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Use when the user wants to train, compare, or export machine learning models using Classification Learner or Regression Learner — including opening the app, loading data, training models, evaluating metrics, comparing results, visualizing plots, testing on held-out data, exploring model interpretability, and exporting trained models. Programmatic access to Classification Learner and Regression Learner apps via AppController.

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Deploy ML models on Kubernetes with KServe (formerly KFServing) and NVIDIA Triton Inference Server. Includes canary deployments, autoscaling, model versioning, A/B testing, and GPU resource management for production model serving.

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Audit model delegation and subagent effectiveness for a session — which models handled which subagent types, per-type success rates and average durations, and wasted delegations (heavy models on trivial work or types that consistently fail) — using the Agent Monitor workflow intelligence API. Use when reviewing how a session delegated work across models and subagents.

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Find the decisions in a pipeline that do not need the expensive model and propose the gate for each: a rule, a classic classifier, or a small model, with fail-closed routing. Use when the user asks to cut model costs or latency, says the big model handles everything, or wants a triage / routing / filter layer in front of an agent. Analysis plus an optional baseline scaffold. Do NOT use for auditing context layout (context-audit) or for building eval suites (evals-bootstrap).

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Extract and maintain global and method-specific mathematical-model assumptions from the problem frame, active method cards, data profile, and risk probes, while leaving necessity and impact judgments to the human modeler.

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Design and run risk-targeted robustness, sensitivity, error, and baseline checks for an approved mathematical model, emitting compact machine evidence in lean mode and a final report in submission mode.

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Audit and improve an entire agentic-development setup, including skills, context, memory, tools, models, permissions, hooks, workflow habits, and secrets hygiene. Invoke for setup reviews, workflow grading, Goblin Mode, or questions about what to improve. Supports shallow, deep, ultra, and yolo modes. Use domain-specific skills for a single model, report, or tenant.

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Compare several Japanese NLP libraries, models, or datasets for a keyword (a specific tool name, or a function/task like '形態素解析') across a handful of criteria chosen for that comparison, rendered as a ○/△/✕ table. Use when the user wants a side-by-side comparison of multiple Japanese NLP tools/libraries/datasets, not just the single best one. Trigger phrases include 'X と Y と Z を比較して', '形態素解析ライブラリを比較', 'MeCab と Sudachi どっちがいい', 'どのツールを使うべき', 'compare japanese tokenizers', 'mecab vs sudachi vs janome', 'which embedding model should I use', '日本語NERライブラリの比較表', 'pros and cons of japanese OCR tools'. For a single ranked list use search; for alternatives to one specific tool (or contribution candidates) without a multi-axis table, use discover.

Use when modeling in Autodesk Maya, such as hard-surface props, weapons, vehicles, a sci-fi crate, a game-ready or SubD film model, or a character or creature base mesh, or when asked to "clean up this model" or a scene before rigging or UVs. Also for support loops vs bevels vs creases, Smart Bevel, weighted normals, hard edges, pinching or wobbly highlights, a faceted cage, flipped faces, a triangle budget, n-gons, non-manifold geometry, mirroring a half, a low poly from a high poly, or deformation-ready topology.

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 administering a Scenario team from an agent through MCP: restricting which models a team or project can run (model access control, allowlist, blocklist), inviting or removing team and project members, changing roles, capping a member's Creative Unit spend, auditing API keys and their roles or scopes, or attributing consumption per user or model. Keywords: governance, model control, consumption cap, spend limit, API key hygiene, team admin, enterprise.

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Use when a Scenario generation is blocked, refused, or returns a moderation or sensitive-content error, when a video is rejected after rendering or its audio track is flagged, when an output is flagged for likeness to a real person or for IP and copyright detection, when the same prompt passes on one model and fails on another, or when a team's own characters, weapons, or props get flagged. Keywords: blocked prompt, content moderation, refused generation, provider filter, false positive.

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Generate fashion lookbook, model showcase, and style video prompts for Seedance 2.0 on Higgsfield. Use whenever the user wants fashion video content, lookbook videos, model walks, outfit showcases, style guides, fashion campaigns, runway clips, streetwear content, or any fashion/clothing video. Triggers on: fashion video, lookbook, model showcase, outfit, style guide, fashion campaign, runway, streetwear, collection launch, fashion ad, clothing video, OOTD, fashion film, or any fashion/model video request. Use even for "show off this outfit" or "fashion content for my brand.

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Selects a fine-tuning technique (SFT, DPO, RLVR, or RLAIF) for the user's use case and validates it against the selected model's available recipes. Use when the user has decided to finetune and needs to choose a technique, or when the technique needs to be validated against a model. Requires a base model to already be selected (via model-selection skill).

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 code that deploys fine-tuned models from SageMaker Serverless Model Customization to SageMaker endpoints or Bedrock. Use when the user says "deploy my model", "create an endpoint", "make it available", or asks about deployment options. Identifies the correct deployment pathway (Nova vs OSS), generates deployment code, and handles endpoint configuration.

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Use when the user asks to "design an experiment", "build a predictive model", "run A/B test analysis", "perform causal inference", "engineer features", "evaluate model performance", "set up MLOps pipeline", "analyze time series", "calculate sample size", or "deploy a model to production". Expert data science covering statistical modeling, experimentation, causal inference, feature engineering, ML deployment, and advanced analytics with Python, R, and SQL.

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 evaluate and compare multiple AI models or agents without pre-existing test data. Generates test queries from a task description, collects responses from all target endpoints, auto-generates evaluation rubrics, runs pairwise comparisons via a judge model, and produces win-rate rankings with reports and charts. Supports checkpoint resume, incremental endpoint addition, and judge model hot-swap. Use when the user asks to compare, benchmark, or rank multiple models or agents on a custom task, or run an arena-style evaluation.

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。

Always-on operational guardrails, model-independent. Apply on EVERY task and EVERY model whether or not fable-mode's staged loop is running. Three rules: verify-before-flag, warning batching, and find-and-replace safety. Trigger whenever a response would flag a problem, raise a warning, or edit files with search-and-replace.

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。

Pick the right LLM for LEGAL RESEARCH & ANALYSIS — issue-spotting, rule application, case/statute analysis, memos, and multi-step agentic research. Vendor-neutral routing grounded in mid-2026 benchmarks (Vals AI LegalBench across 124 models; Harvey Legal Agent Benchmark for agentic work). Asks up to 4 quick questions (cost, speed, accuracy/stakes, privacy/jurisdiction/language), then recommends a primary model + fallback + what to avoid + what a human must verify. Use when someone asks "which model for legal research / case analysis / a memo", "best AI for legal reasoning", "route this research task", or is starting legal analysis without a fixed model.

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 code generation models across HumanEval, MBPP, MultiPL-E, and 15+ benchmarks with pass@k metrics. Use when benchmarking code models, comparing coding abilities, testing multi-language support, or measuring code generation quality. Industry standard from BigCode Project used by HuggingFace leaderboards.

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。

Leveraged buyout model for A-share and China-market LBO scenarios. Adapts the standard LBO-model methodology for Chinese debt markets, CAS accounting, and AkShare data. Use instead of the original lbo-model skill when analyzing leveraged buyouts involving Chinese targets or China-based sponsors.

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。

Audit financial models and Excel workbooks for A-share analysis. Adapts the original audit-xls skill for Chinese financial modeling standards, CAS conventions, and A-share-specific checks. Triggers on "A股模型审计", "财务模型核查", "audit model China", "audit xlsx", "模型QC", or "check model [company]".

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。

Discounted cash flow (DCF) valuation model for A-share equities. This alias skill redirects to china-dcf, which provides comprehensive DCF modeling adapted for Chinese market conventions. Triggers on "A股DCF模型", "DCF估值", "DCF model China", "现金流折现", "discounted cash flow A-share", or "DCF [company]".

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 professional Excel workbooks for A-share financial analysis. Adapts the original xlsx-author skill for Chinese financial modeling standards, CAS conventions, and A-share formatting. Triggers on "A股Excel模型", "财务模型制作", "create model China", "build model xlsx", "制作模型", or "Excel model [company]".

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。

Find and compare recommended Hugging Face models for a task using benchmarks, model size, and device constraints. Use for model selection questions; use huggingface-local-models for GGUF setup and hf-cli for Hub operations.

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。