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概要と使いどころ
Use this skill when the user asks about truck purchase financing, leasing options, factoring, fuel surcharge math, lease-purchase agreements, the total-cost-of-ownership (TCO) comparison between buying + leasing, and how owner-operators finance their first truck. Cover Section 179 + Bonus Depreciation tax treatment.
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 the user asks about team driving — two-driver OTR operations, how teams maximize miles, HOS rules with sleeper berth swap, pay structures, equipment requirements (sleeper berth specs), team retention, and common challenges. Reference 49 CFR 395.1(g).
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 carrier asks how to spec a new tractor — engine choice (X15 vs DD16 vs PACCAR MX), transmission (manual vs automated manual vs automatic), axle ratio, sleeper size, fuel tanks, aerodynamic options, electronic features, warranty, fleet-spec vs premium-spec tradeoffs, and how spec choices affect resale value.
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 a driver encounters adverse weather, road, or traffic conditions and needs to extend driving time. Covers eligibility criteria, the 2-hour maximum extension, documentation requirements, and what does NOT qualify.
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 the user asks about logging / log hauling operations — short-haul exemption specifics for logging, log truck securement, axle weights, state-by-state logging permits (especially OR, WA, ID, BC), bunk + stake configurations, and how logging fits into FMCSA + state rules. Reference 49 CFR 393 for cargo securement of logs.
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 to differentiate IFTA (fuel tax) and IRP (apportioned registration). Covers what each is for, deadlines, and how they interact.
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 carrier asks whether to buy or lease their next tractor (or trailer), comparing full-service lease (Ryder, Penske, NationaLease), capital lease, finance lease, walk-away open-end lease, or cash purchase. Covers tax treatment, residual risk, maintenance bundling, balance-sheet impact, and cash-flow modeling.
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 the user asks about International Fuel Tax Agreement (IFTA) compliance — quarterly returns, jurisdiction reporting, fuel + miles reconciliation, IFTA-100/101 forms, base jurisdiction selection, IFTA license + decals, recordkeeping requirements, common IFTA audit findings, or how to handle non-IFTA jurisdictions. Cite IFTA Articles of Agreement.
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。
Improve Coval trace quality after basic ingestion works. Use when traces are sparse, missing useful STT/LLM/TTS/tool spans, missing attributes needed for Coval built-in metrics, or when a customer wants maximum debugging and observability value from agent traces.
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。
Dense UI posture — maximum information density with clear hierarchy. Use when building or reworking admin dashboards, data tables and grids, ops or monitoring views, settings-heavy panels, debug inspectors, state or log viewers, and multi-pane tool UIs someone drives all day.
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。
Optimize content for AI search engines (ChatGPT, Perplexity, Claude, Gemini) and featured snippets. Covers Generative Engine Optimization (GEO), Answer Engine Optimization (AEO), E-E-A-T signals, and content structuring for maximum AI citation probability. Use when optimizing content for AI visibility or auditing AI search readiness.
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。
Convex backend specialist. Use this agent for any code inside a `convex/` directory — function definitions, schemas, indexes, queries, mutations, actions, HTTP endpoints, cron jobs, file storage, auth wiring, and component installation. Knows the object-form function syntax, validator patterns, resource limits, and component ecosystem that generic Claude routinely gets wrong.
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。
Diagram knowledge base. Provides Mermaid syntax, C4 model, diagram types, and best practices for technical diagrams.
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。
Read analysis results. Use when user asks about maximum stress, extracting displacements, reaction forces, or exporting results. Post-processes ODB files.
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。
[71] CLOSE. Quick, safe cleanup after completing a milestone. Fix objective issues only (syntax errors, dead code, poor naming). Must be <5% of main task time, <30 seconds per fix, and reversible. Use after key points, not after every small change.
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。
Master automated billing systems including recurring billing, invoice generation, dunning management, proration, and tax calculation.
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。
First-principles assumption auditor. Classifies each hidden assumption (fact / convention / belief / interest-driven), ranks by fragility × impact, and rebuilds conclusions from verified premises. Bilingual: auto-detects Chinese or English.
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。
Guarantee valid JSON/XML/code structure during generation, use Pydantic models for type-safe outputs, support local models (Transformers, vLLM), and maximize inference speed with Outlines - dottxt.ai's structured generation library
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。
Optimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency. Use for production deployment on NVIDIA GPUs (A100/H100), when you need 10-100x faster inference than PyTorch, or for serving models with quantization (FP8/INT4), in-flight batching, and multi-GPU scaling.
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。
Connect to a running NeuroSkill instance and incorporate the user's real-time cognitive and emotional state (focus, relaxation, mood, cognitive load, drowsiness, heart rate, HRV, sleep staging, and 40+ derived EXG scores) into responses. Requires a BCI wearable (Muse 2/S or OpenBCI) and the NeuroSkill desktop app running locally.
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。
Automatically organizes invoices and receipts for tax preparation by reading messy files, extracting key information, renaming them consistently, and sorting them into logical folders. Turns hours of manual bookkeeping into minutes of automated organization.
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 and optimize tweets for maximum reach using Twitter's open-source algorithm insights. Rewrite and edit user tweets to improve engagement and visibility based on how the recommendation system ranks content.
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 job descriptions and generates tailored resumes that highlight relevant experience, skills, and achievements to maximize interview chances
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 technical diagrams using Mermaid syntax for architecture, sequences, ERDs, flowcharts, and state machines. Use for visualizing system design, data flows, and processes. Triggers: diagram, mermaid, architecture diagram, sequence diagram, flowchart, ERD, entity relationship, state diagram, C4 model, component diagram, visualize, draw.
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。