「ngs」の検索結果

小红书热门笔记搜索工具，支持关键词搜索获取热门内容数据，基于数据评分排序推荐热门笔记，助力创作者发现热门趋势、获取创作灵感。仅在主Agent中执行,不派发给子Agent。

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检测文案、文件或网页中的小红书违禁词并加粗显示，提供违禁词替换建议和仅替换违禁词后的文案；当用户需要查询小红书平台违禁词、检查小红书笔记文案是否包含敏感词、或希望获得安全替换词和修改后文案时使用。

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。

AI小红书信息源 — 每日自动扫描小红书 AI 相关笔记，按互动量（点赞/分享/评论）筛选最火内容，智能聚类生成精美 HTML 日报，支持封面图、数据指标和每日订阅。当用户需要 AI 小红书日报、小红书AI热点、AI小红书内容、小红书信息源、xhs ai feed 时使用。

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。

小红书笔记优化助手工具，基于全网爆款数据对用户文案进行多维度评分并给出改进建议；当用户需要评估文案质量、获取优化建议时使用

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。

专为小红书内容创作打造的AI封面设计工具，基于全网每日持续收录的2000+条爆款笔记数据，深度抓取同赛道爆款封面的视觉元素，通过AI智能分析总结高转化视觉规律，生成贴合笔记内容、符合平台流量审美的可落地封面设计方案，省去繁琐设计流程，提升笔记封面点击率。

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。

基于官方违禁词库，覆盖公众号、小红书、抖音三大平台审核标准，支持文案、文件、图片、链接多形式输入，快速输出违禁词标记与上下文替换建议。

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。

文旅小红书信息源 — 搜索文旅相关小红书热门笔记，按点赞量筛选最火内容，生成精美 HTML 报告。当用户需要文旅小红书、旅游攻略、景区热度、文旅资讯、文旅爆款内容时使用。触发词：文旅小红书、文旅信息源、文旅资讯、旅游小红书、景区笔记、文旅爆款。

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。

中国互联网品牌感知监测统一工具。聚合全网热榜追踪（hub）、小红书/抖音/公众号近30天深度讨论研究（cn30）、豆包/Kimi/DeepSeek AI 搜索品牌可见度分析（geo）三个互补模块，帮助品牌方/市场运营/内容创作者从「话题热度—真实口碑—AI 回答」三层视角全面感知品牌。当用户需要研究中国社交媒体热点、分析品牌舆情、对比竞品口碑、分析品牌在 AI 搜索中的表现、做 GEO 优化、追踪全网热点时使用。触发词：品牌感知、舆情监测、社媒研究、热榜聚合、AI 搜索可见度、GEO 分析、跨平台舆情、热点追踪、品牌口碑、品牌竞品对比、社媒热榜。

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Diagnoses why your product team ships slowly and produces a velocity plan with a baseline, a bottleneck map, the top 3 interventions and a stop-doing list. Use it when you say 'we're too slow', 'how do we ship faster', 'projects keep slipping', 'too many meetings', 'approvals take forever', 'our releases are scary', 'velocity', 'cycle time', 'we hired more people and nothing got faster' or 'AI made coding fast but we still ship slowly'. Draws on 226 Lenny's Podcast guests including Farhan Thawar, Nicole Forsgren and Melissa Perri.

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。

SEO analysis and optimization specialist. Use when conducting technical SEO audits, optimizing meta tags, analyzing Core Web Vitals, or improving search engine rankings. Focuses on actionable recommendations.

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。

Analyzes meeting transcripts and recordings to uncover behavioral patterns, communication insights, and actionable feedback. Identifies when you avoid conflict, use filler words, dominate conversations, or miss opportunities to listen. Perfect for professionals seeking to improve their communication and leadership skills.

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Trigger for: finding federal opportunities; capture and bid screening; competitor or incumbent intelligence; recompete pipelines; teaming partner research; agency, customer, or market intelligence; federal labor-rate or pricing context; or refreshing prior GovCon research. Always begin with the complete workflow menu and obtain the user's selection before research, preflight, file generation, web search, or MCP calls. Produce sourced chat findings or an optional validated GovCon Growth Brief without making a bid decision from public data alone.

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。

Display Claude Router usage statistics and cost savings

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。

AI product photography with studio lighting, lifestyle shots, and packshot conventions. Covers angles, backgrounds, shadow types, hero shots, and e-commerce image requirements. Use for: product photos, e-commerce images, Amazon listings, packshots, lifestyle photography. Triggers: product photography, product photo, packshot, e-commerce photography, product shot, product image, studio photography, lifestyle product, amazon product photo, product listing image, hero shot, product mockup, commercial photography

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。

Display knowledge base status and recent learnings

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Toolkit for styling artifacts with a theme. These artifacts can be slides, docs, reportings, HTML landing pages, etc. There are 10 pre-set themes with colors/fonts that you can apply to any artifact that has been creating, or can generate a new theme on-the-fly.

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 for Codex models/pricing, scheduled tasks, skills, settings, setup, troubleshooting, customization, automations, and self-knowledge—including 'you,' 'your,' 'this app,' or 'this coding agent' when they refer to Codex—and for OpenAI APIs/products and ChatGPT Work. Also use for model choice/migration, prompting, SDKs, Responses, Realtime, agents, evals, and Chat/Work/Codex comparisons. Do not use for generic app/software tasks that merely mention Codex.

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Enhancement-overlay SOP for adding sparse (BM25 / keyword) retrieval alongside dense (embedding) retrieval. Activate when a calling agent is building, reviewing, or debugging a retrieval pipeline whose corpus contains exact-match tokens — identifiers, error codes, SKUs, API/function names, proper nouns, citations, rare jargon — that pure dense embedding silently misses. Encodes the single decision rule (**hybrid is traffic-driven, not theoretical: add sparse only when the query share that depends on exact tokens is non-trivial**), the wiring of QueryFusionRetriever-style fusion (RRF vs alpha-weighted), and per-query-type alpha tuning. Frame the work as recovering lexical identity that dense pooling destroys, not as "add keyword search for completeness". Cross-links [[llamaindex]].

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Enhancement overlay for multi-agent / tool-using coder agents. Encodes the per-agent tool- scoping discipline that role-based frameworks (CrewAI, LangChain) document only as a passing best-practice: which agent gets which tool, and why blanket-sharing every tool to every agent is a correctness and blast-radius risk. Activates when an agent system has tools AND there is more than one agent (or one agent holding many tools). Treat a tool as a capability grant; scope by least-privilege. ENHANCE overlay — read alongside [[crewai]], [[agentsop-http-tool-wrapping]], [[agentsop-llm-tool-idempotency]]. Search keywords: which tools per agent, least-privilege agent, agent tool access, tool permissions, limit agent tools, scope tools to roles.overlay_type: enhancement

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Lens — Add a cognitive lens to any problem. It accepts a task description and produces an enhanced description that surfaces hidden dimensions, prerequisites, and lines of inquiry—the things you do not know you do not know. Use when the user asks to brainstorm, analyze, generate a skill, distill, or fuse, or when the input is too simple and needs to be expanded.

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Designs multiscale chunking for RAG by embedding small units for retrieval precision and returning larger context for synthesis. Use when fixed-size chunks either lose surrounding context or dilute relevance in long documents, manuals, filings, or codebases. Covers sentence-window and parent-child or auto-merging strategies, base chunk sizing, and evaluation. Do not use when a single chunk scale already meets retrieval and generation needs.

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Routes heterogeneous queries to the appropriate index, tool, or answering engine before retrieval. Use when one endpoint serves multiple handlers, such as summary, vector retrieval, text-to-SQL, or tools, and query types require different paths. Covers LLM, semantic, and rule-based routers, confidence thresholds, fallbacks, and framework mappings. Do not use when one handler serves all queries or branching is fixed.

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Lifecycle SOP for **per-model prompt artifacts** — the compiled prompts, instructions, few-shot demos, edit-format pins, and embedding-bound indices that change behavior when the underlying LM, dataset, or framework version changes. Activate when adopting compiled prompts (DSPy, GEPA, BootstrapFewShot output), when supporting multiple LMs in production, when a provider deprecates a model snapshot, or when a framework deprecates a config surface (LlamaIndex `ServiceContext` → `Settings`, Aider edit-format defaults). Do NOT activate for one-off raw prompt edits or for truly model-agnostic system prompts that have been swap-tested. Search keywords: prompt portability, model swap, recompile prompt, model deprecation, prompt per model, prompt breaks on new model, version compiled prompts.

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Review a complete mathematical-modeling competition paper with an atomic checklist covering abstract readability, page structure, typography, headings, pagination, figures, tables, equations, anonymity, metadata, visual balance, and page allocation. Return the review in chat or Markdown by default, and provide the bundled single-sheet checklist when the user requests a reusable form. Use when the user provides a PDF or Word paper and asks for a full-paper format and writing-compliance audit; do not replace technical model correctness review.

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。