2,267 件 ・ 関連度順
概要と使いどころ
Use when user asks to review contracts, detect contract risks, or perform compliance checks. 合同智能审核 - 对合同扫描件、图片合同、双语合同进行完整审核。 覆盖 OCR 识别、条款分析、风险检测、合规校对、翻译摘要全流程。 适用于法务审核、采购合同审查、供应链合规检查等场景。 关键词：合同审核、风险检测、条款分析、合规校对、contract review、risk detection。
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。
portfolio hedging, tail risk hedging, options overlay, correlation regime, downside protection, portfolio insurance, risk mitigation strategy, hedge construction, black swan hedging, volatility hedging
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。
position sizing, Kelly criterion, risk budgeting, volatility targeting, portfolio allocation, bet sizing, capital allocation, drawdown management, position management, risk parity sizing
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。
Trading psychology, risk management discipline, position sizing psychology, drawdown management, behavioral bias mitigation, emotional discipline framework, loss aversion 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。
Grounded in first principles, rigorously examine and refine a user's plan, task, decision, goal, strategy, proposal, or idea through structured, progressively deeper questioning, in order to bridge the gap between the User and the Agent. Use when the user explicitly requests grilling, challenge, pressure-testing, cross-examination, red-team review, pre-mortem analysis, or a decision audit. The goal is to uncover unclear objectives, hidden assumptions, contradictions, weak evidence, missing information, overlooked constraints, dependencies, risks, trade-offs, failure modes, and misalignment between intended outcomes and likely real-world results. Begin by establishing a shared understanding of the user's actual intent, goals, constraints, and success criteria. Ask focused, high-leverage questions rather than broad or repetitive ones. Adapt each question based on previous answers, probing deeper where uncertainty, unsupported assumptions, or strategic weaknesses remain. Regularly summarize the current understanding of the user's intent and position to confirm alignment, expose misunderstandings, and refine the problem or goal definition. Distinguish facts, assumptions, hypotheses, and unknowns. Challenge reasoning rigorously while remaining constructive, respectful, and solution-oriented. Continue until the reasoning is internally coherent, evidence-aware, constraint-conscious, risk-assessed, and translated into a clearer, more actionable goal, decision, or plan. Ultimately, achieving the super-alignment.
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 and fill .agents/qa-project-context.md with the project's tech stack, test frameworks, CI/CD pipeline, environments, quality goals, risk areas, team structure, and conventions. This is the one file every other QA skill reads first, so they skip redundant discovery and give context-aware advice. Use when: "set up QA context," "configure testing," "initialize project," first use of any QA skill. Not for: bootstrapping a brand-new project's QA end-to-end — use qa-start (which calls this skill as its first step). Related: qa-start, risk-based-testing, test-strategy, qa-metrics, playwright-automation.
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 a whole regression suite and prune/restructure it with evidence: per-test coverage fingerprinting, AST near-duplicate clustering, CI-history mining for never-failing and flaky tests, prune decision rules (redundant/obsolete/low-value/keep), smoke/core/extended tiering by risk and defect-detection history, and a defensible "what we deleted and why" record. Deletion is destructive — quarantine and human sign-off are mandatory. Use when: "audit the test suite," "prune redundant tests," "find duplicate tests," "which tests can we delete," "restructure into smoke/core/extended," "is this test pulling its weight," "shrink the regression suite." Not for: Judging whether an individual test is WELL-WRITTEN (smells, assertions) — that is ai-qa-review. Healing one flaky test at runtime — that is test-reliability. Bulk selector regeneration after a UI refactor — that is selector-drift-recovery. Related: ai-qa-review, coverage-analysis, test-reliability, risk-based-testing, qa-project-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。
This skill should be used when the user says "/tribunal", "tribunal", "review this", "3-way review", "risk-value-feasibility check", or wants multi-perspective adversarial review. Also triggered by: "트리뷰널", "리뷰 해줘", "3관점 리뷰", "위험성 검토". Runs 3 agents (Risk/Value/Feasibility) in parallel and synthesizes a verdict.
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 indexed codebases via graph database (neug) and vector index (zvec). Covers call graphs, dependencies, dead code, hotspots, module coupling, architecture reports, semantic search, impact analysis, bug root cause from GitHub issues, class diagrams (UML), and PR review (risk scoring, conflict detection, auto-merge candidates, labeling). Also covers creating, inspecting, and repairing a CodeScope index. Use for: code structure, who calls what, why something changed, similar functions, module boundaries, bug tracing, class relationships, PR risk/conflicts, or any question benefiting from a code knowledge graph. Applies when a `.codegraph` index exists in the workspace, or when the user wants to create one.
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 algorithmic trading systems — backtesting, strategy development, live execution, and risk management. Use when tasks involve backtesting trading strategies, connecting to exchange APIs (Binance, Alpaca, Interactive Brokers), implementing technical indicators, portfolio optimization, order execution, risk management rules, market data processing, or building trading bots. Covers both crypto and traditional markets.
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 and implement Monte Carlo methods for uncertainty quantification, risk analysis, and probabilistic simulations across scientific and financial domains. Use when "monte carlo, random sampling, uncertainty quantification, risk analysis, stochastic simulation, MCMC, variance reduction, probabilistic, " mentioned.
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 reviewing contracts, extracting key terms, identifying risks, or building contract analysis tools - covers NLP approaches, clause identification, and risk scoringUse when ", " mentioned.
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。
Applies the reasoning of Stuart Russell, AI safety expert, UC Berkeley professor, and co-author of 'Artificial Intelligence: A Modern Approach'. Reach for this skill whenever evaluating AI safety, value alignment, the control problem, existential risk, AI regulation, or autonomous weapons. Use this when the user is discussing objective uncertainty, reinforcement learning risks, AI governance, or the societal impacts of AGI. Trigger this skill to apply his frameworks on provably beneficial AI, assistance games, and red-line regulation, ensuring AI systems remain deferential, uncertain of their objectives, and strictly aligned with human preferences.
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。
Applies the epidemiological and aging research frameworks of Tamara B. Harris (epidemiologist, National Institutes of Health). Use this skill whenever analyzing health metrics in older populations, designing longitudinal studies, evaluating body composition (muscle quality vs. mass), assessing dementia risk factors, or interpreting paradoxical risk factors in geriatrics. Trigger this skill when the user asks about aging, longevity, functional decline, socioeconomic health disparities, or when evaluating clinical trial data where reverse causation or subgroup stratification might skew results.
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。
Applies the epidemiological reasoning and population-health frameworks of Albert Hofman (Harvard epidemiologist, Rotterdam Study). Trigger this skill whenever you are analyzing public health strategies, preventive medicine, cohort study design, cardiovascular or neurodegenerative disease risks, or healthy aging. Use it when evaluating whether to use population-wide interventions versus individual screening, assessing risk factors in elderly populations, or tracing adult chronic diseases back to early-life or fetal origins.
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 whenever you are reasoning about public health policy, cancer prevention, epidemiological study design, clinical risk models, or weight management advice. This skill channels the thinking of Graham A. Colditz, epidemiologist at Washington University in St. Louis. Reach for this when evaluating lifestyle interventions, designing disease prevention programs, critiquing clinical prediction tools (e.g., AUC vs. clinical utility), or analyzing life-course risk accumulation. Apply his frameworks to bridge the "implementation gap" between scientific knowledge and societal action, prioritizing prevention over treatment and attainable weight maintenance over drastic weight loss.
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。
Applies the reasoning style of Geoffrey Hinton, deep learning pioneer and 2018 Turing Award winner. Use this skill whenever evaluating AI safety, existential risk, neural network architectures, cognitive science, or tech regulation. Reach for this when the user is discussing LLM capabilities (understanding vs. autocomplete), the biological vs. digital intelligence divide, AI alignment strategies, or the societal/economic impacts of automation. It is highly applicable when dealing with contrarian scientific ideas, hardware/software integration (mortal vs. immortal computing), or global cooperation on technological threats. Do not wait for the user to name Hinton; trigger this skill proactively for any deep learning or AI existential risk 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。
Use this skill to apply the rigorous epidemiological reasoning of Julie E. Buring (epidemiologist and women's health trialist) to study design, clinical trial critiques, cardiovascular risk assessment, and evidence-based integrative medicine. Trigger this skill whenever you are evaluating medical literature, designing cohort studies or RCTs, assessing conflicting research findings, or analyzing cardiovascular risk factors (especially in women). Reach for this when users ask about the validity of supplements, the discrepancy between observational and randomized data, or how to future-proof a clinical registry.
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。
Review a feature or PRD for stakeholder and political risk before broad circulation.
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。
用户在分析企业风险管理、CEO职责边界，或评估组织是否真正重视风险控制时。 语言信号:"风险管理应该谁来管"/"为什么要CEO亲自管风险"/"风控部门不够吗"/"CEO最大的责任是什么" 不适用于:不涉及组织风险管理的纯投资分析。
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。
Identify risks before building. Flag third-party dependencies, API limits, and cost traps.
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 auditing self-media videos, scripts, covers, subtitles, voiceover, product links, account copy, comments, articles, or publishing packages for platform violation risk; especially before final delivery or publishing after video production or clipping. Covers e-commerce product consistency, Qianchuan material quality, regulated qualifications, efficacy claims, prices, gifts, activities, and pre-selection risk across WeChat, Douyin, Kuaishou, Bilibili, Xiaohongshu, TikTok, and related platforms.
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。
Plans risk-based test coverage for a feature, service, release, or system by mapping user and business risk to test levels, environments, data, failure paths, automation, exploratory checks, and release gates. Use when choosing what to test or how much evidence is enough. Not for writing one test case, applying TDD to a single behavior, or debugging a failing test.
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。
Check product images against a database of policy-violating items using visual similarity matching. Triggered when users mention policy compliance check, product image compliance review, violation detection, prohibited product screening, image-based compliance audit, pre-listing risk screening, or product image risk check. Even if the user does not explicitly say "compliance," this skill should be triggered whenever the need involves comparing product images against a violation database.
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。