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概要と使いどころ
Use Ontoly Software Graph evidence, CLI reports, MCP, and official Ontoly Agent Skills before searching source files when reviewing architecture, dependencies, request flows, configuration, impact, or security-sensitive ownership.
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 targeting Geology (GSA) or deciding whether a short, high-impact geoscience research letter fits this venue. Encodes the journal's broad-significance-in-tight-length fit, the novelty and rapid-communication bar, GSA house style, official-submission re-check, and desk-reject heuristics.
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 deciding whether a materials result clears Advanced Materials' advance-and-broad-impact gate, or whether it belongs in Advanced Functional Materials, Advanced Science, Small, or Advanced Materials Interfaces. Diagnoses venue fit; does not write or characterize the material.
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 deciding whether a project is a strong AAAI submission across its broad AI scope, should be reframed or routed to a dedicated track such as AI for Social Impact or AI Alignment, or should instead go to IJCAI, NeurIPS, ICML, ICLR, AISTATS, UAI, ACL, CVPR, KDD, CHI, ICRA, or another specialist venue.
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 designing or auditing AAAI experiments for the broad-AI program committee, including baselines, ablations, statistical significance, robustness, human evaluation, AI-for-Social-Impact and alignment/safety evidence, compute and cost reporting, and reproducibility-checklist alignment for Phase-1 survival.
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。
Predicts whether a DNA variant alters mRNA splicing using sequence-based deep-learning tools — SpliceAI (10kb context dilated CNN, clinical default), Pangolin (multi-tissue), MMSplice (modular per-region CNN with calibrated ΔPSI), SpliceTransformer/TrASPr (tissue-aware transformers), SpliceVault (empirical 300K-RNA lookup of likely mis-splicing outcomes), CADD-Splice (composite score). Applies the ClinGen SVI 2023 framework for ACMG/AMP variant interpretation (PVS1, PP3, BP4 evidence codes), HGVS splicing nomenclature (c.123+1G>A, c.123-3T>G, r.spl?), extended-window scoring for deep-intronic pseudoexons, tissue-specific predictions, branchpoint variant detection (BPHunter, LaBranchoR), and splice-switching ASO design. Use when interpreting splice impact of clinical variants, prioritizing VUS, identifying deep-intronic pathogenic variants, or designing ASOs.
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。
Build shared real-time experiences using GroupActivities and SharePlay. Use when implementing shared media playback, collaborative app features, synchronized game state, or any FaceTime, Messages, AirDrop, or nearby visionOS group activity on iOS, macOS, tvOS, or visionOS.
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 multiplayer spatial board games using TabletopKit on visionOS. Use when creating tabletop game experiences with boards, pieces, cards, or dice; managing seats, turns, equipment state, TabletopAction flows, or TabletopInteraction delegates; synchronizing gameplay through FaceTime Group Activities; rendering with RealityKit; or implementing snapping, tosses, and physics on a virtual table surface.
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。
Implement, review, or improve Live Activities and Dynamic Island experiences in iOS apps using ActivityKit. Use when building real-time updating widgets for the Lock Screen and Dynamic Island — delivery tracking, sports scores, ride-sharing status, workout timers, media playback, or any time-sensitive information that updates in real time. Also use when working with ActivityKit, ActivityAttributes, Activity lifecycle (request/update/end), Dynamic Island layouts (compact/minimal/expanded), push-to-update Live Activities, or Lock Screen live widgets.
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。
Develop business continuity plans and impact analysis. Implement BCP testing and communication procedures. Use when building organizational resilience.
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。
App stack advisor — essential apps by business stage, cost optimization, performance impact 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。
Pricing strategy for TikTok — competitive pricing, commission impact, bundling, discount psychology
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。
Offsite Ads analysis — when to opt out, ROI tracking, fee impact on margins, attribution
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。
E-commerce growth strategy advisor. Diagnoses current business health using unit economics (CAC, LTV, AOV, contribution margin), identifies the highest-impact growth opportunities across 5 levers (traffic, conversion, AOV, retention, expansion), and builds a prioritized 90-day growth roadmap. Uses the Ansoff Matrix adapted for e-commerce to evaluate market penetration, channel expansion, product expansion, and new market entry. Includes multichannel readiness assessment (Amazon, Walmart, TikTok Shop, Etsy, DTC/Shopify/Shopify) and product line expansion analysis. No API key required. Use when: (1) planning next phase of e-commerce growth, (2) deciding whether to expand to new channels or products, (3) diagnosing why growth has stalled, (4) prioritizing what to fix or build next.
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 a transcript has to be cut to a fixed size and you must choose which turns go. Jev marks each turn keep, summarize or drop. Measured: it does not make a handoff better.
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。
Automated content creation pipeline for business growth. Creates blog posts, landing pages, email sequences, social media content, and video scripts with a supply-demand topic gate, 5-dimensional quality diagnosis, one-core resonance check, per-sentence information density scoring, 12-signal authenticity audit, headline impact matrix, content substance scoring, and a cheap-format MVP testing ladder. Use when the user needs content marketing, blog posts, email sequences, copywriting, video scripts, or says 'write content', 'blog post', 'email sequence', 'content calendar', 'marketing copy', 'video script', or 'hook'.
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。
Render an ordered timeline of one Claude Code session's events (every event type) with per-event durations and tool names, reconstructed from Agent Monitor data. Pairs PreToolUse with PostToolUse to compute tool durations and surfaces gaps, errors, and compaction points. Use when reconstructing what happened in a session step by step.
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 a workflow-intelligence report from CCAM orchestration, delegation, tool flow, concurrency, complexity, compaction, and fleet-run data. Use for architecture reviews, agent-fleet analysis, or workflow optimization reports.
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。
Summarize a sprint's worth of Claude Code activity — sessions grouped by project (cwd), per-model cost breakdown, token efficiency (cache hit rate, compaction baselines), subagent effectiveness from workflow API, velocity metrics (turn_count, turn_duration_ms), and tool diversity across the sprint.
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。
Generate a daily standup summary from recent Claude Code sessions — completed work grouped by project (cwd), session costs from the pricing engine, tool invocations, error/compaction/APIError events, and turn velocity metrics from session metadata (turn_count, total_turn_duration_ms).
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。
Break down Claude Code costs using the Agent Monitor pricing engine. Shows per-model costs (input, output, cache_read, cache_write at $/Mtok rates), per-session costs, daily trends, and compaction baseline token recovery. Use when analyzing spending, comparing model costs, or planning budgets.
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。
Detect quality and efficiency regressions over time using Agent Monitor data — rising error rate (APIError events), falling cache hit rate, growing compaction frequency, and climbing cost-per-session. Splits history into an earlier baseline window and a recent window and reports which metrics are getting worse, by how much, and where. Use when checking whether things are degrading or trending in the wrong direction.
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。
Detect recurring patterns using the Agent Monitor's workflow intelligence — toolFlow transitions (tool A → B frequency matrices), recurring workflow patterns, agent co-occurrence pairs, model delegation habits, error propagation paths by agent depth, and compaction triggers. Use to discover habitual usage patterns and anti-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。
Calculate a productivity score using actual Agent Monitor metrics — session completion rates, cache efficiency (cache_read vs input), compaction pressure (baseline tokens), turn velocity (turn_count / total_turn_duration_ms), tool success ratio (PreToolUse vs PostToolUse), and the workflow intelligence API's complexity and effectiveness scores.
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。