「building」の検索結果

Designs publication tables for social-science manuscripts. Covers whether a result belongs in a table at all, column order, row grouping and panels, decimal precision, standard-error and confidence-interval conventions, self-contained titles and notes, and code-generated output. Use when building or revising a regression, balance, descriptive, or robustness table, or when working with modelsummary, stargazer, gt, esttab, or booktabs. Finished tables go to figure-table-audit for end-stage QA.

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Writes or reviews a pre-analysis plan before data collection — registry choice (OSF, AEA RCT Registry, AsPredicted, registered reports), PAP structure, models and decision rules locked in advance, analysis code run on simulated data, contingencies for attrition, failed manipulations, and exclusions, deviation records, and timeline. Operationalizes the pre-data-collection side of DA-RT. Use when the user asks to draft or audit a pre-registration or PAP, choose a registry, decide what to lock versus leave exploratory, or handle a later deviation. Hypotheses and estimands come from hypothesis-building, post-hoc reporting from methods-reporting.

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Drafts or audits a scientific paper’s introduction and framing — the real-world "why", an evidence audit, the funnel from theory to a falsifiable if-then with a named estimand, bridges across experiments, and a contribution list matched to the design. Use when the user asks to write, restructure, or critique an introduction, motivate or frame a paper, repair a gap-in-the-literature opening, sharpen or calibrate a contribution claim, or connect experiments into one argument. Sentence-level flow and wording are out of scope.

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Drafts or audits a scientific paper's introduction: real-world motivation, evidence audit, theory-to-if-then funnel with a named estimand, bridges across experiments, and calibrated contributions. Use to write, frame, or critique an introduction.

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Turns a theory into falsifiable, pre-registerable hypotheses: DGP and DAG, identification, estimands, SESOIs, test choice among NHST, equivalence, and minimum-effect tests, scope conditions, and hypothesis tiers. Use when converting a research question into testable predictions or predicting a null.

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Create, improve, or audit Vapi voice agent and Squad system prompts for production phone and web based voice agents. Use when the user wants help designing a Vapi assistant prompt, multi-assistant Squad prompt set, refining an existing prompt, creating prompt sections, building an intake or handoff workflow, improving tool-use instructions, adding guardrails, or optimizing voice-agent behavior for brevity, turn-taking, error handling, caller data collection, escalation, handoffs, and spoken formatting.

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Configure Vapi server URLs and webhooks to receive real-time call events, transcripts, tool calls, and end-of-call reports. Use when setting up webhook endpoints, building tool servers, or integrating Vapi events into your application.

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Use when modifying agentic contextual building blocks like skills (SKILL.md), slash commands, agents, hooks, plugins, marketplaces, and instruction files (CLAUDE.md, AGENTS.md, GEMINI.md, QWEN.md, Cursor, Copilot, Cline, or Kiro rules), tool configuration such as an OpenCode `opencode.json`, Muse Code hooks (`.muse/hooks.json`), a Grok Build `.grok/` project layer or a Google Antigravity customization root (`.agents/`, `.agent/`, `_agents/`, `_agent/` — hooks, MCP config, rules, registries and plugins), and committed project memory (`.agents/memory/`). Run skillsaw on the files you touched, apply autofixes, resolve remaining violations with `skillsaw explain` guidance, and re-lint until clean before considering the work complete.

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IOTA blockchain MCP server providing wallet management with human-in-the-loop signing, Move smart contract build/test/publish, and on-chain queries via JSON-RPC and GraphQL. Use when interacting with IOTA network from AI coding agents, managing IOTA wallets, building or publishing Move packages, querying on-chain objects or transactions, or checking epoch and network statistics.

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Use this skill when controlling the AltirraSDL Atari emulator programmatically — driving the simulator from a script, capturing screenshots from a running Atari, injecting joystick/keyboard input, frame-stepping for deterministic testing, reading or writing CPU/memory/chip state, setting breakpoints or watchpoints, profiling 6502 code, loading symbols, or building automated tests against an Atari binary. Triggers on phrases like "AltirraBridge", "AltirraSDL --bridge", "AltirraBridgeServer", "Altirra Python SDK", "headless Atari emulator", "screenshot the Atari", "automate AltirraSDL", "drive the emulator from Python", "Atari debugger over a socket", "altirra_bridge package", as well as on tasks involving the bridge protocol, the Python `altirra_bridge` package, or the C `altirra_bridge.h` SDK.

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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.

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。

Incremental LLM-friendly wiki generator for Obsidian note vaults. Use when: (1) Building wiki from notes, (2) Ingesting notes to wiki, (3) Obsidian LLM wiki, (4) Incremental knowledge base management. Triggers: 'build wiki from notes', 'ingest notes to wiki', 'Obsidian LLM wiki', 'incremental knowledge base'.

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Search engine optimization strategies and tactics for organic growth. Use when optimizing content for search, conducting keyword research, performing SEO audits, or building link strategies.

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。

Social media strategy, content creation, and platform optimization. Use when creating social content, developing engagement strategies, optimizing for platform algorithms, or building community.

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。

When the user wants to create SEO-driven pages at scale using templates and data. Also use when the user mentions "programmatic SEO," "template pages," "pages at scale," "directory pages," "location pages," "[keyword] + [city] pages," "comparison pages," "integration pages," or "building many pages for SEO." For auditing existing SEO issues, see seo-audit.

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。

Performance measurement, attribution modeling, and marketing ROI analysis. Use when setting up tracking, analyzing campaign performance, building attribution models, or creating marketing reports.

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 for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).

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。

Idiomatic Go patterns, best practices, and conventions for building robust, efficient, and maintainable Go applications.

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。

Modern Perl 5.36+ idioms, best practices, and conventions for building robust, maintainable Perl applications.

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Idiomatic Kotlin patterns, best practices, and conventions for building robust, efficient, and maintainable Kotlin applications with coroutines, null safety, and DSL builders.

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Pythonic idioms, PEP 8 standards, type hints, and best practices for building robust, efficient, and maintainable Python applications.

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Codified expertise for managing carrier portfolios, negotiating freight rates, tracking carrier performance, allocating freight, and maintaining strategic carrier relationships. Informed by transportation managers with 15+ years experience. Includes scorecarding frameworks, RFP processes, market intelligence, and compliance vetting. Use when managing carriers, negotiating rates, evaluating carrier performance, or building freight strategies.

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Patterns for building entity actors with Akka.Hosting - GenericChildPerEntityParent, message extractors, cluster sharding abstraction, akka-reminders, and ITimeProvider. Supports both local testing and clustered production modes.

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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.

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。