'hex' 검색 결과

Deploy AI models to embedded hardware using MathWorks tools (MATLAB, Simulink, Embedded Coder). Covers two workflow patterns: (1) MathWorks-native or 3P-imported models rebuilt as dlnetwork for lean hardware (Cortex-M, DSP), (2) direct C/C++ code generation from PyTorch and LiteRT models for high-performance hardware (Cortex-A, x86, GPU). Trigger when: user wants to deploy AI to embedded targets; generate C/CUDA from neural networks; compress AI models for MCU/DSP; integrate AI in Simulink for system-level simulation; import PyTorch/ONNX/TensorFlow models for embedded deployment; optimize AI for resource-constrained hardware; or use loadPyTorchExportedProgram, importNetworkFromPyTorch, dlquantizer, exportNetworkToSimulink, or Embedded Coder with AI models.

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Calculates the WCAG 2.x contrast ratio between two colors, grades it against the AA and AAA thresholds for body text, large text and non-text UI parts, and finds the closest shade that passes. Use when someone asks to "check the contrast", "is this text readable on this background", "does our palette meet WCAG AA", "fix the low-contrast text", "audit the design tokens for accessibility", or brings a failed contrast finding from an audit. Handles hex and rgb() values, alpha, whole palettes, light and dark themes and a CI gate.

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Architecture Patterns workflow skill. Use this skill when the user needs Master proven backend architecture patterns including Clean Architecture, Hexagonal Architecture, and Domain-Driven Design to build maintainable, testable, and scalable systems and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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Architecture Patterns workflow skill. Use this skill when the user needs Master proven backend architecture patterns including Clean Architecture, Hexagonal Architecture, and Domain-Driven Design to build maintainable, testable, and scalable systems and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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Architecture Patterns workflow skill. Use this skill when the user needs Master proven backend architecture patterns including Clean Architecture, Hexagonal Architecture, and Domain-Driven Design to build maintainable, testable, and scalable systems and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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Specialized visual and multimedia processing tools. Use this skill whenever a task involves complex visual content — UI mockups, dense screenshots, design images, charts, artwork — where precise details like spacing, hex colors, font sizes, and component hierarchy need to be extracted accurately. Also use for: reviewing or auditing existing UI against designs, comparing screenshots for visual regressions, transcribing audio/video, extracting data from PDFs with complex layouts, and generating images. Trigger whenever the user wants to implement from a design, review or compare UI screenshots, analyze visual details precisely, describe artwork or aesthetic content, or process any media file (audio, video, PDF).

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Use when adding a use case, port, repository or adapter in a Go service — where each piece goes, how errors and transactions cross layers, and how to check the import direction

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Use when starting a new Spring Boot 4 project — scaffolding a service, REST API, or modular backend; picking an architecture (layered, package-by-module, modular-monolith, tomato, DDD-hexagonal); selecting Spring Boot 4 features; or applying the bundled templates and references in this skill. Not for migrating existing projects or for isolated JPA/repository work without broader project-creation context.

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Encode and decode raw Hyperlane messages to/from packed hex bytes using the CLI. Use when you need to construct a message for testing, inspect a raw message from logs or a transaction, or decode a warp transfer body.

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Audit existing color usage in a codebase — find inconsistencies, hardcoded values, and contrast failures. Use when asked to "audit our color usage", "find hardcoded hex values", or "check for contrast failures".

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Software design principles, patterns, and architecture from SOLID through distributed systems. Covers the five SOLID principles with violations and fixes, DRY/KISS/YAGNI heuristics, separation of concerns, 12 GoF design patterns organized by intent (creational, structural, behavioral), architectural patterns (MVC, MVP, MVVM, layered, hexagonal, microservices, event-driven), coupling and cohesion metrics, dependency injection, and the design decision framework for choosing between competing approaches. Use when making design decisions, reviewing architecture, refactoring code, or teaching software engineering principles.

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Use this skill whenever the user wants to analyze binaries with radare2/r2 from the command line, including reverse engineering, disassembly, function analysis, strings/import inspection, patching, binary diffing, hex inspection, or r2 scripting. Also use it when the user mentions PE/ELF/Mach-O/DEX/WASM files together with CLI analysis, `rabin2`, `rasm2`, `radiff2`, `r2pipe`, or asks for radare2 command help on Windows/Linux/macOS.

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Analyzes a Stitch project's screens and synthesizes a natural-language DESIGN.md — visual atmosphere, color palette with hex values, typography rules, and Stitch-ready prompt snippets. Use this before stitch-loop or any multi-page build to establish design consistency.

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Generate jigsaw puzzles via generate_puzzle() or geom_puzzle_*() with parameter validation against inst/config.yml. Supports rectangular, hexagonal, concentric, voronoi, and snic puzzle types with configurable grid, size, seed, offset, and layout parameters. Use when creating puzzle SVG files for a specific type and configuration, testing generation with different parameters, generating sample output for documentation or demos, or creating ggplot2 puzzle visualizations.

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Epistemic transparency — acknowledging uncertainty, flagging limitations, avoiding overconfidence, and communicating what is known, unknown, and uncertain with proportional confidence. Maps the HEXACO personality dimension to AI reasoning: truthful calibration of confidence, proactive disclosure of gaps, and resistance to the temptation to appear more certain than warranted. Use before presenting a conclusion, when answering questions where knowledge is partial or inferred, after noticing a temptation to state uncertain information as certain, or when a user is making decisions based on provided information.

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Identify insects using body plan analysis, dichotomous keys to order, wing venation, mouthpart type, antennae form, leg and tarsal structure, and confidence levels. Covers the fundamental hexapod body plan verification, a simplified dichotomous key to major orders, wing venation and type analysis, mouthpart classification, antennae morphology, leg specialization and tarsal formula, and a structured confidence assessment framework. Use when you need to identify an unknown insect beyond preliminary order placement, are working through a specimen for taxonomic study, want to distinguish between similar orders or families, or need to assign a confidence level to a field identification.

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Backend conventions and patterns. Use when working on API routes, dependency injection, or hexagonal architecture in the backend.

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Clean/hexagonal architecture: dependency rules, ports and adapters, layering, and testable boundaries. Use when structuring applications for maintainability and testability.

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Server-Side Request Forgery (SSRF) occurs when user-controlled input is used to construct URLs that the server fetches, enabling access to internal services, cloud metadata endpoints (169.254.169.254), and local files via `file://` scheme. Detect via parameters accepting URLs or hostnames, PDF/report generators rendering `<iframe>/<img>/<script>`, and blind SSRF via out-of-band DNS callbacks. Bypass filters using IP decimal/octal/hex encoding, URL-userinfo tricks, and URL fragments. Tools: Burp Collaborator, curl.

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Analyzes position-independent shellcode: disassembling raw bytes at the right architecture, recognizing PEB-walk API resolution and egg hunters, and emulating execution to recover behavior and payloads. Activates for requests to analyze shellcode, disassemble raw position-independent code, or emulate a shellcode blob.

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Uses YARA to classify and triage samples at scale: applying rule sets, reading matches and string offsets, tuning for false positives, and organizing rules for malware family identification. Activates for requests to scan files with YARA, apply YARA rules, or classify samples by signature.

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Expands one indicator into the full scope of an incident: pivoting across data sources to find related hosts, accounts, and infrastructure, building a timeline, and bounding what is and is not affected. Activates for requests to scope an incident, pivot from a single IOC, or determine the blast radius of a detection.

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Structures a clear, actionable malware analysis report covering summary, sample identity, capabilities, IOCs, ATT&CK mapping, and detection guidance for both technical and decision-making audiences. Activates for requests to write, structure, or review a malware analysis or reverse-engineering report.

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Converts hunt findings into portable Sigma detection rules: choosing the right logsource, expressing robust selection/filter logic, setting level and ATT&CK tags, and validating before converting to a SIEM query. Activates for requests to write a Sigma rule, convert a hunt to a detection, or create portable SIEM detection logic.

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