift code — mock file system, network, and external APIs using focused protocols and Swift Testing.

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PostgreSQL database patterns for query optimization, schema design, indexing, and security. Based on Supabase best practices.

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Example project-specific skill template based on a real production application.

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Kotlin testing patterns with Kotest, MockK, coroutine testing, property-based testing, and Kover coverage. Follows TDD methodology with idiomatic Kotlin practices.

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Trigger when: (1) User mentions "manimgl" or "ManimGL" or "3b1b manim", (2) Code contains `from manimlib import *`, (3) User runs `manimgl` CLI commands, (4) Working with InteractiveScene, self.frame, self.embed(), ShowCreation(), or ManimGL-specific patterns. Best practices for ManimGL (Grant Sanderson's 3Blue1Brown version) - OpenGL-based animation engine with interactive development. Covers InteractiveScene, Tex with t2c, camera frame control, interactive mode (-se flag), 3D rendering, and checkpoint_paste() workflow. NOT for Manim Community Edition (which uses `manim` imports and `manim` CLI).

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Instinct-based learning system that observes sessions via hooks, creates atomic instincts with confidence scoring, and evolves them into skills/commands/agents. v2.1 adds project-scoped instincts to prevent cross-project contamination.

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C++ coding standards based on the C++ Core Guidelines (isocpp.github.io). Use when writing, reviewing, or refactoring C++ code to enforce modern, safe, and idiomatic practices.

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Persistent Obsidian-based memory for coding agents. Use at session start to orient from a knowledge vault, during work to look up architecture/component/pattern notes, and when discoveries are made to write them back. Activate when the user mentions obsidian memory, obsidian vault, obsidian notes, or /obs commands. Provides commands: init, analyze, recap, project, note, todo, lookup, relate.

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Comprehensive AI/ML development guide for LangChain, LangGraph, and ML model integration in FastAPI. Use when building LLM applications, agents, RAG systems, sentiment analysis, aspect-based analysis, chain orchestration, prompt engineering, vector stores, embeddings, or integrating ML models with FastAPI endpoints. Covers LangChain patterns, LangGraph state machines, model deployment, API integration, streaming, error handling, and best practices.

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Transition from static LLM chats to autonomous agents that execute multi-step tasks. Use this when you need to automate cross-platform reports (e.g., Snowflake to Google Docs), build self-service tools for non-technical teams, or create "anticipatory" engineering workflows that draft PRs based on Slack discussions.

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AgentHero AI - Hierarchical multi-agent orchestration system with PM coordination, file-based state management, and interactive menu interface. Use when managing complex multi-agent workflows, coordinating parallel sub-agents, or organizing large project tasks with multiple specialists. All created agents use aghero- prefix.

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Guidance for selecting appropriate AI model (sonnet vs haiku) based on task complexity, reasoning requirements, and performance needs. Use when implementing agents or justifying model selection.

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Evaluate a codebase for agent-friendliness based on autonomous agent best practices. Use when asked to "evaluate for agents", "check agent readiness", "audit for autonomous execution", "assess agent-friendliness", or when reviewing infrastructure for unattended agent operation. Also use when asked about making a codebase more suitable for AI agents or autonomous workflows.

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Analyze issues to identify the next work item and update focus.md. Enforces issue-first workflow and confidence-based batch limits.

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Orchestrates multi-agent workflows by delegating ALL tasks to spawned subagents via /spawn command. Parallelizes independent work, supervises execution, tracks progress in UUID-based output directories, and generates summary reports. Never executes tasks directly. Triggers on keywords: orchestrate, manage agents, spawn agents, parallel tasks, coordinate agents, multi-agent, orc, delegate tasks

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Headless browser automation CLI optimized for AI agents with accessibility tree snapshots and ref-based element selection

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Generates Agent definitions (.md files) based on user intent and standard templates.

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Appends relevant hashtags to journal entries based on content analysis. Intended for LLM execution.

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[00] TnR ABSOLUTE RIGOR — Systemic Verification Doctrine. Forced cognitive exertion for high-stakes investigations. Mandates absolute deconstruction, mathematical proof chains, and ≥95% derived certainty. Zero-tolerance for heuristics or sentiment-based reasoning. Shortcuts are defined as intentional hallucinations.

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Quantitative strategy frameworks: pairs trading/cointegration, volatility regime strategies, seasonality/calendar effects, multi-factor models (IC/IR), factor research and screening, correlation analysis, statistical methods (ADF/GARCH), strategy optimization, execution modeling, hedging, and ML-based prediction (sklearn). Also provides CLI access to run indicator scripts against K-line data. Triggers: "量化", "因子", "配对交易", "协整", "波动率策略", "季节性", "多因子", "IC", "机器学习", "对冲", "量化策略", "協整", "波動率策略", "季節性", "多因子", "對沖", "quant", "pairs trading", "cointegration", "volatility strategy", "seasonality", "multi-factor", "factor model", "IC IR", "machine learning", "hedging", "walk-forward", "配對交易", "機器學習", "因子選股"

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Quality validation specialist covering functional, performance, and security testing to ensure production readiness through evidence-based testing.

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。