「trend」の検索結果

Live SEO data via DataForSEO MCP server: SERP analysis, keyword research (volume, difficulty, intent, trends), backlink profiles, on-page analysis, competitor and content analysis, business listings, AI visibility (LLM mention tracking), and domain analytics. Requires DataForSEO extension installed. Use when user says "dataforseo", "live SERP", "keyword volume", "backlink data", "AI visibility check", or "real search 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。

Browse, filter, and explore a curated library of 2,500+ AI prompts organized by 19 categories, 17 models, and 4 output types. View stats, discover trending styles, and export prompt collections. Use when user says "prompt library", "browse prompts", "show categories", "list prompts", "prompt collection", "explore prompts", or "what prompts do we have".

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。

Research what people are actually saying about a topic in the last 30 days across Reddit, X / Twitter, YouTube, Hacker News, dev.to, Medium, and other public discourse platforms. API-free; uses WebSearch with platform-targeted site operators plus recency filters. Produces DISCOURSE.md (a structured brief) and JSON output the writer can consume. Complements blog-researcher (which focuses on authority sources) with a recency-and-engagement lens. Use when user says "blog discourse", "discourse research", "what are people saying about", "research what people are saying", "voice of customer", "social listening", "30-day research", "trend research", "what's the discussion on", "real-time research", "practitioner discourse", "/blog discourse".

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。

Leverages BigQuery's built-in machine learning and GenAI capabilities for advanced data analytics. Use when you need to write SQL queries that perform time-series forecasting, predict values, detect outliers or anomalies, find key drivers, perform semantic search or vector search, classify text, calculate similarity, summarize content, translate language, evaluate models, filter by semantic conditions, measure the causal effect of an intervention, compute correlations between columns, detect change points or structural breaks, extract trend or seasonality components, or leverage generative AI capabilities in BigQuery. Do not use for general BigQuery dataset, table, or job management requests.

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。

Weekly engineering retrospective. Analyzes commit history, work patterns, and code quality metrics with persistent history and trend tracking. Team-aware: breaks down per-person contributions with praise and growth areas.

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。

Search and analyze X (Twitter) using xAI Grok 4 via OpenRouter with Live Search. Trigger on prompts that explicitly or implicitly ask to "search Twitter/X", "what's trending", "tweets from @handle", "hashtag #…", "what are people saying", or that require tweet-level activity/engagement from X.

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。

สคริปต์ TikTok 15-60 วินาที — first-second hook, trending sound, comment bait, FYP optimization 2026

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。

Session retrospective. Runs after a workflow, coding session, or multi-step task completes. Analyzes what shipped, what failed, patterns observed, and growth opportunities. Generates a retro summary with per-step breakdowns, shipping streaks, and test health trends. Use after any significant session (>3 steps or >15 min). Proactively suggest when the user finishes a workflow, completes a feature, or says "let's review what we did." Also supports global retro across all projects.

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。

This skill should be used when the user asks to "implement a DiD regression", "run a staggered difference-in-differences", "set up an event study", "implement IV / 2SLS", "run a regression discontinuity design", "build synthetic control", "do propensity score matching", "test parallel trends", "run Honest DiD", "wild cluster bootstrap", "Callaway-Sant'Anna", "Sun-Abraham", "Bacon decomposition", "double machine learning", "debiased machine learning", "DML / DoubleML", "cross-fitting", "causal forest", "heterogeneous treatment effects", "ATE with ML controls", "which causal design fits my data", or needs causal-inference code, diagnostics, or identification writing in Python (StatsPAI), R, or Stata. Based on Scott Cunningham's Causal Inference: The Mixtape, with runnable validated templates, ML-based causal inference patterns, and Journal of Finance applications.

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。

Track AI visibility — measure whether a brand is mentioned and cited by AI assistants (Gemini, ChatGPT, Perplexity) for target prompts. Runs scans, tracks mention/citation rates over time, detects trends, and identifies opportunities. Uses Gemini API free tier (with grounding) as primary method, web search as fallback. Use when a user wants to: check if AI models mention their brand, track AI citation changes over time, measure AEO content effectiveness, monitor competitor AI visibility, or audit their brand's presence in AI-generated answers. Pairs with aeo-prompt-research-free (identifies prompts) and aeo-content-free (creates/refreshes content). This skill closes the loop by measuring results.

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。

36氪热门资讯监控 | 36kr Tech News Monitor. 获取 36氪 热门科技新闻、创业动态、投资快讯 | Get 36kr trending tech news, startup updates, investment news. 触发词：36氪、科技新闻、创业、36kr.

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 Notion to track meeting patterns, identify communication insights, and improve team coordination. Record decisions, attendees, and outcomes for trend 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。

Systematically document research findings, competitive analysis, market trends, and user insights in Notion. Build searchable research repository for data-driven decisions.

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。

Weekly engineering retrospective from commit history, work patterns, and code-quality metrics, with trend tracking and per-person breakdown. NOT for per-session reflection (mk:memory / Phase 6).

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。

Chief Security Officer mode: infrastructure-first audit of secrets, dependency supply chain, CI/CD, LLM/AI security, OWASP Top 10, STRIDE, active verification. Daily/comprehensive, trend tracking.

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。

Weekly-cadence engineering retrospective. Analyzes commit history, work patterns, and code quality metrics with persistent history and trend tracking. Team-aware: breaks down per-person contributions with praise and growth areas. Use when asked to "weekly retro", "what did we ship", or "engineering retrospective". Proactively suggest at the end of a work week or sprint. NOT for per-session or per-task reflection (see mk:memory / Phase 6 Reflect).

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。

Chief Security Officer mode. Infrastructure-first security audit: secrets archaeology, dependency supply chain, CI/CD pipeline security, LLM/AI security, skill supply chain scanning, plus OWASP Top 10, STRIDE threat modeling, and active verification. Two modes: daily (zero-noise, 8/10 confidence gate) and comprehensive (monthly deep scan, 2/10 bar). Trend tracking across audit runs. Use when: "security audit", "threat model", "pentest review", "OWASP", "CSO 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。

Reddit compound content operations skill. Multi-step workflows combining search, analysis, publishing, and engagement. Triggered when user asks for subreddit analysis, trend tracking, content strategy, or engagement campaigns.

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。

Causal inference with treatment/control groups over time. Use when user mentions: treatment effects, policy evaluation, pre/post comparison, parallel trends, DID, DiD, difference in differences, natural experiment, quasi-experimental.

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。

Generates production-ready design tokens from natural language descriptions. Outputs Tailwind configs, CSS custom properties, and DTCG W3C tokens from 24 trends + 31 AI-ready styles with 2800+ component examples.

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。

Identifies differentially bound ChIP-seq regions between conditions using DiffBind, csaw (sliding windows), DESeq2/edgeR/PyDESeq2 on count matrices, NormR (control-aware), or MAnorm2. Distinguishes three distinct normalization problems (composition bias, trended bias, global shifts) and matches each to its appropriate fix including spike-in scaling. Use when comparing ChIP-seq binding between experimental conditions, choosing normalization for global vs local changes, integrating spike-in data, or reconciling DiffBind/DESeq2 disagreement.

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。

Local-only regression / benchmark skill for ui-clone-skills maintainers. Drives the standard ui-reverse-engineering pipeline against the canonical reference site (https://realfood.gov) and records AE/SSIM, iteration count, gate fail counts, and outcome to benchmark/history.csv so prompt / sub-doc / model-version drift surfaces as a trend. Trigger phrases: "run benchmark" / "regression benchmark" / "benchmark clone". The Makefile no longer has a `benchmark` target — setup is inline bash in this skill (Step 1 below). Internal: NOT registered in `.claude-plugin/plugin.json` `skills`. Not part of the public 3-skill marketplace surface. Maintainer tooling only.

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。

Pure API reference for reddapi.dev - authentication, all endpoints (vector search, semantic search, trends, subreddit lookup), request parameters, response schemas, and error codes, with no research-workflow framing. Use when the user wants raw endpoint documentation, is debugging a reddapi.dev integration, needs exact request/response field names, or asks for 'reddapi API reference', 'reddapi.dev endpoints', or 'reddapi error codes'. For guided research workflows and query playbooks, see reddit-research. For B2B lead scoring, see reddit-leads.

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。

The original reddapi.dev Reddit search skill (vector search, semantic search, trends, subreddit discovery), no Reddit OAuth or app registration needed. This is the same engine now packaged as reddit-research with added market-research playbooks and a fuller pitch on semantic vs keyword search; reddapi is kept live under its original name for existing installs and works standalone. Use when the user says 'reddapi' by name, or wants a minimal drop-in Reddit search skill without the extra research-workflow guidance. For the expanded research-oriented version with query playbooks, see reddit-research. For B2B lead scoring, see reddit-leads. For a bare API reference, see reddit-search-api.

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。