「engineering」の検索結果

Perform static analysis of malicious PDF documents using peepdf, pdfid, and pdf-parser to extract embedded JavaScript, shellcode, and suspicious objects.

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 or modify production UI components, layouts, client state, responsive behavior, accessibility, or UX inside an app when that frontend surface is the primary deliverable; do not trigger for rich campaign/media pages, backend integration, every user-facing text change, delegated browser QA, or review alone.

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Configure or diagnose project context, rules files, context packs, or documented context degradation when explicitly requested; do not trigger automatically at session start, task switching, ordinary repository exploration, implementation, or completion.

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。

Creates reusable prompt templates with strict output contracts, style rules, few-shot examples, and do/don't guidelines. Provides system/user prompt files, variable placeholders, output formatting instructions, and quality criteria. Use when building "prompt templates", "LLM prompts", "AI system prompts", or "prompt engineering".

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。

Reviews animation and motion code against a high craft bar derived from Emil Kowalski's design engineering philosophy. Default to flagging; approval is earned.

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 Spark engineering knowledge must be placed, deduplicated, migrated, or validated across AGENTS, Notes, Roles, Skills, and Workflows.

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。

Phishing campaign execution methodology for authorized red team engagements. Covers end-to-end campaign lifecycle: infrastructure provisioning (GoPhish, SMTP relay configuration, domain acquisition and aging, SPF/DKIM/DMARC alignment), payload delivery vectors (Office macro weaponization, HTA droppers, ISO/IMG container abuse, LNK shortcut hijacking, OneNote embedded payloads, HTML smuggling), email authentication bypass techniques (SPF softfail exploitation, DKIM replay attacks, display name spoofing, homoglyph and cousin domain registration), credential harvesting with MFA bypass (EvilGinx2 transparent proxy, Modlishka session relay, pixel-perfect HTML cloning), spear phishing pretext development informed by OSINT, email security gateway evasion, QR code phishing (quishing), and callback phishing for initial access. Integrates with GoPhish for campaign management, EvilGinx2 for adversary-in-the-middle credential interception, King Phisher for template design, and the Social Engineering Toolkit for paylo...

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Comprehensive offensive methodology for software supply chain attacks covering the full kill chain from reconnaissance through exploitation. Addresses dependency confusion across npm, PyPI, and NuGet ecosystems where internal registry override allows an attacker to inject malicious packages that shadow private dependencies. Covers typosquatting techniques for popular packages, compromised package injection via maintainer account takeover or social engineering, and build system attacks through Makefile injection, setup.py install hooks, and npm postinstall scripts. Extends into CI/CD artifact tampering where build outputs are replaced or modified in transit, code signing abuse through stolen or self-signed certificates, upstream repository compromise via commit injection or force-push to trusted repos, and container image supply chain attacks including base image trojaning and registry confusion. Maps to MITRE ATT&CK T1195.001 (Supply Chain Compromise: Compromise Software Dependencies and Development Tools...

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。

A comprehensive skill for using the Cursor CLI agent for various software engineering tasks (updated for 2026 features, includes tmux automation guide).

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。

Strategies for managing LLM context windows including summarization, trimming, routing, and avoiding context rot Use when: context window, token limit, context management, context engineering, long context.

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。

Pragmatic coding standards - concise, direct, no over-engineering, no unnecessary comments

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。

ClickHouse database patterns, query optimization, analytics, and data engineering best practices for high-performance analytical workloads.

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。

Every product will be AI-powered. The question is whether you'll build it right or ship a demo that falls apart in production. This skill covers LLM integration patterns, RAG architecture, prompt engineering that scales, AI UX that users trust, and cost optimization that doesn't bankrupt you. Use when: keywords, file_patterns, code_patterns.

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。

Expert in building products that wrap AI APIs (OpenAI, Anthropic, etc.) into focused tools people will pay for. Not just 'ChatGPT but different' - products that solve specific problems with AI. Covers prompt engineering for products, cost management, rate limiting, and building defensible AI businesses. Use when: AI wrapper, GPT product, AI tool, wrap AI, AI SaaS.

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Make your AI agent learn and improve automatically. Reviews sessions, extracts learnings, updates memory files, and compounds knowledge over time. Set up nightly review loops that make your agent smarter every day.

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。

Complete DevOps & Platform Engineering system. CI/CD pipelines, infrastructure as code, container orchestration, observability, incident response, and SRE practices — all platforms, all clouds.

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Design, build, deploy, and operate production AI agent systems — single agents, multi-agent teams, and autonomous swarms. Complete methodology from agent architecture through orchestration, memory systems, safety guardrails, and operational excellence.

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。

React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.

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Use when running analytical SQL over Parquet/CSV/JSON without a warehouse, replacing pandas for data wrangling, joining S3 data in-place, building local data marts, or embedding OLAP into an app. Triggers: read_parquet/read_csv setup, partitioned dataset queries, hive partitioning, glob patterns for S3, COPY TO export, attach Postgres/MySQL, UDFs in Python/R, MotherDuck cloud sync, columnar performance vs row stores. NOT for OLTP workloads (concurrent writes), distributed analytics at petabyte scale (use Spark/Trino), or vector search (use pgvector/Lance).

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。

Prepare high-quality datasets for LLM fine-tuning with filtering, deduplication, augmentation, and RLHF data formatting. Activate on: fine-tuning data, training data curation, RLHF dataset, data quality filtering, SFT dataset. NOT for: model training infrastructure (ai-engineer), prompt engineering without fine-tuning (prompt-engineer).

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。

Cognitive Systems Engineering design methodology for joint cognitive systems and human-centered automation

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。

Review of Cognitive Systems Engineering applications and current practice in safety-critical domains

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Design decisions for when and what sounds to use in web apps, mobile apps, and desktop native apps — plus how to create them with AI. Covers UI sound vocabulary (confirms, alerts, transitions, errors), platform conventions (iOS, macOS, Material), creating sounds with AI tools (ElevenLabs, AudioCraft, Stable Audio), Web Audio API for procedural sounds, haptic-audio pairing, and the psychology of satisfying vs. annoying UI sounds. Activate on 'app sounds', 'UI sounds', 'notification sound', 'sound design app', 'earcons', 'sonic branding', 'interaction sounds', 'feedback sounds', 'app audio', 'satisfying sounds'. NOT for professional audio engineering (use sound-engineer), Windows 3.1 retro sounds specifically (use win31-audio-design), music composition (use sound-engineer).

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。

Expert in 3D computer vision labeling tools, workflows, and AI-assisted annotation for LiDAR, point clouds, and sensor fusion. Covers SAM4D/Point-SAM, human-in-the-loop architectures, and vertical-specific training strategies. Activate on '3D labeling', 'point cloud annotation', 'LiDAR labeling', 'SAM 3D', 'SAM4D', 'sensor fusion annotation', '3D bounding box', 'semantic segmentation point cloud'. NOT for 2D image labeling (use clip-aware-embeddings), general ML training (use ml-engineer), video annotation without 3D (use computer-vision-pipeline), or VLM prompt engineering (use prompt-engineer).

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。