「iab」の検索結果

Use when work is ambiguous or has multiple viable paths; compares options, assumptions, evidence, tradeoffs, and the best next action.

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。

Debug container agent issues. Use when things aren't working, container fails, authentication problems, or to understand how the container system works. Covers logs, environment variables, mounts, and common issues.

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。

Debug container agent issues. Use when things aren't working, container fails, authentication problems, or to understand how the container system works. Covers logs, environment variables, mounts, and common issues.

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。

Set up and configure WorkOS AuthKit authentication with Convex backend. Use when integrating AuthKit, configuring JWT providers, setting up environment variables, or implementing sign in and sign out flows with React and Vite. Supports Netlify deployment.

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。

Set up and configure WorkOS AuthKit authentication with Convex backend. Use when integrating AuthKit, configuring JWT providers, setting up environment variables, or implementing sign in and sign out flows with React and Vite. Supports Netlify deployment.

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。

Senior embedded software engineer specializing in firmware and driver development for ARM Cortex-M microcontrollers (Teensy, STM32, nRF52, SAMD). Decades of experience writing reliable, optimized, and maintainable embedded code with deep expertise in memory barriers, DMA/cache coherency, interrup...

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。

Optimize multi-agent systems with coordinated profiling, workload distribution, and cost-aware orchestration. Use when improving agent performance, throughput, or reliability.

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。

Turn any domain folder of skills into a bounded agentic loop: compile a goal into a verifiable task plan, execute tasks with the domain's own tools, verify every task with machine-run checks, retry with caps, escalate to a human when budgets exhaust.

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Analyzes UEFI bootkit persistence mechanisms including firmware implants in SPI flash, EFI System Partition (ESP) modifications, Secure Boot bypass techniques, and UEFI variable manipulation. Covers detection of known bootkit families (BlackLotus, LoJax, MosaicRegressor, MoonBounce, CosmicStrand), ESP partition forensic inspection, chipsec-based firmware integrity verification, and Secure Boot configuration auditing. Activates for requests involving UEFI malware analysis, firmware persistence investigation, boot chain integrity verification, or Secure Boot bypass detection.

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。

Redesign GitHub README homepages or create project-native pure SVG, hybrid SVG-composed PNG/WebP, and opt-in animated GIF assets. Use when a user asks to beautify, redesign, rebrand, visually upgrade, simplify, or audit a GitHub README; create only a hero, section headers, diagrams, badges, motion graphics, showcase modules, or other README assets; or turn a repository homepage into a cohesive visual story. If whole-README work versus asset-only work is unclear, ask which scope the user wants. For hero-like assets where pure SVG and generated raster material are both viable, explain the tradeoffs and confirm the implementation before creating the asset.

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 critiquing a system design across correctness, scalability, reliability, maintainability, and observability using probe-first questioning.

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。

Publish or refresh one or a few free-AI-credit offers from screenshots or text — official sources win, unverifiable offers dropped, validates, diffs, commits on approval. Don't use for catalog-wide re-checks, scraping, or bulk imports.

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。

대화형 System-wide Refactoring — 변경 파일에서 Extract Method·Domain Logic 이동·Pull Up 등 클래스 경계를 넘는 구조 변경 후보를 찾아 승인 후 기법별 커밋으로 적용. "구조 리팩토링", "도메인 로직 이동", "클래스 분리", "system-wide", "/system-wide-refactoring" 요청 시 사용. 단, 메서드 내부 정돈(guard clause·extract variable 등)은 /tdd-tidy가 적합. /system-wide-refactoring [commit-ref]로 호출.

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。

Refactor code by renaming variables, methods, classes, and concepts for clearer intent. Use when asked to improve names, clarify naming, perform a naming pass, or run `/refactor:names`.

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。

Compress review pressure into the right next action instead of more code by default. Classify findings, reject non-liabilities, and choose proof-only vs minimal-fix vs refactor-kernel, preventing one-patch-per-comment churn. Use after a code review — PR comments, reviewer suggestions, or audit findings — and especially when you are about to reflexively patch every comment.

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。

Refactor code to remove dead code, useless comments, unused variables, and unused imports. Use when asked to declutter a class, clean up noisy code, remove cruft, or run `/refactor:declutter`.

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。

Mine a codebase for breakthrough, evidence-backed opportunities — features, additions, refactors, simplifications, DX, UX, reliability, performance, or architecture cleanup. Researches project reality first, then forces two escalation gates (reject the merely-adequate idea; expand ambition 10x then collapse to the smallest proof-bearing artifact). Outputs ranked opportunities, an escalation ledger, and one plan seed. Does not implement or create tickets.

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。

Load for reliable post-commit messaging, domain/integration event delivery, transactional outbox tables, dispatch workers, retries, idempotent consumers, duplicate delivery, or dual-write failure; not for synchronous in-process events with no external side effect.

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。

Scientific debugging for bugs, flakes, failures, and performance regressions. Use when something is broken, throwing, failing, slow, nondeterministic, or when the user says diagnose/debug this. Builds a trusted repro loop, tests falsifiable hypotheses, installs a regression oracle, and routes durable findings back into ln-* planning.

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。

End-to-end data pipeline for empirical research: fetch economic data from APIs (FRED, World Bank, IMF, BLS, OECD, Yahoo Finance), clean and transform raw data, construct strategy-specific variables, and validate panel structure. Use when asked to fetch data, download data, clean data, merge datasets, prepare analysis-ready data.

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。

Econometrics skill for machine learning methods in causal inference. Activates when the user asks about: "causal forest", "generalized random forest", "GRF", "double machine learning", "DML", "debiased machine learning", "LASSO for variable selection", "post-LASSO", "heterogeneous treatment effects", "CATE", "conditional average treatment effect", "BLP analysis", "CLAN analysis", "causal tree", "honest estimation", "因果森林", "双重机器学习", "异质性处理效应", "条件平均处理效应", "LASSO变量选择", "机器学习因果推断", "去偏机器学习"

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Guidance on writing high-quality requirements: the requirement quality characteristics and the EARS (Easy Approach to Requirements Syntax) patterns — ubiquitous, event-driven, state-driven, optional-feature, unwanted-behaviour — for unambiguous, singular, verifiable requirements, plus defect/anti-pattern review and verification & traceability. Use when authoring or reviewing requirements. Original synthesis citing EARS (Mavin et al.) and NASA public-domain guidance; not a reproduction of ISO/IEC/IEEE 29148 or the EARS paper.

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。

Knowledge base from the NIST Framework for Cyber-Physical Systems (SP 1500-201/202/203, v1.0, 2017). Use for engineering and analyzing cyber-physical systems (CPS) and IoT: the CPS Framework's aspect × facet grid (nine aspects of concern examined through conceptualization, realization, and assurance facets), cross-property trustworthiness risk management (safety/security/privacy/reliability/resilience together), data interoperability (syntactical/semantic/contextual, canonical models, identifiers, provenance), the timing aspect (time-interval classes, time domains, time-aware networking) and secure/resilient time (GNSS jamming/spoofing, PTP/NTP attacks, holdover, the clock-error model and Allan/MTIE metrics), plus the use-case requirements method. Bridges IT and OT under a clock. Scope limits: a 2017 living/draft document, thin on post-2017 standards and on detailed control catalogs; it names but does not reproduce ISO/IEC/IEEE 42010 and 15288 process text or permission-licensed third-party figures.

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。

Knowledge base from the DoD Test & Evaluation Enterprise Guidebook (August 2022 edition 8.02) with DOT&E-facing enterprise emphasis. Use for T&E enterprise roles (DOT&E vs USD(R&E)), DT&E/OT&E/LFT&E, cybersecurity T&E, MOSA/automation/digital enablers, suitability/reliability growth, and TEMP/Strategy/STE planning. Distinct routing from dod-te-guidebook (same guidebook family, pathway-deep pack). Guidance only—defer to DoDI 5000.89 and Title 10 for binding requirements.

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。