「combine」の検索結果

Detects and prevents code injection attacks targeting serverless functions (AWS Lambda, Azure Functions, Google Cloud Functions) through event source poisoning, malicious layer injection, runtime command execution, and IAM privilege escalation via function modification. The analyst combines static analysis of function code, CloudTrail event correlation, runtime behavior monitoring, and IAM policy auditing to identify injection vectors across the expanded serverless attack surface including API Gateway, S3, SQS, DynamoDB Streams, and CloudWatch event triggers. Activates for requests involving Lambda security assessment, serverless injection detection, function event poisoning analysis, or serverless privilege escalation investigation.

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Build a structured SOC escalation matrix defining severity tiers, response SLAs, tiered escalation paths, and notification procedures for security incidents, using context-driven criteria that combine business risk, asset criticality, and data sensitivity. Use when designing or revising how a SOC triages and escalates incidents across analyst tiers.

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。

Pre-deployment security audit of Solidity smart contracts in a Foundry project. Combines static analysis (Slither, Aderyn), symbolic execution (Mythril), and property-based testing (forge fuzz + invariant tests with handlers) to catch reentrancy, access-control, oracle/price manipulation, and arithmetic bugs BEFORE deploying to an EVM chain. Also enforces key hygiene (no plaintext private keys, encrypted cast keystore) and a secure deploy workflow. Use when writing, reviewing, testing, or deploying Solidity/Foundry contracts, building a dApp, or working with forge/cast/anvil, MetaMask, or Web3/DeFi code.

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。

Large Language and Vision Assistant. Enables visual instruction tuning and image-based conversations. Combines CLIP vision encoder with Vicuna/LLaMA language models. Supports multi-turn image chat, visual question answering, and instruction following. Use for vision-language chatbots or image understanding tasks. Best for conversational image analysis.

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。

Merge multiple fine-tuned models using mergekit to combine capabilities without retraining. Use when creating specialized models by blending domain-specific expertise (math + coding + chat), improving performance beyond single models, or experimenting rapidly with model variants. Covers SLERP, TIES-Merging, DARE, Task Arithmetic, linear merging, and production deployment strategies.

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Combines search results from multiple sources into coherent, deduplicated answers with source attribution. Handles confidence scoring based on freshness and authority, and summarizes large result sets effectively.

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, scaffold, refactor, and troubleshoot ChatGPT Apps SDK applications that combine an MCP server and widget UI. Use when Codex needs to design tools, register UI resources, wire the MCP Apps bridge or ChatGPT compatibility APIs, apply Apps SDK metadata or CSP or domain settings, or produce a docs-aligned project scaffold. Prefer a docs-first workflow by invoking the openai-docs skill or OpenAI developer docs MCP tools before generating code.

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。

Discovers requirements and generates architectural, design, and deployment guidance for dynamic hybrid search systems by combining semantic search and keyword search. Optimized for AlloyDB hybrid search use cases in Google Cloud. Use when users need vector search combined with structured SQL filtering, faceted attributes, semantic reranking, in-database AI validation, or serverless hosting across transactional relational databases, analytical data warehouses, or managed database engines. DON'T use this skill for simple keyword-only search, or when a standalone non-relational vector database is required.

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。

Scoring rules for daily churn-risk detection — usage decline, support friction, billing/payment risk, and renewal proximity — plus how to combine them into a ranked at-risk list with a reason and a suggested next step per account.

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。

Pre-deployment security audit of Solidity smart contracts in a Foundry project. Combines static analysis (Slither, Aderyn), symbolic execution (Mythril), and property-based testing (forge fuzz + invariant tests with handlers) to catch reentrancy, access-control, oracle/price manipulation, and arithmetic bugs BEFORE deploying to an EVM chain. Also enforces key hygiene (no plaintext private keys, encrypted cast keystore) and a secure deploy workflow. Use when writing, reviewing, testing, or deploying Solidity/Foundry contracts, building a dApp, or working with forge/cast/anvil, MetaMask, or Web3/DeFi code.

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。

Large Language and Vision Assistant. Enables visual instruction tuning and image-based conversations. Combines CLIP vision encoder with Vicuna/LLaMA language models. Supports multi-turn image chat, visual question answering, and instruction following. Use for vision-language chatbots or image understanding tasks. Best for conversational image analysis.

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。

Merge multiple fine-tuned models using mergekit to combine capabilities without retraining. Use when creating specialized models by blending domain-specific expertise (math + coding + chat), improving performance beyond single models, or experimenting rapidly with model variants. Covers SLERP, TIES-Merging, DARE, Task Arithmetic, linear merging, and production deployment strategies.

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。

Launch, restart, or troubleshoot the real AO Electron desktop app from this repository; run a checkout against isolated or real local AO data; combine PR branches for local UI review; and diagnose stale Electron processes, port conflicts, or preload bridge mismatches. Use whenever asked to run, open, show, or visually verify AO frontend changes in the actual desktop app rather than ao preview, dev:web, or mock 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。

Builds iOS/macOS/watchOS/tvOS applications, implements SwiftUI views and state management, designs protocol-oriented architectures, handles async/await concurrency, implements actors for thread safety, and debugs Swift-specific issues. Use when building iOS/macOS applications with Swift 5.9+, SwiftUI, or async/await concurrency. Invoke for protocol-oriented programming, SwiftUI state management, actors, server-side Swift, UIKit integration, Combine, or Vapor.

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。

Read, write, merge, split, rotate, watermark, encrypt, and OCR PDF files using Python (pypdf, pdfplumber, reportlab, pypdfium2) and command-line tools (poppler-utils, qpdf). Use when the user asks to extract text/tables/images from a PDF, create or modify a PDF, combine or split PDFs, OCR a scanned PDF, password-protect a PDF, or render PDF pages as images.

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。

Required before calling image_generate. Create and edit images from a prompt — generate an image, make a picture / logo / illustration / icon / banner / poster / thumbnail / hero image / mockup / product shot / social graphic, or edit / restyle / combine existing images from reference images. Uses OpenAI gpt-image-2 through the Hive image service, billed to the user's Hive credits like an LLM call (no API key needed). Teaches the exact call shape, the quality/cost tradeoff (quality="low" is the default and cheapest), reference-image editing, how to show the result to the user with attach_file, and the failure modes (out of credits, model unavailable, moderation).

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Read before automating LinkedIn with browser_* tools. LinkedIn combines shadow DOM (#interop-outlet), strict Trusted Types CSP that silently drops innerHTML, Lexical composer, native beforeunload dialogs that hang the bridge, and aggressive spam filters — each has bitten us at least once. Verified flows for profile messaging, connection-request acceptance, feed composition, and search. Requires hive.browser-automation. Verified against logged-in production 2026-04-11.

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。

Combines all of the `better-*` skills into a single review across accessibility, layout, writing, typography, color and UI polish.

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。

Diagnose and resolve file access issues by verifying directory structure, reading multiple files in parallel batches, running combined shell property checks (head/wc-l) in the same iteration as directory listing, and validating TypeScript import patterns — all with built-in guidance to minimize total iteration count.

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。

Verify a file's first N lines and total line count in one shell call using head and wc -l with an echo separator, minimizing read operations and shell iterations.

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, scaffold, refactor, and troubleshoot ChatGPT Apps SDK applications that combine an MCP server and widget UI. Use when Codex needs to design tools, register UI resources, wire the MCP Apps bridge or ChatGPT compatibility APIs, apply Apps SDK metadata or CSP or domain settings, or produce a docs-aligned project scaffold. Prefer a docs-first workflow by invoking the openai-docs skill or OpenAI developer docs MCP tools before generating code.

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 cross-game mashups and total conversions, the "Minecraft inside Elden Ring" or "skateboarding in MW2" kind. Covers passthrough mods (two games running at once and exchanging state, rendering and collision), porting one game's content or mechanics into another, embedding a decompiled game as a library, and clean reimplementations that read the user's own game files. Use when the user wants to combine two games, bring an enemy, mechanic or world from one game into another, or rebuild a game's runtime, or reimplement the guest's rules headless inside a real host.

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Record and cut a short showcase video of a game mod, or a compilation of clips, ready to post on X/YouTube. Records the game window with only the game's own audio, picks moments from contact sheets, and edits with one-line animated titles, creator credits, transitions, music, watermark and fade-out via an EDL JSON. Use when the user wants a demo video, trailer, clip or montage of a mod, or wants to combine existing clips into a styled video.

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。

Create a gooey blob system using SVG filters where multiple shapes merge into a single fluid form. Use overlapping circles combined with a Gaussian blur and color matrix filter to produce a continuous, organic mass. The forms should visually fuse and separate based on proximity. Focus on filter-driven merging (blur + threshold effect), soft organic boundaries with no hard edges, multiple independent shapes behaving as one system, and smooth continuous motion that feels fluid and cohesive.

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。