「engine」の検索結果

Expert-level geotechnical engineering covering soil classification, shear strength, consolidation, foundation design, slope stability, and retaining structures.

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Expert-level thermodynamics knowledge. Use when working with heat, temperature, entropy, thermodynamic laws, heat engines, refrigerators, phase transitions, statistical mechanics, or thermodynamic cycles. Also use when the user mentions 'entropy', 'enthalpy', 'Carnot', 'heat engine', 'thermodynamic cycle', 'ideal gas', 'phase transition', 'Gibbs free energy', 'specific heat', 'thermal equilibrium', or 'second law'.

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Reconstruct a free-form prompt into actionable AC + assumptions + confidence band before the engine plans — '/work "…"', 'baue X', 'ist der Prompt klar genug für die Engine?'.

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Configure this repo for the engineering skills — set up its issue tracker, triage label vocabulary, and domain doc layout. Run once before first use of the other engineering skills.

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QA engineer for test automation and coverage. Use when the user asks to talk to Quinn or requests the QA engineer.

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[Documentation] Use when scanning the codebase to (re)generate ONE project-reference doc. Parameterized by `--target=<key>`: project-structure | backend-patterns | frontend-patterns | scss-styling | design-system | code-review-rules | domain-entities | feature-spec | docs-index | e2e-tests | integration-tests | seed-test-data | ui-system. Shared 4-phase scan engine; per-target detail lives in references/targets.md. `ui-system` is an orchestrator meta-target (`kind: orchestrator`) that runs the 3 UI child scans in parallel instead of the 4-phase engine.

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Use when reverse engineering binaries, firmware, mobile apps, JavaScript/Electron apps, .NET assemblies, network protocols, or malware samples; when decompiling, deobfuscating, unpacking, debugging, or hooking shipped artifacts; when solving CTF challenges (pwn, crypto, forensics, web); when a conclusion needs evidence from a shipped artifact rather than available source; or when routing authorized pentest and security-analysis work. Do not use for ordinary review of source code you already have.

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Construa sistemas de ML em produção com PyTorch 2.x, TensorFlow e frameworks modernos de ML. Implementa model serving, feature engineering, A/B testing e monitoramento.

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。

Configure this repo for the engineering skills — set up its issue tracker, triage label vocabulary, and domain doc layout. Run once before first use of the other engineering skills.

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。

Design phishing and social engineering simulations with explicit authorization and measurable outcomes.

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Use when working with Slang shaders, shader modules, HLSL-compatible GPU code, graphics pipelines, compute shaders, tessellation, ray tracing, parameter blocks, generics, interfaces, capabilities, cross-compilation, shader optimization, shader review, or C++ engine integration for Slang. Trigger on any mention of Slang, .slang files, slangc, SPIR-V from Slang, Slang modules, [shader("compute")], [shader("vertex")], or requests to write/review/refactor shader code with modern language features. Also trigger for Slang-to-HLSL/GLSL/Metal/CUDA cross-compile questions, or when the user says "shader" alongside "generics", "interfaces", "parameter blocks", "autodiff", or "capabilities".

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Product-discovery coach for Fishbowl engineers — inspired by Marty Cagan's work (never impersonates him). Stage-routes between opportunity brief, research, PRD, ticket drafting, and outcome review. Triggers on "product coach", "help me with product", "I need to write a PRD", "I want to do discovery", "opportunity assessment", "who is this for", "why are we building this", or when an engineer is starting, resuming, or post-mortem-ing product-adjacent work. Invoke with a short description of what you want to do, or invoke bare to be surveyed and routed.

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。

QA Engineer role in AID methodology. Use for test strategy, BDD scenarios, bug reporting, acceptance testing, flaky test prevention.

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。

TJS2 (吉里吉里Z 内蔵スクリプト言語) の言語仕様と組み込みクラスのリファレンス。.tjs ファイル / *.ks (KAG) 内の埋め込みスクリプト / TJS2 コード断片 を扱う、書く、レビューする、デバッグするときに使う。JavaScript / TypeScript に似ているが文法と意味論が違うので、JS の感覚で書くと壊れる場合が多い。組み込みクラス (Array / Dictionary / Date / Math / RegExp / Exception) の API もここに集約。**呼び出されたら必ず「JS との主な違い」セクションを最初に確認し、その上で必要な詳細リファレンスを Read で取りに行くこと。** 吉里吉里Z 本体のクラス API (Window / Layer / System / Storages / Bitmap 等) や engine 内部はこのスキルの対象外。

Master Anthropic's prompt engineering techniques to generate new prompts or improve existing ones using best practices for Claude AI models.

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You know AI wrappers get a bad rap, but the good ones solve real problems. You build products where AI is the engine, not the gimmick. You understand prompt engineering is product development. You balance costs with user experience. You create AI products people actually pay for and use daily.

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Engineering operating model for teams where AI agents generate a large share of implementation output.

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Spearphishing simulation is a targeted social engineering attack vector used by red teams to gain initial access. Unlike broad phishing campaigns, spearphishing uses OSINT-derived intelligence to craf

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Extract and catalog attack patterns from cyber threat intelligence reports into a structured STIX-based library mapped to MITRE ATT&CK for detection engineering and threat-informed defense.

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Reverse engineer Go-compiled malware using Ghidra with specialized scripts for function recovery, string extraction, and type reconstruction in stripped Go binaries.

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Analyzes malicious Linux ELF (Executable and Linkable Format) binaries including botnets, cryptominers, ransomware, and rootkits targeting Linux servers, containers, and cloud infrastructure. Covers static analysis, dynamic tracing, and reverse engineering of x86_64 and ARM ELF samples. Activates for requests involving Linux malware analysis, ELF binary investigation, Linux server compromise assessment, or container malware analysis.

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Writes product-led content pages optimized for both search engines and AI engine citations. Produces markdown files with frontmatter, following page-type frameworks (comparison, guide, use case, learn, trust) with verified sources and no fabricated data.

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Creates data visualizations (charts, graphs, tables) optimized for AI engine parsing and citation. Produces inline SVG/HTML with text summaries, data tables, and JSON-LD so AI engines can quote the data.

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Use when designing, evaluating, documenting, modernizing, reviewing, refactoring, or implementing software architecture and code-level structure, including requirements, context, architecture style selection, microservices patterns, WebAssembly/WASI/Component Model, UMA portable capabilities, runtime contracts, capability graphs, governed MCP/agent orchestration, clean code, code smells, Rust crate/library API architecture, Cargo feature/MSRV/SemVer policy, unsafe/FFI/no_std/library testing, modularity, views, NFR tactics, IAM/auth, security patterns, cost estimation, architecture-as-code fitness functions, ADR/RFC work, and engineering execution plans.

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。