product-discovery

by headcount

Finds out whether a problem is real and a solution would work, before building it — recruiting the right people, interviewing without leading them, separating what users say from what they do, naming the riskiest assumption and testing that one first, and reaching a decision rather than a summary. Use this to validate a problem, test an idea cheaply, decide whether to build something, or fix a discovery process that keeps confirming what the team already believed.

v1.0.0New

#headcount

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Install this skill

Run this command in your terminal. No account required — it auto-detects your AI tool and installs the skill file.

npx @skills-hub-ai/cli install headcount-product-discovery

Or download directly:

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Setup by platform

Claude Code

~/.claude/skills/<skill>/SKILL.md

Setup guide →

Install

One-click setup for your editor

Run in your project root

npx @skills-hub-ai/cli install headcount-product-discovery --target claude-code

Instructions

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product-requirementsWrites down what is being built so a team can build it and know when they are done — problem and success measure before solution, scope stated by exclusion, user-visible behavior including the states everyone forgets, acceptance criteria someone can test, and the open questions named rather than buried. Use this to write a specification, review one that is causing rework, or work out why a delivered feature technically matched the request and still was not what anyone wanted.0 installs chief-legal-and-risk-officerOwns legal, contracts, intellectual property, regulatory compliance, privacy, security governance, enterprise risk, and audit readiness. Use this to review a contract or commitment, assess regulatory or privacy exposure, evaluate an IP or licensing question, judge the risk in a business decision, prepare for an audit or certification, or when a plan may create obligations the business cannot meet. Also use to decide whether a risk should be accepted, mitigated, or refused.0 installs budgeting-and-forecastingRuns the planning cycle — annual budget, rolling forecast, consolidation of business unit inputs, and the variance analysis that explains actuals against plan. Use this to build or challenge a budget, run a forecast cycle, consolidate departmental submissions, explain why results diverged from plan, or improve forecast accuracy.0 installs scenario-planningPlans under genuine uncertainty — building scenarios, identifying which assumptions are load-bearing, setting early-warning indicators, and stress-testing a plan against futures rather than forecasting one. Use this when a decision depends on something unknowable, when a plan assumes conditions that may not hold, before a large irreversible commitment, or when a market, regulatory, or technology shift could invalidate the strategy.0 installs ux-product-auditorAudits a website, app, onboarding flow, or design for usability, conversion, and product problems, tying every finding to a business outcome and a severity. Use this to review an interface, diagnose low conversion or activation, find where users drop off, get structured product feedback, or decide which UX fixes to ship first. For raising visual quality rather than finding problems, use `interface-craft`.0 installs chief-human-resources-officerOwns the organization itself: org design, hiring, performance, compensation, development, culture, and employee relations. Use this to design or restructure a team, plan hiring, write a role or leveling definition, handle a performance or compensation question, diagnose why a team is underperforming for non-technical reasons, or assess the people risk in a plan. Also use for onboarding design and retention of key staff.0 installs

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saas-idea-validatorEvaluates a software or startup idea against problem, market, competition, monetization, defensibility, and execution, and returns a verdict rather than encouragement. Use this when an idea needs pressure-testing before anyone builds, when deciding whether something is worth pursuing, when assessing competition or willingness to pay, or when a validated idea needs a first-customers and MVP plan. Default to scrutiny; the useful answer is usually the unwelcome one.1 installs data-modelingDesigns the warehouse and semantic layer — source-to-mart structure, dimensional modeling, grain, slowly changing dimensions, and the metric layer analytics reads through. Use this to design or restructure a warehouse, model a new source, decide on grain or table structure, build a semantic or metric layer, or diagnose why queries are slow, wrong, or impossible to write.0 installs experimentationDesigns, runs, and reads A/B tests and growth experiments — hypothesis, sample size, duration, and honest interpretation. Use this to plan a test, judge whether a result is real, build an experimentation program, decide what to test next, or diagnose why tests keep producing inconclusive or non-replicating results.0 installs lead-captureConverts anonymous traffic into known contacts — lead magnets, gated content, free tools, popups, and the forms behind them. Use this to design or improve a lead magnet, plan a free tool, add or fix popups and overlays, improve form conversion, or decide what is worth gating and what should stay open.0 installs marketing-analyticsSets up, audits, and reports on marketing measurement — tracking plans, event schemas, attribution models, and the dashboards built on them. Use this to instrument a site or product, audit tracking nobody trusts, choose or interpret an attribution model, build reporting that answers a specific question, or reconcile numbers that disagree between tools.0 installs ai-ml-governanceGoverns models and AI systems in production — intended use, evaluation, monitoring, human oversight, documentation, and the decision to deploy or retire. Use this before deploying a model or AI feature, when defining evaluation criteria, when a model's behavior has drifted, when assessing AI risk or regulatory exposure, or when deciding whether an AI system is fit for a consequential decision.0 installs

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Frequently asked questions about product-discovery

What does the product-discovery skill do?

How do I install the product-discovery skill?

Run `npx @skills-hub-ai/cli install headcount-product-discovery` from your terminal. The CLI writes the SKILL.md to the correct location for your AI tool (e.g. ~/.claude/skills/headcount-product-discovery/ for Claude Code or ~/.cursor/skills/ for Cursor with --target cursor) and adds it to your project's .skills.json lockfile.

Which AI tools does product-discovery work with?

product-discovery runs in Claude Code. It follows the open Agent Skills standard (SKILL.md), so the same skill works in every supported tool without modification.

Is the product-discovery skill free?

Yes. Every skill on skills-hub.ai is free and open-source. There are no premium tiers, paywalls, or usage limits. You only pay for whatever AI assistant you're already using.

How do I use product-discovery after installing it?

In Claude Code, type `/headcount-product-discovery` (or whatever slash command the skill registers) and the AI follows the skill's instructions immediately. You can also reference it by name in natural language, your AI loads the skill into context when relevant.

Can I share the product-discovery skill with my team?

Yes. Commit your project's .skills.json lockfile and teammates run `npx @skills-hub-ai/cli install` (no args) to install every skill at the exact version you pinned. Organization-scoped installs work via skills-hub.ai organizations.