「book」の検索結果

Apply John Ousterhout-inspired software design rules when reducing complexity, designing module boundaries, or reviewing APIs and abstractions.

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。

Apply Hunt and Thomas-inspired pragmatic programming rules when improving engineering judgment, feedback loops, DRY knowledge, orthogonality, automation, or adaptability.

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。

Apply Martin Kleppmann-inspired data-system rules when designing reliability, scalability, consistency, replication, transactions, streams, or schema evolution.

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。

Apply Martin Fowler-inspired refactoring rules when improving existing code structure while preserving observable behavior.

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。

Apply Vaughn Vernon-inspired lightweight DDD rules when using bounded contexts, subdomains, context maps, and tactical modeling without excess ceremony.

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。

Apply Eric Evans-inspired DDD rules when modeling a complex domain, defining bounded contexts, protecting ubiquitous language, or designing aggregates.

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。

Apply Robert C. Martin-inspired clean code rules when improving readability, naming, function design, responsibilities, or testable everyday code.

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。

Apply Martin Fowler-inspired enterprise architecture rules when choosing layers, service layers, transaction scripts, domain models, repositories, mappers, or DTOs.

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。

Apply Refactoring.Guru-inspired rules when diagnosing code smells, choosing refactoring techniques, and stopping cleanup before uncontrolled redesign.

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。

Apply Michael T. Nygard-inspired production reliability rules when designing failure handling, timeouts, retries, circuit breakers, observability, or deployment behavior.

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。

Apply Steve McConnell-inspired construction rules when implementing routines, variables, classes, control flow, defensive code, or tests.

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 Stockbee-style setup model book from momentum-burst screener candidates, then update 3-day and 5-day forward outcomes with MFE/MAE, stop-hit status, outcome tags, and cohort statistics. Use when the user wants to study Stockbee Momentum Burst examples, track failed candidates, build setup fluency, review A/B setup quality, or convert screener outputs into a learning loop rather than immediate trade signals.

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 and maintain a Stockbee-style daily 20% mover study for US equities by scanning +20%/-20% movers, classifying catalysts and setup context, updating forward outcomes, and summarizing cohort patterns. Use when the user asks to run a daily 20% study, backfill historical 20% movers, find recurring edge patterns, or build a model book of explosive market moves.

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。

Abstract detector tickets and hints into reusable edge concepts with thesis, invalidation signals, and strategy playbooks before strategy design/export.

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 when the user asks to "make everyone tell the same story", "write our elevator pitch ladder", or "build a spokesperson Q&A and approved boilerplate pack"; derives from the narrative-registry canon, message house, and brand voice a repeatable enablement kit — an elevator ladder (10-second / 30-second / 2-minute), a spokesperson Q&A (tough questions with on-canon answers), an approved boilerplate/bio pack (25 / 50 / 100-word), and a do/don't language sheet — so sales, support, founders, and partners repeat one consistent story. Not for the launch-day runbook — use launch-day-conductor; not for finished per-channel copy — use content-writer or each discipline creative builder; not for launch-window battle cards — use sales-enablement-kit; this kit never adjudicates a claim. 叙事赋能包/电梯梯度/发言人问答/审定样板/该说不该说

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 when platform-reported conversions disagree with GA4/ecommerce, when you suspect Meta and Google are double-counting the same sales, or for a standing (monthly) reconciliation workbook that de-dups stacked credit against an order-ID truth set, normalizes attribution windows and currency, compares attribution models, and reads incrementality from a geo/holdout test. Not for the point-in-time R2 veto or RQS gate — use ad-account-auditor; not for the ROI/ROAS ratio math itself — use roi-calculator; not for organic dark-social share attribution or GA4 direct-traffic decomposition — use dark-social-attributor. 付费广告归因对账/去重/增量

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。

Builds, runs, debugs, and operates applications on AWS Lambda MicroVMs — Firecracker-isolated, snapshot-resumable serverless compute environments running inside a container with up to 8 hr lifetimes. Applicable when workloads need strong isolation between tenants, isolated serverless compute, sandbox compute, or secure multi-tenant execution. Also suited for AI/agent code-execution sandboxes, interactive code playgrounds and notebooks (Jupyter, REPLs, dev environments running user-supplied code), reinforcement-learning environments, multi-tenant CI executors and build runners, sessionful game or simulation servers, or isolated security scanners. Also applicable when the workload needs long-lived sessions, a real port-listening server (gRPC, WebSocket, custom TCP protocols), state preserved across periods of inactivity (suspend/resume), container-level access (FUSE, eBPF, custom syscalls), or session-affine routing.

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 the Appllama MCP (mcp.appllama.io) well — research real top-grossing mobile apps, their screens, flows, and UI elements, then build from what you learn. Load when the Appllama MCP is connected and the task involves building a mobile app or screen, researching app design patterns, studying onboarding/paywall/feature flows, improving an existing screen, or whenever an appllama_* / search_apps / list_app_screens tool is available. Covers the tool map, pagination, expiring media, and the full build-from-research playbooks.

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。

Apollo.io incident response procedures. Use when handling Apollo outages, debugging production issues, or responding to integration failures. Trigger with phrases like "apollo incident", "apollo outage", "apollo down", "apollo production issue", "apollo emergency".

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。

Diagnose and manage an Algolia-backed search incident with evidence, containment, and reversible recovery. Use when users see failed, stale, slow, or irrelevant search results. Trigger with "Algolia incident", "search outage", or "Algolia degraded".

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, contain, recover, and review Adobe auth, product, async-job, event, storage, spend, or App Builder incidents with evidence. Use when the task requires adobe integration incident command. Trigger with "Adobe incident", "Firefly outage", or "Adobe credential leak".

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。

Verify skillshare on a real Windows guest running in local UTM: build a pinned commit in the devcontainer, push it into the VM, and either run the Windows E2E runbook with full and basic-user tokens or hand the maintainer a one-line hands-on setup. Use this whenever a change touches Windows links, junctions, file symlinks, Developer Mode, copy fallback, Windows paths, or the dashboard on Windows, or when the user asks to test or accept something on Windows / UTM.

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。

Rebuilds an app screen by screen from the recon map: app shell first, then the core flow as a vertical slice, then every screen with all its states, ticking off the feature matrix as it goes. Writes every line fresh, never the original's code, assets or copy. Use when the user says "build the clone", "build screen S07", "start building", "rebuild this screen", "implement the booking page", or after /replica-design.

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 AI-powered software with TypeSafe: small units of AI intelligence you can use like programming primitives. Its System One models, including Jev, turn natural language and application state into typed judgments and probabilities that code can combine. Use when a feature needs programmable common sense, when brainstorming what AI could make possible in an app, or when an LLM prompt-and-parse step could become a structured decision. Applications include routing, ranking, extraction, verification, and interactive experiences; these are starting points, not the limits. Read live docs and cookbooks to find useful patterns and discover new combinations.

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。