「deep」の検索結果

Modern Angular (v20+) expert with deep knowledge of Signals, Standalone Components, Zoneless applications, SSR/Hydration, and reactive 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。

Orchestrate IA shard authoring via design and deepen passes, with pipeline-state-driven shard selection and ambiguity gates

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。

Author and validate BE spec files, update indexes/tracker, run deepening and ambiguity gates, and enforce allowed next-step transitions

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。

Author FE spec files, update indexes/tracker, run deepening and ambiguity gates, and enforce navigation completeness before plan-phase handoff

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。

Validate shard load/dependencies, create deep-dive skeletons, update ledger assignments, and generate pipeline tracker

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。

Exhaustive idea extraction through recursive breadth-before-depth exploration with Deep Think protocol and fractal structure. Use during /ideate to transform raw ideas into comprehensive, structured ideation output. Writes to a fractal folder hierarchy — every node has an index, CX file, and children.

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。

Execute RED→GREEN→REFACTOR with validation, deep checks, and mandatory progress updates

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。

Deep code analysis covering security, performance, maintainability, and best practices

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 this skill when handling inbound URLs, custom schemes, app links, universal links, or URL-bearing notification payloads.

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。

Enables ultra-granular, line-by-line code analysis to build deep architectural context before vulnerability or bug finding.

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。

Multi-tiered memory management (HOT/WARM/COLD) for context compaction. Invoke ONLY for explicit compaction events: post-`/compact` cleanup, MEMORY.md tier promotion, archive batch, or "trim my context". NOT for general recall (use deep-recall) or routine memory writes (use storage-router). Triggers: "compact memory", "promote to durable", "archive old context", "tier this".

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。

Single-skill wrapper for the 4-layer recall (durable memory → semantic DB → PostgreSQL → daily notes). Use BEFORE saying "I don't have context". Triggers: "search memory", "what do we know about X", "deep recall", "I don't remember".

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。

4-layer recall procedure (durable → semantic → PostgreSQL → daily notes) before claiming "no context". Invoke when user explicitly asks "do you remember", "what did we discuss about X", "search history". NOT a general assistant — only fires when recall is the explicit task. Drop the prior "use proactively" rule. Triggers: "do you remember", "what did we discuss", "when did", "find that conversation about".

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 working with Aws Iam — aWS IAM deep analysis for policy evaluation, role trust relationship review, access advisor data, credential reports, permission boundary inspection, and identity-based vs resource-based policy analysis. Covers IAM users, roles, groups, policies, SCPs, and cross-account access patterns. Read this skill before any IAM operations — it enforces discovery-first patterns and strict read-only safety rules.

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。

Sep 24, 2025 Deep Dive into Context Engineering for Agents Sep 18, 2025 Architectures for Multi-Agen

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。

Training Details of the Second RL Stage 4 Experiment 5 Ethics and Safety Statement 6 Conclusion, Limitation, and Future Work 7 Author List A Backgroun

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。

Orchestrator for the full academic research pipeline: research -> write -> integrity check -> review -> revise -> re-review -> re-revise -> final integrity check -> finalize. Coordinates deep-research, academic-paper, and academic-paper-reviewer into a seamless 10-stage workflow with mandatory, coverage-bounded integrity checks, two-stage peer review, and auditable quality-assurance artifacts. Triggers on: academic pipeline, research to paper, full paper workflow, paper pipeline, end-to-end paper, research-to-publication, complete paper workflow, 연구부터 논문까지, 연구 주제 설정부터 논문 완성까지, 논문 전체 워크플로.

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。

Scan a codebase for deepening opportunities, present them as a visual HTML report, then guide a user-directed decision loop for the selected candidate.

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。

Pick the right serving container for a SageMaker model deployment and find its current image URI. Use this skill whenever about to deploy a model to a SageMaker endpoint and an image URI needs to be chosen — including when the user says "deploy this LLM", "host this HuggingFace model", "serve this fine-tuned model", "deploy this embedding model", "host a reranker", "serve a sentence-transformers model", or when about to hardcode any container URI in deployment code. HuggingFace-curated Deep Learning Containers are ALWAYS preferred: HuggingFace vLLM (LLMs and generative rerankers), HuggingFace vLLM-Omni (multimodal), TEI (embeddings/cross-encoder rerankers), HF Inference Toolkit (other transformers). Generic images (AWS vLLM, DJL-LMI, SGLang) are used only when no HuggingFace image is compatible — never merely because they carry a newer version. Never hardcode a container URI from memory and never default to TGI. Prevents stale-image failures and wrong-region URIs.

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。

Detects and removes infostealer malware (credential stealers, data exfiltrators) via full-system file search, cryptographic hashing, and public threat-intelligence cross-checks (VirusTotal, MalwareBazaar). Primary method is always custom hash-based detection. Windows Defender (or any platform-native AV) is allowed **only when necessary** (e.g. inconclusive hashes or deep remediation) and **must never be the default option**. The agent must exhaust the custom workflow first. Works on Windows/macOS/Linux.

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。

Drive the Codex in-app browser (open, navigate, click, type, screenshot, read page state) through the app's own node_repl runtime. Use when the session uses a custom (non-OpenAI) model, for example deepseek-v4-flash or mimo-v2.5, and the user asks to use the in-app browser, open or navigate a page in it, test a local app in a browser, or click, type, or take a screenshot in the Codex browser panel.

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, list, read, message, wait on, fork, rename, archive, and pin Codex threads (sidebar tasks), plus automations and app navigation, using the app-native codex_app tools. Use when the session uses a custom (non-OpenAI) model, for example deepseek-v4-flash or mimo-v2.5, and the user asks to create a thread or a new task or agent, list or read threads, send a message to a thread, wait for a thread, fork or rename a thread, archive or pin a thread, set up an automation or reminder, or open something in the Codex app.

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。

Orientation for custom (non-OpenAI) models running in the Codex app through the codex-router proxy. Explains that the app's native tools arrive as flattened codex_app__ and mcp__ names, that the router restores them so the app executes them, which companion skills to read before threads, browser, or computer-use work, and that a turn with no tool call ends the task. Use when the session uses a custom (non-OpenAI) model, for example deepseek-v4-flash or mimo-v2.5, when codex_app__ or mcp__ tool names appear in the tool list, when a tool result just arrived and more work remains, or when thread, browser, or computer-use work is requested.

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。

Control local apps through Computer Use (the @oai/sky runtime) inside the Codex app. Use when the session uses a custom (non-OpenAI) model, for example deepseek-v4-flash or mimo-v2.5, and the user asks to control the computer, operate a desktop app's UI, use Safari or Chrome through computer use, click or type in an app, or take a screenshot of an app. Prefer purpose-built connectors, APIs, or CLIs when they exist.

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。