discovery-call-prep
$
npx mdskill add mohitagw15856/pm-claude-skills/discovery-call-prepProduces a complete discovery call brief — research summary, call hypothesis, structured questions, and success criteria — so every call starts with context and ends with a clear next step.
SKILL.md
.github/skills/discovery-call-prepView on GitHub ↗
--- name: discovery-call-prep description: "Prepare a structured discovery call plan for any prospect. Use when asked to prepare for a sales call, discovery call, prospect meeting, or first call with a potential customer. Produces a call brief with research, hypotheses, questions, and success criteria." --- # Discovery Call Prep Skill Produces a complete discovery call brief — research summary, call hypothesis, structured questions, and success criteria — so every call starts with context and ends with a clear next step. ## Required Inputs - **Prospect company name** - **Contact name and role** - **Any known context** (how they found you, prior interaction) - **Your product/solution** (one line) - **Call duration** (15 / 30 / 45 / 60 min) ## Output Structure --- # Discovery Call Brief **Prospect:** [Company] | **Contact:** [Name, Title] | **Duration:** [X min] --- ### Research Summary - What they do: [Product/service, customer, business model] - Size: [Headcount, revenue if public] - Stage: [Startup / Scaleup / Enterprise] - Recent news: [Funding, launches, leadership changes — last 90 days] - Contact background: [Role tenure, previous companies, LinkedIn activity] - Likely priorities for someone in this role: [Based on title and stage] --- ### Call Hypothesis Before the call write your best guess: - **Their most likely pain:** [What someone in this role at this company probably has] - **Why they would care about us:** [Specific connection to your value] - **Biggest risk to the deal:** [What might make this not a fit] Write it down — then test it on the call. --- ### Call Agenda "Here is what I was thinking for our [X] minutes: - 2 min: Quick intros - [X] min: Learn more about your situation - [X] min: Share how we have helped similar companies - 5 min: Next steps Does that work? Anything specific you would like to cover?" --- ### Discovery Questions Open with context (not a pitch): - "What prompted you to take this call today?" - "What does [relevant area] look like for you at the moment?" Go deeper on pain: - "How long has [problem] been an issue?" - "What have you tried to solve it?" - "What is the impact of not solving this?" Understand buying context: - "Who else would be involved in a decision like this?" - "Have you looked at other solutions?" - "Is there a reason you are exploring this now?" Qualify on budget: - "Have you set aside budget for this kind of initiative?" Close discovery: - "Based on what you have told me, it sounds like [summary]. Is that right?" --- ### Success Criteria This call is successful if we leave with: - Understanding of specific pain and business impact - Knowledge of buying process and key stakeholders - A clear agreed next step (demo / proposal / intro) - Sense of timeline This call is NOT successful if we only pitched and got "sounds interesting, send me some info." --- ### Suggested Next Step "Based on what we discussed, the logical next step would be [specific]. Does [day/time] work?" ## Quality Checks - [ ] Research summary includes recent news (last 90 days) — not just LinkedIn bio - [ ] Call hypothesis is written before the call (not post-rationalised after) - [ ] Discovery questions progress from context → pain → business impact → buying process - [ ] Success criteria define what "not successful" looks like (not just the ideal outcome) - [ ] A specific next step is proposed (not "let's stay in touch") ## Example Trigger Phrases - "Prepare me for a discovery call with [company/contact]" - "Build a call brief for my meeting with [name] at [company]" - "What questions should I ask in a discovery call for [use case]?"
More from mohitagw15856/pm-claude-skills
- 360-feedback-templateDesign a 360-degree feedback survey or write a structured 360 feedback report. Use when asked to build a 360 feedback process, write 360 feedback for a colleague, design a feedback survey, or produce a feedback report. Produces either a complete survey instrument with rating scales and open-ended questions, or a structured narrative feedback report with themes, strengths, and development areas.
- ab-test-plannerDesign statistically rigorous A/B tests for product features, UI changes, onboarding flows, and pricing experiments. Use when asked to set up an experiment, design an A/B test, calculate sample size, or interpret test results. Produces a complete test plan with hypothesis, variant definitions, sample size, duration estimate, guardrail metrics, and a results interpretation guide.
- accessibility-auditGenerate a WCAG 2.2 accessibility audit checklist and remediation suggestions for any UI or design. Use when asked to audit for accessibility, check WCAG compliance, review a design for a11y issues, or create an accessibility remediation plan. Produces a prioritised checklist with pass/fail assessments and specific fixes.
- account-planBuild a structured account plan for any key customer or target account. Use when asked to create an account plan, key account strategy, strategic account review, or territory plan. Produces a complete account plan with relationship map, growth opportunities, risks, and 90-day action plan.
- aeo-optimizerOptimize an article for Answer Engine Optimization (AEO) — restructuring content so AI engines like ChatGPT, Perplexity, and Claude can extract, quote, and cite it. Rewrites headings as questions, drops 50-80 word answer capsules, audits paragraph length, and flags trust signals. Use when asked to AEO-optimize, make content AI-readable, improve AI citation chances, or adapt an article for answer engines.
- ai-ethics-reviewConduct an ethical review of an AI or ML feature, model, or product. Use when asked to run an AI ethics review, assess AI risks, audit a model for bias, or produce an AI impact assessment. Produces a structured ethics review covering fairness, transparency, privacy, safety, accountability, and societal impact with prioritised mitigations.
- ai-product-canvasStructure AI and ML product decisions with the rigour of any product decision. Use when building AI-powered features, evaluating LLM integrations, designing AI products, or assessing AI readiness. Produces a complete AI product canvas covering problem definition, model approach, data requirements, evaluation framework, UX design, responsible AI checklist, and launch monitoring plan.
- ambiguity-resolverStructure vague opportunities and unclear briefs into actionable one-page problem statements. Use when asked to clarify a vague brief, frame an undefined problem, make sense of an unclear opportunity, or when the user says 'we need to figure out what to do about X' or 'I've been asked to look into Y'. Produces a structured problem brief with reframed questions, scoped boundaries, and a minimum viable research plan.
- api-docs-writerWrite clear, developer-facing API documentation. Use when asked to document an API endpoint, write API reference docs, create a developer guide, or turn a raw spec/Postman collection into documentation. Produces endpoint documentation with descriptions, parameters, request/response examples, and error codes.
- api-versioning-strategyWrite an API versioning strategy document for a service or API platform. Use when asked to define versioning policy, plan API deprecation, classify breaking changes, or document version lifecycle. Produces a complete versioning strategy with breaking-change classification table, deprecation timeline, migration guide template, and client communication template.