idea-validation-autopilot

Use when a founder has a rough product idea and wants autonomous deep validation, market and competitor research, and an evidence-based MVP decision with minimal back-and-forth.

Safety Notice

This listing is imported from skills.sh public index metadata. Review upstream SKILL.md and repository scripts before running.

Copy this and send it to your AI assistant to learn

Install skill "idea-validation-autopilot" with this command: npx skills add nbsp1221/agent-skills/nbsp1221-agent-skills-idea-validation-autopilot

Idea Validation Autopilot

Turn a rough idea into an evidence-backed build decision in one run.

Overview

This skill is a single orchestrator for:

  1. idea clarification
  2. market and competitor research
  3. MVP scope definition
  4. go/no-go style decision memo

Default behavior favors action over over-analysis:

  • ask as few questions as possible
  • run parallel research
  • output a build-ready decision packet

When to Use

Use this skill when:

  • user says they have many ideas but cannot decide efficiently
  • user wants "startup-like" process without paying for SaaS tools
  • user wants AI to drive research and synthesis, not just brainstorm
  • user asks for market validation + MVP boundaries + next execution steps

Do not use this skill when:

  • user already has validated requirements and only wants implementation planning
  • user wants only code generation with no discovery work

Operating Defaults

If user context is missing, proceed with defaults instead of blocking:

  • Goal priority: speed-to-learning > polish
  • Budget assumption: near-zero external spend
  • Team assumption: solo builder or very small team
  • Timebox assumption: one focused discovery cycle

Only ask questions when missing data would invalidate the result (for example: unclear target user or regulated domain).

Workflow

Copy this checklist and track progress:

Progress
- [ ] Step 1: Normalize idea into problem hypothesis
- [ ] Step 2: Run 4 parallel research tracks
- [ ] Step 3: Grade evidence quality and resolve contradictions
- [ ] Step 4: Produce decision scorecard and verdict
- [ ] Step 5: Define MVP scope and exclusions
- [ ] Step 6: Define first experiments and stop rules
- [ ] Step 7: Deliver final report using template

Step 1: Normalize the idea

Convert raw idea into this structure:

  • target user
  • painful job-to-be-done
  • current workaround
  • why-now trigger
  • value promise in one sentence

If unclear, propose your best assumption and mark it explicitly.

Step 2: Run 4 parallel research tracks

Dispatch four independent subagents (or equivalent parallel workers).

  1. User/Problem Research
  • Find who feels the pain and how urgently.
  • Capture behavioral evidence, not just opinions.
  1. Market/Competitor Research
  • Map direct/adjacent alternatives, pricing, positioning, switching cost.
  • Identify market gap with realistic differentiation.
  1. Business Model/Risk Research
  • Estimate willingness-to-pay signals, acquisition path, and major risks.
  • Flag legal/compliance/data-access blockers early.
  1. MVP/Technical Feasibility Research
  • Define thinnest viable product delivering the core job.
  • Identify build constraints, integration risks, and timeline risk.

Step 3: Grade evidence quality

Use evidence tiers:

  • Tier A: behavioral or monetary signal (payment, waitlist intent with commitment, repeated real usage)
  • Tier B: strong secondary evidence (credible reports, robust competitor/user data)
  • Tier C: weak signal (opinions, generic trend articles, unsupported claims)

Rules:

  • critical claims need at least two independent sources
  • if evidence is weak, lower confidence regardless of narrative quality

Step 4: Score and decide

Score 0-100 using weighted dimensions:

DimensionWeight
Problem severity and frequency25
Distribution reachability20
Willingness-to-pay potential20
MVP speed/feasibility20
Strategic differentiation15

Scoring rules (fixed):

  • each dimension score is 0..100
  • weighted_i = score_i * weight_i / 100
  • total_score = round(sum(weighted_i), 1)
  • map verdict from total_score using the bands below

Verdict bands:

  • 80-100: Build now
  • 60-79: Validate-first (run targeted tests before building)
  • 40-59: Pivot
  • <40: Drop

Step 5: Define MVP scope

Use strict scope slicing:

  • Must: smallest set proving core value
  • Should: useful but deferrable
  • Won't (now): explicitly excluded features

Output a 2-week implementation target:

  • week 1: build core flow
  • week 2: launch to first users and collect signals

Step 6: Define experiments and stop rules

For top risks, define:

  • experiment
  • pass threshold
  • fail threshold
  • next action if pass/fail

Keep experiments cheap and fast. Favor reversible steps.

Step 7: Deliver final report

Use assets/final-report-template.md.

Output path rules:

  • if reports/ does not exist, create it first (mkdir -p reports)
  • write report to reports/YYYY-MM-DD-<idea-slug>-idea-validation.md

Required output qualities:

  • explicit assumptions table
  • explicit unknowns
  • citations and dated evidence
  • final recommendation plus next 7-day action plan

Common Failure Modes

  1. Over-research without decisions
  • Fix: enforce scorecard and verdict section every run.
  1. Generic competitor list with no switching analysis
  • Fix: include why users switch or stay.
  1. MVP too large
  • Fix: require "what can be deleted" before finalizing scope.
  1. False confidence from weak sources
  • Fix: downgrade to Tier C and force validation-first verdict.

Quick Command Patterns

Adapt to available tools:

  • web search + fetch for sources
  • repository/API lookup for existing solutions
  • parallel subagents for independent tracks
  • markdown report output in project reports/

If one tool is unavailable, continue with the best fallback and document the limitation in assumptions.

Source Transparency

This detail page is rendered from real SKILL.md content. Trust labels are metadata-based hints, not a safety guarantee.

Related Skills

Related by shared tags or category signals.

Automation

commit

No summary provided by upstream source.

Repository SourceNeeds Review
Automation

docker-compose

No summary provided by upstream source.

Repository SourceNeeds Review
Research

skillforge

Generate production-grade Agent Skill packages through a structured 7-step pipeline: requirement analysis, architecture decisions, metadata crafting, body ge...

Registry SourceRecently Updated
076
Profile unavailable