当用户在面临投资/创业/职业选择,群体都在做一个方向但自己有疑虑、或在问"什么时候应该反着来""如何做出不同于大多数人的正确决策""共识什么时候是错的""怎么承受被误解的压力"时激活。 核心信号:用户在做一个与群体共识相反的决策,或者在考虑这样做。 不适用于:技术性问题、纯情感问题、不需要逆群体方向的常规决策。
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。
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概要と使いどころ
当用户在面临投资/创业/职业选择,群体都在做一个方向但自己有疑虑、或在问"什么时候应该反着来""如何做出不同于大多数人的正确决策""共识什么时候是错的""怎么承受被误解的压力"时激活。 核心信号:用户在做一个与群体共识相反的决策,或者在考虑这样做。 不适用于:技术性问题、纯情感问题、不需要逆群体方向的常规决策。
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 A/B tests with hypotheses, variants, sample sizes, and analysis plans. TRIGGERS - Use when user wants to run A/B tests, split tests, or experiment with variations.
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 running or interpreting VCSDD adversarial reviews. Provides calibration guidance for the Adversary agent, finding severity classification, and anti-leniency enforcement 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。
Performs financial ratio analysis, DCF valuation, budget variance analysis, and rolling forecast construction for strategic decision-making. Use when analyzing financial statements, building valuation models, assessing budget variances, or constructing financial projections and forecasts. Also applicable when users mention financial modeling, cash flow analysis, company valuation, financial projections, or spreadsheet 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 when a plan, feature, or launch needs an explicit adversarial pass to find weak assumptions, abuse cases, or strategic blind spots before execution.
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 financial statements (income statement, balance sheet, cash flow) with period-over-period comparison and variance analysis. Use when preparing a monthly or quarterly P&L, closing the books and need to flag material variances, comparing actuals to budget, building a financial summary for leadership review, or looking up GAAP presentation requirements and period-end adjustments.
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。
"Management accounting toolkit for internal decision support: ABC costing, variance analysis, transfer pricing, and responsibility accounting. Use for product profitability disputes, budget variance diagnosis, inter-division pricing design, and business-unit manager performance evaluation. Triggers: 『這個產品真的賺錢嗎』『成本分攤』『事業部之間怎麼算錢』『本月成本超支』『評核廠長績效』『內部轉撥』『ABC 作業成本』『差異分析』『責任中心』『管理會計』. For Taiwan EMBA cases, term reports, and management-decision simulations (台大/陽交/成大 財管必修).".
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 budget-versus-actual or forecast-versus-actual performance and turn finance data into a management-ready narrative using variance logic, drivers, cash impact, and forward actions. Use when finance, FP&A, or business leaders need commentary, management accounts, or performance explanation. Do not use for bookkeeping entries, tax filing preparation, or legal accounting sign-off.
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。
Smart contract and Web3 security audit skill for Solidity, Vyper, Move, and Cairo. Covers reentrancy, access control, oracle manipulation, MEV/sandwich exposure, signature replay, upgrade safety, ERC token edge cases, bridge risk, and DeFi invariants. Tooling includes Slither, Mythril, Echidna, Foundry, Halmo, Certora, and custom invariant tests. Use for design review, code audit, and incident analysis on contracts you own or are authorized to assess.
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。
Simulate supply chain and adversarial machine learning attacks by injecting poisoned data or targeted backdoors into training and fine-tuning datasets. Use this skill when assessing the integrity controls of MLOps pipelines or evaluating the resilience of AI models against highly targeted, stealthy manipulation intended to alter model behavior on specific triggers.
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 and simulate Data Poisoning attacks aimed at degrading or skewing an AI model's accuracy. This skill focuses on Adversarial Machine Learning concepts where attackers inject malicious or mislabelled data points into training or fine-tuning datasets (e.g., feedback loops) to bias the AI.
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 or revise Interpreter UI for non-developers with an ultra-minimal, utilitarian style inspired by the OpenAI website and ChatGPT, not the developer platform. Use when simplifying settings, onboarding, chat, forms, empty states, or helper copy; when removing extra containers, labels, or visual noise; when replacing technical wording with plain language; when clarifying hierarchy and making the main user action obvious; when making every visible label and status understandable to an average HR admin or other non-technical user; when hiding implementation details such as logs, IDs, endpoints, paths, or protocol names; or when a screen must respect the app's existing CSS variables and light/dark theme 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。
Orchestrate a whole-registry sweep of Gaia — fan out 13 parallel audit agents across every skill, run adversarial verification, surface Semantic Fusion candidates, propose new generic skill references, and synthesize a publish-ready HTML report under docs/meta/reports/. Use this skill for broad, systemic analysis across many skills at once: "run a meta sweep", "sweep the meta", "full meta audit", "audit the whole registry against META.md", "widespread nomenclature issues", "find all skills missing a GitHub link", "produce a meta report", "check for evidence type mismatches across the registry", "find skills that document a third-party tool with no attribution", or explicitly types /gaia-meta-sweep. This is the registry-wide macro companion to /gaia-meta-audit (a prioritized queue, single-pass) and /gaia-audit (fixes one skill). Use it when you need the full surface — 13 audit dimensions, adversarial verification, fusion proposals, and a durable findings artifact — not just a queue.
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。
Top-level orchestrator for the Gaia Skill Tree evidence verification pipeline. Run this when you want to do a full evidence pass — collecting raw sources, verifying live GitHub star counts, running Phase 2B benchmark-source verification, adversarially auditing for quality/formatting issues, and checking that every URL is reachable — all in one coordinated sequence. Trigger phrases: "run the evidence pipeline", "full evidence pass", "verify evidence", "evidence verification pipeline", "run ev-pipeline", "check all evidence", "audit the data lake", "evidence quality sweep", "refresh evidence", "validate evidence sources". Also aliased as /evidence-verification-pipeline. Use the individual sub-skills (ev-collection, ev-star-verification, ev-adversarial-audit, ev-link-validation) only when you need to re-run one phase in isolation; for end-to-end work, always start here.
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。
Copy-number variant analysis — detection from sequencing/arrays, segmentation, and clinical interpretation.
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, implementing, or reviewing Tailwind CSS design systems, token mapping, theme extension, utility class governance, component variants, dark mode, responsive utilities, safelists, or maintainable class composition; Chinese triggers include Tailwind, design Token, component variants, dark mode, class management.
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 task needs the judgment of a Chemical Plant and System Operator — reconciling a batch's mass balance to catch a leak or unaccounted reaction that individually normal readings would miss, responding to an early exotherm temperature trend before it accelerates, prioritizing simultaneous alarms by consequence severity rather than order of occurrence, or flagging a combination of near-limit variables that no single-variable check would catch.
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。
D-ZERO のアクセシビリティ規約。マークアップやインタラクションの実装・変更・レビューで、WAI-ARIA、代替テキスト(alt)、ランドマーク、見出し、キーボード操作、フォーカスを扱うときに使う。
Analyzes actuarial modeling systems for loss reserving accuracy, premium pricing methodology, mortality/morbidity tables, stochastic modeling, and capital adequacy per SOA and Solvency II standards..
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。
Scope a milestone to a fixed time window with variable scope. Use when the user says "scope this milestone", "what fits in the sprint", "cut scope for the release", "define the MVP for this milestone", "we need to ship by [date] what do we cut", "shape the work to fit the cycle", or needs to make hard scope decisions under a time constraint - even if they don't explicitly say "milestone scoping". Based on "Shape Up" by Ryan Singer (appetite, shaping, betting table) and "The Art of Project Management" by Scott Berkun (milestones as forcing functions, scope as a variable).
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 Agentforce conversations that span multiple turns without losing context: session variable scoping, conversation memory, clarifying-question patterns, topic-to-topic (now subagent) handoff, and the right abstractions for accumulating state across turns. NOT for deciding the topic boundaries themselves or out-of-scope behavior — use agentforce/agent-topic-design. NOT for single-turn agent actions and their input/output contracts — use agentforce/agent-actions.
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。
Comprehensive calculus expert covering limits, derivatives, integrals, sequences, series, and multivariable calculus with rigorous proofs and practical applications
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。
The GraphQL pipeline for SKMTC generators — authoring generators whose input schema is GraphQL SDL rather than OpenAPI. Covers `toGqlOperationEntry`, `GqlOperation`, `synthesizeArgsObject` (mutation args -> object schema), the GQL enrichment routing (`[id][rootKind][fieldName][variant]` — two nested subject keys where OAS has path+method), the `to<Lang>GqlOperationProjectionBase` companion factories, and the `GeneratorKey` shape `id|rootKind|fieldName|variant`. Use this skill ALONGSIDE `skmtc-generator` whenever the schema source is GraphQL SDL or the task mentions "GraphQL", "SDL", "GqlOperation", "toGqlOperationEntry", or GraphQL query/mutation generators. Engine rules (producers, register/insert, the axioms) stay in `skmtc-generator`; this skill carries only what differs for GraphQL.
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。
Adversarial project planning workflow. Explores the problem space, drafts tickets in batches, then runs sequential adversarial loops — a mandatory UX loop ("should we build this?"), zero or more discretionary specialist loops (security, performance) for projects with architectural implications in those domains, and a mandatory implementer loop ("could we build this?") — to find gaps before implementation. Creates well-specified, batch-tagged tickets upstream only after every applicable loop signs off.
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。