「assumption」の検索結果

Analyze sales pipeline, historical revenue, conversion rates, and assumptions to produce forecast scenarios. Use for sales reviews, RevOps planning, and founder revenue forecasting.

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Turn ambiguous requests into an executable implementation plan with assumptions, sequence, acceptance checks, and rollback thinking.

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First-time onboarding — asks where you are, then guides you to the right workflow. No assumptions.

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Use when frontend decisions depend on framework, library, browser, runtime, package, API, or platform behavior that may have changed, including official documentation checks, version-sensitive patterns, migration choices, or public interface assumptions; Chinese triggers include Check official documentation, source code shall prevail, source-driven, version differences.

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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.

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Analyze thermal-fluid engineering systems, experiments, CFD, and correlations with explicit assumptions, validity limits, uncertainty, and mechanism-based interpretation. Use for heat transfer, fluid mechanics, phase change, HVAC, energy systems, turbomachinery, piping, or thermal management.

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Use when a task needs the judgment of an Economist — estimating a demand elasticity or causal treatment effect from observational or experimental data, building a cost-benefit or welfare analysis for a policy or pricing decision, forecasting a macro or market aggregate under stated assumptions, or stress-testing whether a "the data shows X" claim survives a confounding-variable check.

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Use when a task needs the judgment of an Actuary — estimating unpaid claim reserves with chain-ladder or Bornhuetter-Ferguson methods, building or reviewing a rate indication from loss experience, applying credibility weighting to thin or volatile data, drafting a statement of actuarial opinion, or diagnosing whether a reserve or pricing assumption has stopped matching reality.

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Audit an existing mathematical, logical, algorithmic, or technical proof from its stated conclusion backward as a bounded AND/OR graph of exact obligations. Use to reconstruct conclusion dependencies, determine whether a proof-supplied route establishes the conclusion, or expose hidden assumptions, unmet side conditions, cycles, overly strong steps, or silent route switches. For a source-mapped Lamport rendering use convert-lamport, and for forward hierarchy, construct, and scope conformance use forward-lamport before this skill. This does not decide full theorem-level entailment by searching for a different proof; do not turn the audit into a linear reversed proof, exhaustive proof search, or replacement proof.

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Invoke a Rubber Duck Reviewer subagent to independently critique plans and implementations before proceeding. Use when the agent is about to implement a non-trivial plan (multi-file changes, architectural decisions, security-sensitive logic, database schema changes), after completing a self-contained unit of work (module, endpoint, feature), when stuck or facing repeated failures (same test fails 2+ times, unexpected results), or when the agent wants independent validation of assumptions and design decisions. Triggers on any non-trivial implementation task where independent critique would catch blind spots before they become costly mistakes.

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Use when challenging ideas, plans, decisions, or proposals using structured critical reasoning. Invoke to play devil's advocate, run a pre-mortem, red team, or audit evidence and assumptions.

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Generate an institutional-grade commercial real estate underwriting model — input schema (T-12 income, T-3 trailing, rent roll, debt terms, exit assumptions), calc engine (Cap Rate, NOI, Cash-on-Cash, IRR, DSCR, Debt Yield, ROI, Equity Multiple, levered & unlevered returns).

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Design product experiments to test hypotheses. Use when the user says "run an experiment", "A/B test this", "build-measure-learn", "validate this assumption", "test this hypothesis", "we don't know if this will work", "minimum viable test", "lean experiment", or wants to reduce risk before fully building a feature - even if they don't explicitly say "experiment".

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Identify, score, and mitigate delivery risks for a program or release. Use when the user says "risk review", "what could go wrong", "risk register", "delivery risks for this release", "program risk assessment", "identify blockers before they hit", or wants to pressure-test a plan before committing - even if they don't say "risk review". Based on "The Phoenix Project" by Kim, Behr & Spafford (unplanned work, fragile systems, single points of failure) and "The Art of Project Management" by Scott Berkun (scoping risk, milestone pressure, hidden assumptions).

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Use when the user says "run customer discovery", "help me interview customers", "write customer interview questions", "validate my assumption", "talk to users", "synthesize customer interviews", "what are my customers saying", "user research for my startup", "mom test interview", "are we solving a real problem", or wants to extract real insight from conversations with potential or existing customers.

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**WORKFLOW SKILL** — Understand before implementing. USE FOR: any task where the agent might rush to code — forces understanding WHY/WHAT FOR/FOR WHOM before defining a solution, activates planning and reasoning discipline, prevents technical debt from shallow requests. Use to validate intent, plan before coding, reason before acting, challenge assumptions, prevent premature implementation. DO NOT USE FOR: deep multi-technology analysis (use contextação), persisting decisions across tasks (use task-map).

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**WORKFLOW SKILL** — Structured context analysis before acting. USE FOR: questioning assumptions, mapping dependencies, identifying blind spots, validating scope of any task or subject. Use to contextualize, question, analyze before implementing, review premises, prevent hallucination, structured assessment. DO NOT USE FOR: simple tasks where task-intent suffices, direct implementation, code review.

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Apply the Fable-derived 10-stage OBSERVE, OBSERVE, LISTEN, THINK, CONNECT, CONNECT, FEEL, ACCEPT, CREATE, GROW loop to consequential or ambiguous reasoning and decisions. Use for think this through, deep think, architecture review, postmortem, tradeoff analysis, or challenges to assumptions. This is a reasoning review, not a deterministic vault health check or repair workflow.

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Design and evaluate paid-ad experiments with hypotheses, randomization units, sample-size and duration assumptions, guardrails, platform experiment tools, analysis, and decision rules. Use for A/B test, split test, experiment design, hypothesis, statistical significance, sample size, test duration, or experiment readout.

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。

Flip core assumptions to reveal hidden constraints and alternative approaches - "what if the opposite were true?"

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Good-faith divergent brainstorming for achieving a goal. Validates assumptions, then spawns brainstormers running different techniques (first-principles, working-backwards, lateral, analogical, constraints-shift, etc.) in isolation, and synthesizes the pool into a report of standouts, reasonable ideas, and hybrid combinations. Produces feedback only — no code, no tickets, no artifacts.

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Stress-test a plan, decision, or idea as a dependency-aware design tree. Use for grill-me requests, assumption audits, and decision clarification before implementation.

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Use when a problem requires structured step-by-step reasoning before acting. Prevents jumping to conclusions or guessing root causes without evidence. Triggers on complex debugging, multi-step analysis, hypothesis verification, or when mk:fix invokes diagnosis phase. NOT for being stuck on approach (too many implementations, forced assumptions, innovation block) — use mk:problem-solving instead.

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Use when stuck on approach (not debugging). Strategic unsticking via 7 non-default techniques: simplification cascades, collision-zone thinking, meta-pattern recognition, inversion, scale game, first principles, via negativa. Triggers on complexity spiraling, innovation block, recurring patterns across domains, forced-assumption solutions, scale uncertainty, told-it's-impossible, or bloated systems needing subtraction. For evidence-based root-cause debugging, use mk:sequential-thinking instead.

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。