Claude Skills by Pilot2Service

Collects sufficient structured input on an identified opportunity through well-designed questions — even when the client can't yet frame their idea in business terms.

Places an identified opportunity in an attractiveness × feasibility matrix and assesses it from seven commercialization angles — the owner's own, productized assessment framework.

Connects loose observations into a meaningful opportunity by identifying analogies across industries and situations using the Capability Pattern Mapping method: superficially different cases are abstracted into one named capability pattern, which then serves as a diagnostic question in a new context. Use when an opportunity has no obvious precedent in your own industry and a same-industry case search comes up empty.

Builds and delivers a demo following the Great Demo! methodology (Situation Slide, critical business issue/CBI, \"do the last thing first\"/inverted pyramid): Discovery → Demo Prep → Demo Delivery → Documentation → Debrief. Use when a prototyped solution needs to be presented to a customer or leadership so that it genuinely convinces rather than just showcasing features.

Frames a demo/prototype/PoC for the customer before presenting it with the right term (PoC vs. Pilot vs. MVP) and the right promise — what this demo PROVES, what it does NOT prove, and what happens next if it succeeds. Use before every demo or PoC presentation, especially when there's a risk the customer will over-interpret the demo as production-ready or as automatically progressing to production.

Translates demo/PoC results into business-case-ready ROI inputs: separates technical performance metrics from business impact metrics, tests whether a PoC-scale result scales responsibly to production scale, and checks whether the assumed ROI mechanism fits the customer's actual organizational structure. Use immediately after a successful demo/PoC, before its results are fed into a business case or ROI calculation.

Communicates and shows an AI opportunity using Amazon's Working Backwards method before anything has been built: writes a short future-dated press release (PR) and a question-and-answer section (FAQ) describing what the finished solution would look like from the customer's perspective. Use when an opportunity needs to be made concrete and discussable before prototyping, or when a prototype isn't yet feasible/worthwhile but the vision still needs to be communicated convincingly.

Builds a fast, working, credible-enough prototype to prove an AI opportunity using AI-assisted coding (\"vibe coding\") — tool selection by task type, a tight prompt/review/test/commit iteration cycle, the right fidelity level, and known risks (hallucinated interfaces, auth gaps). Use when you need a fast, proof-capable prototype before committing to a bigger build.

Writes a PRD (Product Requirements Document) as a work order for an AI build agent — problem, vision, core features, scope boundaries, and success criteria — plus supporting documents and a build plan.

Ideates 3 distinct AI-native solution directions for the chosen AI differentiator need (AI wedge) through three lenses — competitor, future, and connect-the-dots — so the team doesn't fall in love with the first idea.

Designs the conversational UI architecture for an AI-native product in six stages — Intent, Strategy Cards, Clarification, Output Cards, Mission, Agent Execution — applying five AI-first product principles (click > question, menus > prompts, dashboards > dialogue, manual actions > agents, screens > chat + cards).

Uses an agentic/closed-loop lens to find genuinely new AI-enabled business opportunities in your own startup case, and scores/prioritizes the findings by feasibility and the smallest prototypeable version.

Selects the smallest workable AI-native tool stack using a 12-category decision tree (thinking partner, research, design, app builder, coding agent, hosting, backend, skills, project management, notes, automation, agent building) — category-based, not tied to product names.

Structures business processes as open or closed loops and decides the human's role (in-the-loop / on-the-loop / outside-the-loop) in each — a mental model for designing AI agents' areas of responsibility.

Structures a preliminary, free-form business vision into a customer profile (ICP), a Jobs-To-Be-Done analysis, and 5+2 need themes, scores them with a 5-criteria NMB+AI-advantage model, and selects an AI differentiator need (AI wedge) for further development.

Scores multiple solution directions with the RICE model (Reach, Impact, Confidence, Effort) to select an MVP, and turns the choice into an MVP definition, a one-sentence positioning statement, and three 'why we win' claims.

Isolates the one tiny interaction that is a product's actual superpower, stress-tests whether it's good enough to carry the product on its own, and enforces a 'no more features' veto until that core is obviously, unmistakably good. Use after an MVP direction is chosen and before or during scoping, as a discipline against the 'just one more feature' trap that AI-cheap building makes especially tempting.

Starts a BMC session from an AI-generated first-draft canvas and brand audit instead of a blank page — the team's real work becomes editing, rejecting, and enriching the draft rather than generating it from nothing, which is faster and surfaces disagreement earlier.

Uses AI-moderated interviews to run customer discovery conversations at a scale no human team could match — hundreds of short interviews in parallel — then treats the aggregate as a filter: mines it for the most interesting outliers and patterns, and follows up on those personally.

Reads a new business model for organizational-rejection triggers — margin conflict, sales-process conflict, cannibalization fear — inside the client's own organization, and designs a protected sandbox so internal 'antibodies' don't kill the experiment before it's tested.

Identifies five of the expert's own BMC-work antipatterns (e.g. too much content per block, too deep too early, canvas as a strategy document) and four of the most common client misunderstandings about the BMC's role, and offers direct corrective moves for each.

Guides BMC work with a variation logic aimed at clarity over depth: builds 2-3 alternative canvas variants, identifies when a team is stuck on a single canvas, and assesses canvas readiness using the expert's three criteria.

Reads a finished BMC canvas as a diagnostic instrument using seven research-based rules (Hook Rule, value-cost consistency, segment specificity, evidence grade, defensive canvas, missing block, gravity/lock-in check) and a four-dimension quality rubric.

Puts a real cost-to-acquire against every Channel on the canvas and checks it against the company's actual budget, payback period, and cash runway — so channels get chosen on economics, not just descriptive fit or trend appeal.

Interprets typical client statements about BMC work ('we need to update our business model', 'we've already done a BMC') as strategic signals, and corrects three of the most common conceptual misunderstandings: value proposition as a feature list, customer segment as demographics, revenue stream as pricing.

Forces pricing, unit economics, and revenue-model assumptions into a live, testable form at the start of BMC work — a dynamic numbers prototype and a willingness-to-pay test — instead of leaving Revenue Streams and Cost Structure as an afterthought.

Decides HOW to test a BMC hypothesis based on real build effort — build and test immediately if a working version is achievable in about two weeks, otherwise choose a cheaper proxy method (landing page, clickable mockup, Wizard of Oz, pre-order) instead of starting to build.

Defines a one-sentence 'hunting zone' — megatrend × owned or acquirable assets × attractive customer segment — before sketching any BMC, so bottom-up business model experimentation stays bounded and comparable instead of producing scattered, unrelated one-off canvases.

Identifies and justifies 3-5 compatible business model innovation patterns (from the public 159-pattern innovation library) for a given business context, using the expert's own four-part innovation taxonomy (Financial/Operative/Value-based/Experience Model Innovations).

Reads Key Partners, Key Activities, and Key Resources as an operational risk and third-party access surface — not just an operating description — to catch vendor-access, concentration, and bus-factor vulnerabilities that standard BMC facilitation misses.

Finds people who professionally interact with your target customer's Jobs, Pains, and Gains every day — salespeople, support staff, brokers, consultants adjacent to your category — and interviews them as a fast, cheap, pattern-rich validation source before or alongside direct customer interviews.

Scores each Revenue Stream and its matching Customer Relationship on a -3 to +3 resilience scale — recurring/contractual vs. one-off/unpredictable — instead of raw size, to reveal how fragile a model's top line actually is before it's mistaken for strength.

Builds the BMC as a structured set of open, falsifiable questions per block instead of filled-in answers, and distinguishes 'we lack data' gaps from 'we haven't interpreted contradictory signals' gaps — shifting effort from collecting more information (cheap, AI-abundant) to asking better questions (the actual scarce skill).

Designs the structure of a BMC workshop using research-based heuristics: choosing a starting point, fill-in order, session length/team composition, when the canvas isn't yet finished, evidence color-coding, and presenting the canvas to stakeholders.

Tests whether a business model can win by deliberately moving up (automating what's manual) or down (stripping out over-engineered technology) the technology-sophistication axis of Key Resources, Key Activities, and the Value Proposition — the win comes from business-model fit, not the tech level itself.

Decides when to move from the BMC to another tool (Value Proposition Canvas, Lean Canvas, Mission Model Canvas, financial modeling, multiple parallel canvases) and assesses the quality of the canvas, hypothesis, and segment using three decision criteria before moving to the testing phase.

Screens a raw AI idea for public-sector fit before it enters formal opportunity scoring: checks mandate alignment, identifies which type of public value it targets, and runs the 'would this survive being on the front page' test. Use when a public-sector or non-profit client brings an AI idea and you need to know if it's worth taking to full scoring.

Checks an AI-enabled public service design for differential impact across community groups before launch: digital divide, accessibility, and disparate-treatment risk. Use before a public-facing AI service goes live, or when a proposal claims a general improvement and hasn't yet been checked group by group.

A six-element model for presenting an AI proposal so a public decision body (council, board, steering group) can actually approve it: options, relevancy and focus, trust, urgency, strategic alignment, and public-sector decision dynamics. Use when preparing to bring an AI initiative to a public decision-maker for a go/no-go or funding decision.

Flags when an AI idea has crossed from 'just build it' into public-procurement or public-funding territory, and what that changes about timeline, vendor choice, and design freedom. Use when scoping or building an AI solution for a public body, before committing to a build-vs-buy path or a delivery timeline.

Reframes an AI business case around the four public-value types (efficiency, service quality, equity, trust/legitimacy) instead of defaulting to a private-sector ROI story, and shows how to combine cost-avoidance and non-financial value in one case a public decision body can act on. Use after opportunity screening, before or alongside building the full business case.

Frames the distinct regulatory and ethical stakes of public-facing AI - higher transparency and accountability bar, disparate-impact risk, due-process concerns when AI touches decisions about individuals - and identifies when a question needs real regulatory or legal expertise rather than general reasoning. Use early when scoping any AI system that will touch citizen-facing decisions or services.

Extends power/interest stakeholder mapping with the actor types specific to public-sector projects (elected officials, civil servants, unions, oversight bodies, citizens) and their distinct veto points and time horizons. Use when scoping an AI initiative for a public body and 'who's the sponsor' isn't a single, simple answer.

Choose your own role in a spin-out (full-time founder, scientific advisor, part-time) and manage balancing an academic career with entrepreneurship.

Structure the entire commercialisation journey into five stages and build an actionable roadmap that ties stage gates to agile iteration.

Help a researcher or team assess whether the idea and team are ready to begin commercialising — from a learning-speed, not perfection, perspective.

Work through a structured 10-area / 76-item self-assessment (AFCA) of a founder's or team's readiness for an academic spin-out or research-based startup.

Build a balanced founding team and agree the rules of engagement (Founders' Agreement) before disagreements arise.

Build a funding strategy that combines non-dilutive and dilutive funding in the right order, and manage runway.

Bring industry partners into commercialisation early so the product and business logic stay grounded in reality.