Results for “feasibility”

Use when main results pass result-to-claim (claim_supported=yes or partial) and ablation studies are needed for paper submission. Codex designs ablations from a reviewer's perspective, CC reviews feasibility and implements.

Build reproducible ML experiment plans with hypotheses, metrics, and ablations. Use when: (1) planning experiments, (2) comparing variants, (3) defining acceptance thresholds. NOT for: long-running experiment execution.

List Available Fabric Capacities

Evaluate a raw app, startup, SaaS, internal tool, marketplace, or AI product idea before planning features. Use when the user shares a vague concept and needs pain analysis, market realism, feasibility, differentiation, risks, or a build/no-build recommendation.

当需要在众多机会中筛选出投入产出比最高的选项，以最小化风险并最大化潜在回报时

当完成一个创作（文章、产品原型等）后，因自我怀疑或追求完美而犹豫是否发布时

当需要在多个领域积累独特竞争优势，避免单一技能风险时

当个人希望提升自身在职业或社会中的不可替代性，以应对环境变化和不确定性时

Strategic patterns for solving intractable problems through cascading approximation, self-improvement, and heterogeneous evaluation from DeepMind's AlphaGo system

Multi-phase dialectical stress-test for HIGH-STAKES decisions only (architecture choices, irreversible product calls, strategic bets). Heavy-cost skill — do NOT use for routine questions, brainstorming, or simple tradeoffs. User must explicitly invoke or describe a decision they call "high-stakes", "irreversible", or "needs stress-testing". Triggers: "stress test this decision", "dialectic on", "challenge this thesis", "should I really".

Run structured What-If scenario analysis with 4-6 branch possibility exploration (best, likely, worst, wild card, contrarian, second-order). Use for speculative questions about uncertain futures, strategic forks, contingency planning, or stress-testing decisions.

Mental models and decision frameworks for building autonomous agents that continuously learn, explore, and accumulate skills in open-ended environments without human supervision

在启动高风险项目或重要决策前，需要预先识别潜在失败点以增强心理韧性和方案鲁棒性时

Avalia riscos e oportunidades do caso civel e sugere os proximos passos processuais. Pondera probabilidade de exito, onus da prova, custos/beneficios de acordo x litigio, momento de tutela de urgencia, escolha de pedidos e de via (conhecimento x cumprimento), e calibra a postura. Use quando o operador disser qual a estrategia, vale a pena recorrer, devo fazer acordo, quais os riscos, proximo passo, ou pedir analise estrategica do caso.

Brainstorm a longlist of benefits and costs for a project. Outputs two clean tables (benefits and costs), each with materiality (H/M/L), a cash flow tag (cash in / cash out / non-cash), a quantification method, and a monetisation method. Supports HMT Green Book, EU Better Regulation Guidelines, World Bank, Asian Development Bank, and the Victorian Treasury High Value High Risk (HVHR) framework.

Apag Advantage Framing

Write a rigorous, decision-grade experiment spec — falsifiable hypothesis, primary metric, guardrails, randomisation unit, exposure definition, method (A/B, holdout, switchback, quasi-experiment, MAB), MDE/duration plan, peek policy, validity threats, and pre-committed decision rule. Platform-agnostic. Load when the user has a candidate experiment and needs to spec it before launch, or says "spec this experiment", "write the test plan", "design this A/B test", "what's the hypothesis", "how big a sample do we need", "how long should we run this", "define the metrics for this test", or when the experimentation orchestrator routes here.

Validate Pleasur.ai Stage 01 blog-keyword candidates with the Business potential, Intent, and Difficulty method using current Ahrefs evidence. Use before AIO vetting and prioritization to reject poor business fits, mismatched search intent, and SERPs the brand cannot realistically win.

当需要设定人生方向或进行重大行为改变时

Graceful degradation when primary approach fails. Try alternates in priority order; record which path succeeded for future routing.

Defines a testable hypothesis with clear success metrics and validation approach. Use when forming assumptions to test, designing experiments, or aligning team on what success looks like.

A/B test design and experiment planning for paid advertising. Structured hypothesis framework, statistical significance calculator, test duration estimator, sample size calculator, and platform-specific experiment setup guides (Meta Experiments, Google Experiments, LinkedIn A/B). Use when user says A/B test, split test, experiment design, test hypothesis, statistical significance, sample size, or test duration.

Rice

Prioritize features for an MVP or first release. Use when the team has too many ideas and needs must-have vs later vs never-for-v1 decisions tied to the core value loop, user pain, operational shortcuts, and launch feasibility.

Turns rough ideas into approved, decision-complete plans with validated structure before coding. Use when users ask in any language for planning, architecture, design direction, feasibility, value judgment, or whether a feature is worth doing before implementation. Not for bug fixes or small edits.

Use after Cell reviews arrive to triage the decision, prioritize the (often substantial) new experiments by impact × feasibility, and draft a point-by-point response that is respectful, evidence-led, and closes the "completeness" gaps reviewers demand. Do not run before the experiments and revision are actually done.

Sharpens and stress-tests an existing plan file through multiple codebase-grounded review passes, editing it in place and recording every finding and iteration in cross-referenced companion files. Use this skill whenever the user wants to iterate on, refine, tighten, or improve a plan. Also use it when the user asks to verify, validate, or confirm feasibility of an approach. Does not implement plan steps, write test plans, review code, or investigate bugs, and does not generate new plans from scratch — use plan-a-feature for a new plan. Runs its review rounds to completion without pausing between them; to review each round as it lands, use pairing.

Score an unbuilt business idea on desirability, viability, feasibility, distribution wedge, why-now, founder-market-fit, market size, alternatives, defensibility, capital intensity, and regulatory/ethical risk — and return a GO / ITERATE / KILL verdict with kill criteria and a next kill test. Load when the user asks to evaluate a business idea, score a startup idea, screen an idea, decide whether to pursue this venture, do an idea review, or says "is this a good business idea", "should I build this", "evaluate this startup", "screen this idea", "go/no-go on this idea", "kill or pursue". Sub-skill of `venture-exploration`. Calls `fermi` for sizing, `assumption-mapping` for hidden beliefs, optional `pre-mortem` / `adversarial-hat` for high-stakes ideas. Does NOT evaluate built products — for that use `reality-check`.