idea-validation-autopilot

📁 nbsp1221/agent-skills 📅 8 days ago
4
总安装量
3
周安装量
#50192
全站排名
安装命令
npx skills add https://github.com/nbsp1221/agent-skills --skill idea-validation-autopilot

Agent 安装分布

openclaw 3
gemini-cli 3
antigravity 3
claude-code 3
github-copilot 3
codex 3

Skill 文档

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:

Dimension Weight
Problem severity and frequency 25
Distribution reachability 20
Willingness-to-pay potential 20
MVP speed/feasibility 20
Strategic differentiation 15

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.