7,028 件 ・ 関連度順
概要と使いどころ
Plan, execute, and measure business events — conferences, webinars, workshops, product launches, networking events, trade shows, and corporate gatherings. Complete event lifecycle from concept to post-event ROI 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。
Complete competitive intelligence system — market mapping, product teardowns, pricing intel, win/loss analysis, battlecards, and strategic monitoring. Goes far beyond SEO to cover the full business landscape.
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。
Query real-time and historical financial data across equities and crypto—prices, market moves, metrics, and trends for analysis, alerts, and reporting.
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。
Fast X Intelligence CLI (Rust) — search, analyze, and engage on X/Twitter from the terminal. Use when: (1) user says "x research", "search x for", "search twitter for", "what are people saying about", "what's twitter saying", "check x for", "x search", "search x", (2) user wants real-time monitoring with "watch", (3) user needs AI-powered analysis with Grok ("analyze", "sentiment"), (4) user needs intelligence reports ("report"), (5) user wants to track followers ("diff"), (6) user needs trending topics ("trends"). Also supports: bookmarks, likes, following (OAuth), x-search, collections, CSV/JSON/JSONL export. Non-goals: Not for posting tweets, not for DMs, not for enterprise features.
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 new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
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。
Converts the learning Markdown into a standalone, split-screen interactive HTML page. Left pane for EN/CN text, right pane for deep linguistic 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。
Conduct comprehensive AI-powered research with citations via the Tavily CLI. Use this skill when the user wants deep research, a detailed report, a comparison, market analysis, literature review, or says "research", "investigate", "analyze in depth", "compare X vs Y", "what does the market look like for", or needs multi-source synthesis with explicit citations. Returns a structured report grounded in web sources. Takes 30-120 seconds. For quick fact-finding, use tavily-search instead.
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 git commit with conventional commit message analysis, intelligent staging, and message generation. Use when user asks to commit changes, create a git commit, or mentions "/commit". Supports: (1) Auto-detecting type and scope from changes, (2) Generating conventional commit messages from diff, (3) Interactive commit with optional type/scope/description overrides, (4) Intelligent file staging for logical grouping
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。
Performs paragraph-by-paragraph translation and deep linguistic analysis. Explains English syntax, grammar, and sentence structure in Chinese. References assets from the raw source directory.
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 bug analysis to break the fix-forget-repeat cycle. Analyzes root cause category, why fixes failed, prevention mechanisms, and captures knowledge into specs. Use after fixing a bug to prevent the same class of bugs.
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。
Cross-channel campaign performance analysis with blended CAC and budget 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。
Monitor competitor ads across Meta, Google, and LinkedIn with strategic 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。
Weekly competitive landscape monitoring and 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。
Reallocate ad budget across campaigns and channels using ROAS and marginal return 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。
Outreach triggered by funding events with post-funding needs analysis and personalized drafts
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。
Analyze closed deals and lost opportunities for 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。
Weekly pipeline health analysis with velocity metrics, stage distribution, forecasting, and 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。
Evaluates current go-to-market positioning versus the market. Analyzes website, content, pricing, ICP alignment, and competitive landscape to identify gaps, opportunities, and produce a GTM health report.
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-touch attribution analysis across marketing channels with multiple models
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。
Classify reply sentiment and determine next action
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。
Revenue forecast from pipeline data with stage-weighted probability and sensitivity 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。
Rigorous benchmarking of multi-agent coordination systems: experiment design, statistical analysis, human evaluation protocols, and reproducible reporting. NOT FOR ML model evaluation (use llm-evaluation-harness), A/B testing for web products, survey design, or general data science.
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。
Data structures and algorithms for AI agent episodic memory. Covers vector stores (HNSW, IVF, PQ), temporal indexing, knowledge graphs with triple stores, hierarchical summarization, forgetting curves, working/long-term/ procedural memory, and memory consolidation. Deep analysis of MemGPT/Letta, Zep/Graphiti, Mem0, and the Stanford generative agents memory architecture. Teaches the CS fundamentals behind how agents remember, retrieve, and forget. Activate on: "agent memory", "episodic memory", "vector search algorithm", "HNSW", "memory retrieval", "forgetting curve", "knowledge graph memory", "MemGPT", "Letta", "Zep", "Mem0", "memory consolidation", "temporal retrieval", "agent long-term memory", "memory layer". NOT for: conversation protocol design (use agent-conversation-protocols), agent infrastructure selection (use agentic-infrastructure-2026), building RAG pipelines (use ai-engineer).
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 introducing a feature flag system, choosing between release flags / experiment flags / operational flags / kill switches / permission flags, designing percentage rollouts, building kill-switch + circuit-breaker patterns, structuring evaluation contexts (user IDs, plan tier, region), or building flag-cleanup discipline. Triggers: LaunchDarkly / GrowthBook / Unleash / Flagsmith / OpenFeature, percentage ramp, sticky bucketing, kill switch wired to alerting, "we still have flags from 2 years ago", flag debt, OpenFeature provider, evaluation context, default value on provider failure. NOT for A/B test statistical analysis (separate skill), traffic-split routing at the edge, build-time bundler defines, or environment-based config.
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。