108 件 ・ 関連度順
概要と使いどころ
Evaluates ML applications in investment with feature engineering, model selection, and implementation considerations for alpha generation. Use when evaluating ML for investing, designing ML pipelines, or assessing ML strategy feasibility.
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 take-private feasibility with premium analysis, financing capacity, governance considerations, and regulatory requirements. Use when analyzing take-private opportunities, modeling go-private premiums, or assessing delisting mechanics.
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。
Validate whether a proposed new artifact is feasible in the current stack before creator workflows run.
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。
当用户想系统评估一个AI项目是否值得投入时使用，结合概念简单性和数据充足性两个维度。 用户在问"这个想法可行吗"、"我们需要多少数据"、"技术上能做到吗"时激活。 不适用于: 纯数据科学洞察项目、探索性分析、已明确不需要AI的场景。
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。
Throwaway experiments to validate feasibility before building.
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。
Reviews construction plans for architecture consistency and implementation feasibility. Use when reviewing implementation plans before approval.
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。
Provide proven marketing strategies and growth ideas for SaaS and software products, prioritized using a marketing feasibility scoring system.
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。
Apply behavioral science and mental models to revenue (cuan) decisions, prioritized using a psychological leverage and feasibility scoring system.
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。
Phase 0 validation gate — problem framing, competitive analysis, feasibility, and a go/no-go checkpoint before committing to a feature. Use when evaluating whether an idea is worth building, before /story or /design. Trigger on "is this worth building", "validate this idea", "discovery", "should we build".
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 feature requirements, dependencies, and security considerations. Use when starting feature implementation from GitHub issues to understand scope, technical feasibility, and risks.
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。
Scaffolds a structured investigation in scratch/ for empirical research and documentation. Use when the user says "start an investigation" or wants to: trace code paths or data flow ("trace from X to Y", "what touches X", "follow the wiring"), document system architecture comprehensively ("document how the system works", "archeology"), investigate bugs ("figure out why X happens"), explore technical feasibility ("can we do X?"), or explore design options ("explore the API", "gather context", "design alternatives"). Creates dated folder with README. NOT for simple code questions or single-file searches.
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。
Marketing analytics and data-driven optimization for B2B SaaS campaigns. Use this skill when analyzing marketing performance, optimizing conversion rates, running A/B tests, conducting customer research, creating marketing dashboards, building attribution models, analyzing funnel metrics, or storytelling with data. Also triggers on: marketing ops, performance analytics, CRO, A/B testing, data storytelling, customer insights, tracking plan, event taxonomy, UTM taxonomy, GA4 audit, consent mode, instrumentation contract, sample size calculation, minimum detectable effect, test duration, not enough traffic to A/B test, statistical significance, Bayesian vs frequentist, sample ratio mismatch, experiment feasibility, martech stack, marketing tech stack audit, tool consolidation, build vs buy, vendor evaluation, shadow IT tools, ESP/CRM/platform migration, switching cost, cutover plan, suppression list migration, data portability, customer interviews, survey design, sampling frame, non-response bias, how many people should I interview, thematic saturation, voice of customer, jobs-to-be-done, buyer persona research, is this finding real, data enrichment, which enrichment provider should we buy, waterfall enrichment, contact data quality, match rate, our list data is bad, email verification, catch-all domain, accept-all, bounce rate from bad data, B2B data decay, how often should we re-verify contacts, ZoomInfo vs Apollo vs Clearbit evaluation, cost per usable record, where did this list come from, GDPR Article 14 notice, purchased list compliance, suppression screening, TAM list building, demand planning, marketing plan, how many leads do we need, what pipeline target should marketing carry, funnel model, top-down vs bottom-up plan, plan of record, annual marketing planning, quarterly planning, pipeline coverage we have to create, sales cycle lag, our leads cannot close this quarter, carry-in pipeline, scenario planning, conservative moderate aggressive forecast, stretch target, reforecast, re-forecast triggers, assumption register, capacity ceiling, addressable audience ceiling, marginal cost per lead, blended CPL is wrong, we missed plan and nobody knows why, AI policy for the marketing team, AI governance, AI acceptable use policy, which AI tools are we allowed to use, can I paste customer data into ChatGPT, shadow AI, AI use register, human review of AI output, when can we stop reviewing every AI draft, can the agent send on its own, the AI made a mistake now what, pull back AI autonomy, AI evals, our AI vendor changed the model, AI output went out wrong, prompt injection, can our AI read prospect replies safely, lethal trifecta, the AI leaked internal notes, turn off auto-generated ad assets, auto-applied recommendations, AI literacy training, EU AI Act Article 4, duplicate records in the CRM, dedupe before import, which value wins in a merge, CRM cleanup.
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 an Environmental Restoration Planner — screening or comparing CERCLA/RCRA remedial alternatives against the nine evaluation criteria, building a life-cycle cost estimate for a Feasibility Study, scoping a stream or wetland restoration design against a reference reach, sizing compensatory mitigation credits, drafting a Statement of Work or Remedial Action Work Plan, or scoping a Five-Year Review for a site with land-use controls in place.
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 Credit Counselor — calculating debt-to-income ratio and disposable income to test debt management plan (DMP) feasibility, comparing DMP versus debt settlement versus bankruptcy for a specific client's financial situation, negotiating and applying creditor interest-rate concessions, selecting a debt payoff order matched to a client's follow-through pattern, or providing federally required pre-bankruptcy credit counseling.
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 Computer Systems Analyst — running requirements elicitation and gap analysis against a target system, writing a feasibility study for a build-vs-buy-vs-customize decision, designing a system integration approach (point-to-point vs middleware vs API-led), scoping data-migration risk before a cutover, or choosing between phased and big-bang implementation 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。
Use when a task needs the judgment of a climate change policy analyst — evaluating a proposed emissions rule's cost-benefit case, comparing carbon-pricing instruments (tax vs. cap-and-trade), drafting regulatory comment letters or legislative issue briefs, or assessing whether a climate proposal survives legal/political feasibility review.
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 an astronomer — calculating exposure time or signal-to-noise for a target, writing the feasibility math for a telescope-time proposal, reducing photometric or spectroscopic data, distinguishing a systematic error floor from statistical noise, or reviewing an observational result for calibration or upper-limit red flags.
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 an active or proposed trip needs planning, logistics, feasibility, entry or transit checks, itinerary work, or investigation of an airport process, immigration, ground transport, a transfer, or fast-track service, even for a narrow question with no booking intent. Route a bounded flight, hotel, restaurant, event, or other item ready for live availability or booking directly to Booking. Skip this skill for a stable travel fact alone or flight status.
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。
Qualitative quality review of game design documents — answers "is this design good, and does it follow best practices?" — across all three GDD axes: systems, flows, and content types. Fans out adversarial lenses (completeness, clarity, pillar alignment, systems-math, fantasy-delivery, feasibility, scope) plus domain lenses that check it against the game-design rules, each prompted to find problems rather than validate; produces a severity-graded verdict and a two-bucket report (apply-ready fixes vs research), handed to /unikit-gd-apply or /unikit-gd-explore in one move. Scope is inferred: a named system, flow, or content type reviews that document; "all" runs a cross-document review. Use when the user wants a critique, quality judgment, or rules check of the design, e.g. "review the combat GDD", "review the onboarding flow". This is the subjective quality pass — for a mechanical consistency check (numbers, terms, IDs, references matching the registry) use /unikit-gd-verify.
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 Amazon Ads search terms ready for exact-match harvesting from auto, broad, phrase, or other discovery campaigns. Use when Codex needs standalone or Rocketcart MCP-aware search-term harvesting: classify terms by harvest readiness, resolve live IDs, choose safe destination campaigns/ad groups, preflight duplicates/current negatives/destination feasibility, decide whether source negatives are justified, execute only explicitly approved rows, and read back/monitor Sponsored Products or Sponsored Brands actions without unsafe negatives, duplicate routing, stale approvals, brand-defense cuts, own-ASIN-defense cuts, launch/rank mistakes, or low-sample overfitting.
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 planned deployment or release diff for rollback feasibility, mixed-version compatibility, migrations, and configuration readiness without deploying it.
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。
Guides selection of the optimal confirmatory test (CCT, SIT, FUT, OSLT) for primary aldosteronism based on patient safety and feasibility. Triggers include when a clinician asks “Which confirmatory test should I use?” or evaluates patient comorbidities before testing.
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。
Plan multi-waypoint journeys with route optimization, feasibility analysis, and time budget constraints.
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。
Data Scientist role within the AID methodology. Activate when working on: EDA, feasibility assessment, experiment planning, ML architecture, model development & validation, or production deployment & monitoring. Stack-agnostic — ask the user for their stack at the start of any new project.
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。