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Use when deciding whether to build an AI product — rapid problem validation, market discovery, early user interviews, and demand signals BEFORE writing any code. Prevents the
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基于 SOC 职业分类
Use when conducting quality audits — reviewing process compliance, identifying gaps between defined process and actual practice, conducting structured inspections (code review audits, test quality reviews), and producing audit reports with remediation plans.
Use when managing quality risk — identifying quality risks in a product or release, applying risk-based testing prioritization, creating risk mitigation plans, and communicating quality risk to stakeholders for go/no-go decisions.
Use when designing or implementing test automation — choosing the right automation framework (Playwright, pytest, JUnit), Page Object Model, selector strategies, test isolation, managing flaky tests, and CI integration.
Use when managing defects — writing effective bug reports, applying severity/priority matrix, tracking defect lifecycle, conducting root cause analysis, and measuring defect metrics for process improvement.
Use when measuring and reporting QA quality — defect escape rate, test coverage analysis, flaky test rate, mean time to detect, shift-left metrics, and building quality dashboards for stakeholders.
Use when managing test data — designing test data strategies, using factories and builders, creating fixtures, generating synthetic data, masking PII for testing, and managing test database state.
| name | ai-product-validation |
| description | Use when deciding whether to build an AI product — rapid problem validation, market discovery, early user interviews, and demand signals BEFORE writing any code. Prevents the |
80% of solo AI products fail because founders build before validating. The goal: find 10 people who WANT to pay you before you write code.
Wrong order (most builders): Right order (validation-first):
Idea → Build → Launch → Crickets Idea → Validate → Build → Launch → Users
Minimum validation bar before building:
Use Claude to research whether the problem exists at scale:
Prompt: "Search Reddit, Twitter, and forums for complaints about [problem area].
Find posts where people express frustration, ask for tools, or describe workarounds.
Summarize: frequency of complaint, exact language used, what solutions people try."
Where to look:
"I wish there was a tool that", "frustrated with", "someone should build"ask.hn threads, "who is hiring" for tool needsSignal vs noise:
Talk to 5 real humans before building anything. Use AI to find them:
# Use Claude to draft outreach for each channel
outreach_dm = """Hi [name], I saw your post about [specific pain].
I'm researching this problem and would love 15 minutes to understand
your workflow. No pitch — I'm deciding whether to build a tool.
Would next [day/time] work?"""
Interview script (15 minutes):
Killer questions:
Build a landing page in 2 hours with Bolt/v0, validate with fake door:
Page structure:
1. Headline: "[Outcome] for [ICP] — 10x faster"
2. 3 bullet benefits (outcomes, not features)
3. Email capture: "Join waitlist — first 50 users get 50% off"
4. No build required — just measure demand
Success: >5% conversion on targeted traffic
Failure: <1% conversion → wrong problem or wrong audience
Drive traffic cheaply:
Before building, run the moat test:
Defensibility matrix:
- Who are the 3 biggest competitors? (search "alternatives to X")
- What do users complain about in their reviews? (G2, Capterra, App Store)
- Could OpenAI/Anthropic just add this to ChatGPT? (if yes, find a niche)
- What would you have that they don't? (data / workflow / trust / niche)
Pass criteria for moat:
ai-product-positioning (once validated, position clearly)mvp-rapid-development (validated? now build fast)vibe-coding (use vibe tools to build fake-door landing page in 2 hours)@solo-ai-builder starts every new product idea with this skill