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概要と使いどころ
Show vault statistics and knowledge graph metrics. Provides a shareable snapshot of vault health, growth, and progress. Triggers on "/stats", "vault stats", "show metrics", "how big is my vault".
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 evaluating business model viability, analyzing profitability per customer/product/transaction, validating startup metrics (CAC, LTV, payback period), making pricing decisions, assessing scalability, comparing business models, or when user mentions unit economics, CAC/LTV ratio, contribution margin, customer profitability, break-even analysis, or needs to determine if a business can be profitable at scale.
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。
Preprocessing and harmonization of multi-omics data before integration. Covers normalization, batch correction, feature alignment, and missing value handling across data types. Use when preparing multi-omics datasets for integration 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。
Similarity Network Fusion (SNF) for patient stratification using multi-omics data. Integrates multiple data types into a unified patient similarity network. Use when performing patient stratification or integrating multi-omics data into unified similarity networks.
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。
XCMS3 workflow for LC-MS/MS metabolomics preprocessing. Covers peak detection, retention time alignment, correspondence (grouping), and gap filling. Use when processing raw LC-MS data into a feature table for untargeted metabolomics.
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。
Quality control and normalization for metabolomics data. Covers QC-based correction, batch effect removal, and data transformation methods. Use when correcting technical variation in metabolomics data before statistical 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。
Construct time-scaled phylogenies and infer evolutionary dynamics using TreeTime and BEAST2 for outbreak analysis. Estimate divergence times, molecular clock rates, and ancestral states. Use when dating outbreak origins, estimating transmission rates, or building time-calibrated trees.
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。
Calculate alignment statistics including sequence identity, conservation scores, substitution matrices, and similarity metrics. Use when comparing alignment quality, measuring sequence divergence, and analyzing evolutionary 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。
Search arXiv physics, math, and computer science preprints using natural language queries. Powered by Valyu semantic search.
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。
Show session analytics, learning patterns, correction trends, heatmaps, and productivity metrics. Computes stats from project memory and session history. Use when asking for stats, statistics, progress, how am I doing, coding history, or dashboard.
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。
Queries S3 object metadata, tracks bucket activity, audits object changes, searches annotations, and analyzes storage metrics using S3 Metadata system tables (journal, inventory, annotation) and S3 Storage Lens tables via Athena SQL. Applies when counting objects, finding recent uploads or deletions, identifying who wrote to a prefix, breaking down storage classes, finding objects by tag, searching annotation content, analyzing storage lens metrics, or enabling S3 Metadata tracking. Prefers system tables over raw S3 APIs (list-objects-v2, head-object) at scale. Trigger phrases: bucket activity, object count, who uploaded, track deletions, storage class breakdown, find by tag, search annotations, storage lens metrics, audit bucket changes.
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。
Provides authoritative guidance on Amazon Neptune Database and Neptune Analytics for graph, knowledge-graph, and relationship-heavy workloads — fraud detection / fraud rings, agentic memory / chatbot context across sessions, recommendations, identity resolution, Gremlin / openCypher / SPARQL queries, supernode / slow traversal, Neo4j to Neptune migration / APOC compatibility, Neptune Database vs Analytics engine selection, PageRank / community detection, GraphRAG, and connectivity from Lambda / EC2 / applications. Creates and modifies Neptune Database clusters/instances and Neptune Analytics graphs on explicit user confirmation; blocks destructive operations (delete, reset-graph, failover, major upgrade) and redirects to change-control.
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。
Aggregate and centralize performance metrics from applications, systems, databases, caches, and services. Use when consolidating monitoring data from multiple sources. Trigger with phrases like "aggregate metrics", "centralize monitoring", or "collect performance 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。
Upgrade Three.js games from prototype visuals to premium browser graphics: art-direction critique, procedural model building, material and texture libraries, world prop kits, shaders, VFX, lighting and render pipeline, LOD and instancing, render budgets, and a 10-category visual scorecard. Use when screenshots still look basic or the user asks for premium, AAA, high-fidelity, showcase, or less-basic graphics.
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。
Evaluate and improve interface usability using heuristic analysis. Use when the user mentions "usability audit", "users are confused", "form usability", "navigation problems", "Nielsen heuristics", "cognitive walkthrough", or "is this easy to use". Also trigger when reviewing a design for usability issues, improving form-completion rates, or evaluating information architecture and navigation. Covers Krug's laws, Nielsen's 10 heuristics, severity ratings, dark-pattern recognition, and accessibility. For visual design fixes, see refactoring-ui. For conversion-focused audits, see cro-methodology.
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 statistics and claims in blog posts by fetching cited source URLs and checking if the claimed data actually appears on the page. Extracts all load-bearing claims (statistics, product or policy claims, ranking and comparative claims, named sources), validates cited URLs before fetching, and scores match confidence (exact match 1.0, paraphrase 0.7-0.9, not found 0.0). Flags uncited claims as UNVERIFIED. Use when user says "fact check", "verify statistics", "check sources", "validate claims", "factcheck", "source verification".
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 statistics and claims in blog posts by fetching cited source URLs and checking if the claimed data actually appears on the page. Extracts all load-bearing claims (statistics, product or policy claims, ranking and comparative claims, named sources), validates cited URLs before fetching, and scores match confidence (exact match 1.0, paraphrase 0.7-0.9, not found 0.0). Flags uncited claims as UNVERIFIED and marks a claim verified only when this run fetched the source and found the passage. Checks that a source says what the post says it says; it cannot prove the source is true or verify first-hand experience. Use when the user says "fact check", "verify statistics", "check sources", "factcheck". Report only; not for rewriting posts (blog-rewrite).
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。
Guide for editing videos where the primary content is people talking — talking-head / 口播, interview / 访谈, lecture, tutorial, podcast, course content, and similar talking-driven formats. Use when the user wants speech editing on a talking video (剪口播 / 口播剪辑 / 去口癖 / clean up fillers / smooth speech), motion graphics layered onto talking video (口播加 MG / 加动画), or B-roll on a talking video (加 B-roll / add B-roll). For motion graphics specifically, use this together with the active Motion Graphics skill/workflow available in the current OpenChatCut environment — this skill adds talking-specific guidance (speech-rhythm timing, frame-aware placement, subject/caption protection, placement verification).
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。
Designs and directs the visual assets that carry content — carousels, infographics, quote graphics, diagrams, and social imagery — including the generation prompts where they are AI-produced. Use this to turn a written piece into a visual format, design a carousel or infographic, create social graphics, or fix visuals that are not stopping the scroll.
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 reporting and self-serve analytics that people actually use — metric trees, dashboard design, distribution, and the discipline that stops dashboards proliferating. Use this to build a dashboard or report, design a metrics framework, set up self-serve analytics, decide what to measure, or diagnose why reporting exists but nobody uses it or trusts it.
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 creating database schemas or tables for Timescale, TimescaleDB, TigerData, or Tiger Cloud, especially for time-series, IoT, metrics, events, or log data. Use this to improve the performance of any insert-heavy table. **Trigger when user asks to:** - Create or design SQL schemas/tables AND Timescale/TimescaleDB/TigerData/Tiger Cloud is available - Set up hypertables, compression, retention policies, or continuous aggregates - Configure partition columns, segment_by, order_by, or chunk intervals - Optimize time-series database performance or storage - Create tables for sensors, metrics, telemetry, events, or transaction logs **Keywords:** CREATE TABLE, hypertable, Timescale, TimescaleDB, time-series, IoT, metrics, sensor data, compression policy, continuous aggregates, columnstore, retention policy, chunk interval, segment_by, order_by Step-by-step instructions for hypertable creation, column selection, compression policies, retention, continuous aggregates, and indexes.
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。
Define custom distance/similarity metrics for clustering and ML algorithms. Use when working with DBSCAN, sklearn, or scipy distance functions with application-specific metrics.
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 answering a question that needs data from a ktx-connected database - investigating, analyzing, "how many", "show me", "what's the breakdown of", finding records by value, exploring tables, comparing periods, explaining metrics, or any data-analysis request. Triggers even when the user does not say "analytics"; if the answer requires querying a configured ktx connection, this skill applies.
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。
Set up 3D physics in Unity 6.3 LTS: Rigidbody movement and forces, colliders, triggers vs collisions, layer-based collision, raycasts, and joints. Use when adding a Rigidbody, handling OnCollisionEnter/OnTriggerEnter, tuning collision layers, casting rays, or when the user mentions Unity physics, AddForce, isKinematic, or linearVelocity.
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。