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- digital product idea validator pro
digital product idea validator pro
A skeptical auditor that stress-tests digital product concepts to prevent building things nobody will buy.
$5
Works with the AI Agents you already use
digital product idea validator pro
Example session with this skill installed
I want to build an AI-powered tool that summarizes Jira tickets for project managers. I have 500 people on a waitlist from a LinkedIn post. Pricing is $20/month. Evaluate this.
- Read your context and instructions
- Compiled the digital product idea
Product Idea Validation
Executive Verdict
Recommendation: VALIDATE
Confidence Level: Low
Reason: A LinkedIn waitlist indicates social interest but does not prove willingness to pay or integrate into a high-friction enterprise workflow like Jira.
Concept Restatement
- Buyer: PMO Director or Engineering Manager.
- Problem: Time spent reading ticket history.
- Outcome: Faster sprint planning.
- Assumptions: Users have permission to connect AI to Jira; summaries are accurate enough to replace reading.
Need, Motivation, and Existing Behavior
The problem is frequent (daily) but current behavior includes manual reading or quick sync meetings. The urgency is moderate unless a project is failing. The waitlist is "Weak Evidence" as it costs nothing to join.
Scorecard
| Dimension | Score |
|---|---|
| Buyer specificity | 3 |
| Problem severity and frequency | 4 |
| Purchase urgency | 2 |
| Existing workaround evidence | 3 |
| Willingness to pay | 2 |
| Differentiation | 1 |
| Distribution feasibility | 3 |
| Value and economics | 3 |
| Adoption and retention potential | 2 |
| Evidence strength | 2 |
Average Score: 2.5 (Materially Risky)
Strongest and Weakest Assumptions
- Assumption: PMs are allowed to input Jira data into a 3rd party AI. (Critical/High Uncertainty).
- Assumption: A $20/mo summary is worth the friction of another tool login. (High Importance/Low Evidence).
Smallest Useful Next Test
- Target: 10 PMs from the waitlist.
- Test: Offer a "Concierge" summary service for $10 for one week (manual delivery).
- Success Threshold: 3+ paid signups.
- Failure Threshold: <1 paid signup.
- Decision: If failed, pivot to a Chrome extension that lives inside Jira to reduce friction.
Next steps
- Categorize your waitlist by company size to identify security hurdles.
- Send the "Paid Week" offer to the top 50 most active LinkedIn commenters.
- Draft a data privacy one-pager to see if it immediately kills the conversation.
Connects securely to your tools. The creator never sees your data.
About this skill
The problem
Building a digital product based on hypothetical interest or founder intuition often leads to wasted engineering cycles. Most concepts fail not due to technical inability, but because they solve a low-priority problem or lack a clear purchase trigger.
What it does
- Identifies the economic buyer versus the end user to map real purchasing power.
- Audits existing workarounds like spreadsheets or manual labor to gauge problem severity.
- Scores the concept across ten dimensions including differentiation, distribution, and unit economics.
- Ranks assumptions by risk using importance and uncertainty metrics.
- Defines a time-boxed, low-cost experiment to disprove the riskiest commercial belief.
Why this beats prompting it yourself
General AI prompts often default to toxic positivity, telling you every idea is a winner. This skill is programmed to be a skeptical auditor that penalizes vague positioning and separates stated interest from actual behavior, preventing costly build-phase errors.
Use cases
- Vetting a SaaS feature idea before adding it to the product roadmap.
- Stress-testing a digital download or course concept against existing free alternatives.
- Deciding whether to pivot a struggling project based on current market evidence.
- Designing a landing page test that measures commitment rather than just email signups.
Known limitations
This skill requires specific details about your target audience and current evidence. It cannot predict market behavior if you provide zero context or purely hypothetical inputs.
Requires specific details on target buyers and existing evidence. Cannot verify real-world market data or competitor pricing without user-provided inputs.
How to use
- 1
Connect your agent to the Agensi MCP
Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup
- 2
Sign in to Agensi
Your agent shows a sign-in link. Sign in and click Allow.
- 3
Ask for what you need
Your agent loads this skill and uses it on your task. It always asks before any payment.
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