「embedded」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. Use when triaging a suspicious PDF attachment from a phishing email, analyzing a PDF-based exploit document, or building detection signatures for weaponized PDF threats.

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Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.

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Zoom Meeting SDK Web - Component View. Embeddable Zoom meeting components with Promise-based API for flexible integration. Ideal for React/Vue/Angular apps and custom layouts. Uses ZoomMtgEmbedded with async/await patterns and embeddable UI containers.

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Rapidly triage an incoming NDA and classify it as GREEN (standard approval), YELLOW (counsel review), or RED (full legal review). Use when a new NDA arrives from sales or business development, when screening for embedded non-solicits, non-competes, or missing carveouts, or when deciding whether an NDA can be signed under standard delegation.

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Use when creating a new builtin skill for Crush, editing an existing builtin skill (internal/skills/builtin/), or when the user needs to understand how the embedded skill system works.

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Use when creating a new shell builtin command for Crush (internal/shell/), editing an existing one, or when the user needs to understand how commands are intercepted in Crush's embedded shell.

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The durable documentation set that makes an AI-built (vibe-coded) app reviewable before shipping. A small core every app needs — architecture, user/permission flows, permissions, variables/secrets, and a test-coverage map — plus conditional docs added only when they apply: emails, scheduled work, SEO, and embedded agents/automation. Defines what each doc must capture and how a reviewer or auditor uses it. Use when documenting a codebase for handoff, mapping user journeys and trust-boundary crossings, planning test coverage, or preparing for a security or performance audit.

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SQLite expert for WAL mode, query optimization, embedded patterns, and advanced features

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Turn an exploration (threads, findings, charts) into a single Markdown report — note, blog post, executive summary, KPI dashboard, slide brief, or multi-section analytical report, with embedded charts.

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Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.

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Use when an agent needs to build or operate the embedded KrillinAI CLI, choose a supported command, or interpret its JSON, manifest, subtitle, dubbing, render, cover, speech, and voice outputs.

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Use when developing firmware for microcontrollers, implementing RTOS applications, or optimizing power consumption. Invoke for STM32, ESP32, FreeRTOS, bare-metal, power optimization, real-time systems, configure peripherals, write interrupt handlers, implement DMA transfers, debug timing issues.

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Use when validating or debugging a workflow in the embedded browser and you need a reproducible, evidence-first loop.

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Use when working in the embedded browser and you want the cheapest reliable interaction loop.

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Use run_shell with embedded Python heredoc as reliable fallback when code execution tools fail

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Fallback to run_shell with embedded Python when execute_code_sandbox fails due to e2b unavailability

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Generate professional documents using write_file when primary data sources fail, leveraging embedded domain knowledge instead of external retrieval

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Handle cascading data retrieval tool failures by falling back to embedded knowledge generation

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Practical offensive fuzzing methodology covering target identification, fuzzer selection (AFL++, libFuzzer, Honggfuzz, Boofuzz, syzkaller), harness writing, corpus curation, mutation strategies, coverage measurement, and crash triage. Use when setting up or running fuzz campaigns against any target: file parsers, network protocols, kernel drivers, EDR engines, embedded firmware, or language runtimes.

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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 payload generation. Maps to MITRE ATT&CK T1566 (Phishing) and sub-techniques T1566.001 (Spearphishing Attachment), T1566.002 (Spearphishing Link), T1566.003 (Spearphishing via Service). Assumes you have written authorization and a defined scope before any campaign execution.

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Route single-cell or single-nucleus RNA-seq FASTQs to public count-generation workflows and defer post-count matrix QC, annotation, clustering, and UMAP analysis to the embedded scrna-seq-qc skill.

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Runs a sequenced monolith-to-modular pipeline that sizes and inventories components, finds shared domain duplication, addresses flattening and hierarchy issues, analyzes coupling, then groups components into candidate domain-aligned units, with optional embedded DDD strategic analysis for bounded contexts. Use when asking how to split a monolith, size components before extraction, find duplicated domain logic, clean up module hierarchy, measure coupling between modules, or group components into services. Do NOT use for phased extraction roadmaps or prioritization without the prior analysis steps (use decomposition-planning-roadmap after this pipeline), end-to-end legacy migration strategy writeups (use legacy-migration-planner), pure infrastructure capacity sizing, or when you only need DDD without the structural pipeline (install domain-analysis standalone).

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Detect hardcoded secrets in code or configuration accessible to the target agent. Focuses on secrets embedded in source, configs, or IaC, not runtime leaks.

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Use when building wallet infrastructure, authentication systems, or financial applications. Reach for Privy when you need to create embedded wallets, manage user authentication, control wallet permissions with policies, execute transactions across blockchains, or build treasury/agent wallets with programmable controls.

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。