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概要と使いどころ
Audit an existing Coval evaluation setup for coverage, misleading scores, missing evidence, judge validation and execution risk. Read-only; use when inheriting an eval or deciding whether its results support a release decision.
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。
Launch a specific Coval evaluation with explicit cases and execution bounds. Use when resources are already selected; for result analysis use quick-eval.
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。
Choose the next useful step for evaluating a voice or chat agent with Coval. Use for getting started or deciding what to do next; use a specific skill directly when the task is already clear.
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。
Retrieve and analyze simulation results from a Coval run. Use when user wants to review evaluation outcomes or debug agent behavior.
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 handoff packages for session transitions. Use when ending sessions, preparing for continuation, saving session state, or creating resumable context.
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。
Design abstract strategy games with perfect information, no randomness, and strategic depth. Use when designing a board game, exploring abstract strategy games, brainstorming game mechanics, or evaluating game balance. Keywords: board game, game design, strategy, mechanics, balance.
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 workflow for static structural analysis. Use when analyzing stress, displacement, or reaction forces under constant loads. For strength and stiffness 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。
[32] ANALYZE. Ensure every critical claim has verifiable evidence with confidence levels. Each fact must have source + confidence percentage. If confidence <85%, enter Loop150 to find more sources. Use for critical decisions, factual claims, legal/compliance work, or any situation where unverified claims are dangerous.
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。
Library of accessibility personas representing people with various disabilities, impairments, and situational limitations. Use this skill when users ask about disability types, accessibility personas, user needs for specific conditions, how people with disabilities use technology, assistive technology users, or designing for accessibility. Triggers on requests about blindness, deafness, cognitive disabilities, motor impairments, low vision, screen readers, sign language, autism, ADHD, temporary disabilities, or any question about "how would a person with X use this".
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。
[31] ANALYZE. Universal deep thinking methodology for any situation requiring quality reasoning. Use when solving problems, debugging, making decisions, analyzing code, planning, reviewing, or anytime you need thorough thinking instead of surface-level responses. Triggers on "think deeply", "analyze thoroughly", "reason carefully", "deep thinking", "understand completely", or any task requiring careful thought.
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。
Perform 12-Factor App compliance analysis on any codebase. Use when evaluating application architecture, auditing SaaS applications, or reviewing cloud-native applications against the original 12-Factor 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。
Formal evaluation framework for Claude Code sessions implementing eval-driven development (EDD) principles
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。
Codified expertise for managing carrier portfolios, negotiating freight rates, tracking carrier performance, allocating freight, and maintaining strategic carrier relationships. Informed by transportation managers with 15+ years experience. Includes scorecarding frameworks, RFP processes, market intelligence, and compliance vetting. Use when managing carriers, negotiating rates, evaluating carrier performance, or building freight strategies.
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。
X-ray any AI model's behavioral patterns — refusal boundaries, hallucination tendencies, reasoning style, formatting defaults. No API key needed.
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。
Azure Resource Manager SDK for Arize AI Observability and Evaluation (.NET).
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。
High-level SDK for Azure AI Foundry projects with agents, connections, deployments, and evaluations.
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。
Azure AI Projects SDK for Java. High-level SDK for Azure AI Foundry project management including connections, datasets, indexes, and evaluations.
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。
Azure AI Projects SDK for .NET. High-level client for Azure AI Foundry projects including agents, connections, datasets, deployments, evaluations, 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。
Architectural decision-making framework. Requirements analysis, trade-off evaluation, ADR documentation. Use when making architecture decisions or analyzing system design.
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 evaluate influencers for brand partnerships, verify authenticity, and track collaboration performance across Instagram, Facebook, YouTube, and TikTok.
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。
A meta-skill that understands task requirements, dynamically selects appropriate skills, tracks successful skill combinations using agent-memory-mcp, and prevents skill overuse for simple tasks.
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。
Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring—where even top agents achieve less than 50% on real-world benchmarks
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Presents a risk framework for every configurable security control in NemoClaw. Use when evaluating security posture, reviewing sandbox security defaults, or assessing control trade-offs. Trigger keywords - nemoclaw security best practices, sandbox security controls risk framework, nemoclaw credential storage, credentials.json, api key security, openclaw security controls, nemoclaw security boundary, prompt injection, tool access 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。
Remove refusal behaviors from open-weight LLMs using OBLITERATUS — mechanistic interpretability techniques (diff-in-means, SVD, whitened SVD, LEACE, SAE decomposition, etc.) to excise guardrails while preserving reasoning. 9 CLI methods, 28 analysis modules, 116 model presets across 5 compute tiers, tournament evaluation, and telemetry-driven recommendations. Use when a user wants to uncensor, abliterate, or remove refusal from an LLM.
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。