UnfairGaps

AI is replacing engineers. The safest career move? Find a boring niche that Big Tech will never touch. This tool finds those niches.

The Story

I spent 2 years building tools nobody wanted. Every time it was the same: I’d get excited about a “clever” idea, spend months building it, and discover that nobody cared enough to pay. I was solving annoyances, not real problems.

Then I had a realization: stop brainstorming ideas. Start reading court filings. If a company is paying a fine or settling a lawsuit, they aren’t looking for a “nice-to-have” tool. They’re looking for a tourniquet.

So I burned $5K in API credits and built AI pipelines that scan enforcement data automatically. I found things like:

• Solar installers losing $12K per rejected warranty claim because field techs forget to geotag photos
• E-commerce stores settling 4,000+ ADA lawsuits/year at $20-50K each
• Apparel brands writing off $1-3M/year on returns from assembly defects

I posted the results on Reddit:

• 659 upvotes on r/Entrepreneur - “I scraped 48,000 court filings to stop guessing business ideas”
• 237 comments on r/SideProject - people begged me to scan their industries
• 102 upvotes on r/Logistics - trucking pain analysis

One user took my research and is now building a company around a gap I found. The posts got over 1.5M views total.

I tried to turn this into a SaaS. But I kept feeling like I was doing something wrong - locking up a methodology that could help thousands of people find real businesses. If developers start scanning boring niches and building solutions, everyone wins. Society wins because broken processes get fixed. The developer wins because they build a real business. And I win because I stop carrying the guilt of hoarding this.

So here’s everything I built. 4 pipelines, 17 prompts, a Python CLI, and AI agent skills. Free. MIT license. Take it, improve it, build a boring profitable business with it.

Why This Matters Right Now

Every week another headline: “Google cuts 12K engineers.” “Meta lays off entire ML team.” “Startup replaces 60% of engineering with AI.”

The standard advice is “build a side project.” But build what?

The most profitable software businesses solve painfully boring problems for industries that never make TechCrunch:

• Plumbing contractors paying $50K/year in OSHA fines
• Solar installers losing $12K per rejected warranty claim because a field tech forgot to geotag a photo
• Restaurant owners settling ADA lawsuits for $20-50K each

AI can’t replace you if your customers are plumbing contractors who barely use email. The boring niches are where the money is. This tool finds them.

The 5 Pipelines

Bonus: the repo ships with a US profession dataset. 130 SCOPE profiles + 25 ready-made pain bundles in data/professions/us/. Clone and grep - no API calls needed to start. Other countries: not yet pre-generated, but the operation works for any country - run it and PR the result.

Live example - Auto Detailers (US)

To make this concrete: one run of profession-scan auto detailers in US produced 13 specific pains, each paired with a structured skill_spec (a buildable AI tool). Total money-at-risk if unmanaged: ~$158,000/yr per operator. 81 hours/week of work that AI could automate. All 5 skill types used.

Top three:

1. Detail Job Price Calculator (calculator). Most detailers eyeball pricing and undercut by 25%. The spec includes 10 inputs (vehicle_type, services, hourly_target_rate, monthly_overhead, miles_to_job, …), the cost-plus formula with the 2026 IRS mileage rate ($0.67/mile) and 15.3% SE tax buffer, and outputs minimum + recommended price plus tax set-aside. Ship this as a $19/mo SaaS.
2. EPA Stormwater Compliance Checklist (checklist, $64,618/day at risk). Clean Water Act civil penalties for runoff into storm drains. The spec is a 12-step procedure with warnings (“biodegradable soap is NOT a defense”) and citations (33 USC 1311, 40 CFR 122.26, EPA 2026 MSGP).
3. California Car Wash Act Compliance (checklist, $36,500/yr at risk). $300/yr registration + $15K surety bond + AB 5 worker classification ($25K+/worker penalty for misclassification). 11-step compliance walkthrough with statutory references (Labor Code §§ 2050-2067).

None of these are “lawyers experience burnout” platitudes. Each is a focused AI tool with input/output schema, formulas, and statute citations.

The Philosophy

Annoyances vs Liabilities.

Most “market research” finds annoyances. UnfairGaps finds liabilities - places where businesses are legally required to lose money. If a company is paying a fine or settling a lawsuit, they aren’t looking for a nice-to-have tool. They’re looking for a tourniquet.

How It Works

There are two architectures depending on the pipeline:

Event-collection pipelines (industry-scan, validate-idea, site-audit, customer-pains)

These find pains from court filings + enforcement events. 4-phase protocol:

1. Compose targeted search queries - Claude determines the right regulatory agencies, court systems, and search language for your country
2. Search the web - finds lawsuits, fines, enforcement actions, industry reports
3. Extract evidence - structured findings: who, what, how much, source URL
4. Analyze & report - clustering, deduplication, scoring, opportunity generation

Use these when the source of pain is “someone paid for breaking the rules.”

profession-scan - two-stage architecture

Professional pain doesn’t live in court filings. It lives in regulation + daily routine. Different problem, different solution:

Stage 1 - Web search for regulatory facts (NOT for pains).

7 targeted WebSearch queries pull facts from .gov / law.cornell.edu / BLS / professional associations:

• daily_reality - hourly workday, documents, deadlines, seasonality
• regulatory - statutes, licensing bodies, penalty amounts
• tools - software, gov portals, what’s broken
• terminology - jargon, abbreviations
• career_psychology - levels, fears, burnout
• community - subreddits, associations, conferences
• market - salary, headcount, trends

Output: a single JSON profile with ~30 specific facts + source URLs. Web search is asked for facts, not insights.

Stage 2 - Claude Opus 4.7 deduces the pains (no web search at this stage).

The model reads the regulatory profile and figures out which specific recurring task is painful given that combination of documents, deadlines, penalties, and tools - and what AI skill would obliterate it. Output: 8-15 pains with structured skill_spec (calculator inputs + formula, or checklist steps + warnings, or template variables, or reference lookup keys, or advisor decision criteria).

Why split it. Web search gives facts but no insight. LLM alone gives insight but no facts. Stack them in order with structured handoffs (JSON, not free text), and you get output quality neither could produce alone: fact-grounded pains that read like a senior consultant wrote them.

All prompts are in prompts/ - fully transparent, fully customizable.

Quick Start - two ways to run it

There are two equivalent modes. Pick one. You can mix them per-task.

Option A - Run it in Claude Code / Cursor / Codex for free (no API key)

If you use any agent that supports the skills.sh ecosystem (Claude Code, Cursor, Codex, VSCode, Cline, and others), install with one command:

npx skills add AyanbekDos/unfairgaps-os

That installs one skill - unfairgaps - which bundles all five operations of the methodology. Call them in any agent session:

/unfairgaps industry-scan construction in US
/unfairgaps validate-idea "SaaS for warehouse ergonomic compliance" in US
/unfairgaps site-audit https://competitor.com
/unfairgaps customer-pains https://my-saas.com
/unfairgaps profession-scan lawyers in US

The first four operations share the same 4-phase unfairgap-detection protocol (research plan -> candidate pool -> evidence ledger -> unfairgap pattern synthesis -> report). Only the input and the final-report shape differ per operation.

profession-scan uses a different 3-phase protocol (SCOPE builder -> pain generator -> bundle formatter) because professional pains live in regulation + daily routine, not in court filings. It’s native-only (no Perplexity / no API key) and country-aware (auto-localizes regulators, statutory citation format, language, and currency for any country).

No Perplexity API key, no Python environment, no config. The skill uses your agent’s built-in WebSearch and WebFetch tools.

Prefer a manual install? Clone the repo and copy the skill folder:

git clone https://github.com/AyanbekDos/unfairgaps-os.git
cp -r unfairgaps-os/skills/unfairgaps ~/.claude/skills/

Claude Code runs the 4-phase protocol (research plan → candidate pool → evidence ledger with compressed cards → unfairgap pattern detection → final report). Output lands in your current working directory as report-*.md with a full run manifest for reproducibility.

This is the recommended path for single-run analysis, iteration, and anyone who doesn’t want to manage API keys.

Option B - Run it via CLI with a Perplexity API key (for automation / batch)

If you want to script it, run it in CI, or run overnight batches, use the CLI path. It’s the same 4 pipelines, same output schema - just deterministic and non-interactive.

Requirements:

• Python 3.10+
• A Perplexity API key. Perplexity gives $5/month free API credits to every account (~20 full pipeline runs, no credit card needed). Get one at perplexity.ai/settings/api.

Install:

git clone https://github.com/AyanbekDos/unfairgaps-os.git
cd unfairgaps-os
pip install scrapling httpx
cp .env.example .env
# Edit .env and paste your PERPLEXITY_API_KEY

When the skill files from Option A detect PERPLEXITY_API_KEY in the environment, they automatically delegate to the CLI - so you can still invoke via /unfairgaps-industry-scan in Claude Code and it will take the faster CLI path.

Run

# Find pain points in an industry
python run.py industry-scan --industry "construction" --country US

# Validate a business idea
python run.py validate-idea --idea "SaaS for restaurant health code compliance" --country US

# Audit a website's pain claims
python run.py site-audit --url "https://example.com"

# Find your customers' pain points
python run.py customer-pains --url "https://your-site.com"

AI Agent support

Native skill flow works in any agent on the skills.sh ecosystem - Claude Code, Cursor, Codex, VSCode, Cline, and others. Install via npx skills add AyanbekDos/unfairgaps-os (see Option A).

Agents outside the skills.sh ecosystem: copy-paste the SKILL.md content into your agent and run the 4-phase protocol manually. Results depend on the agent’s WebSearch/WebFetch support.

No AI agent at all: copy-paste the prompts from prompts/ into ChatGPT or Claude.ai. Works, just more manual.

Use prompts manually

Every prompt is in prompts/ as plain markdown. Copy-paste into ChatGPT, Claude, or any LLM:

1. Open the pipeline folder (e.g., prompts/industry-scan/)
2. Follow steps 01, 02, 03… in order
3. Feed output of each step as input to the next

No code required. Works with any LLM that can search the web.

Country Support

All 5 pipelines work worldwide. When you specify a country, Claude automatically determines:

• Regulatory agencies (OSHA in US, BG BAU + Finanzamt in Germany, HMRC + HSE in UK, КГД + Минтруда in Kazakhstan, URSSAF + DGFiP in France…)
• Court systems and legal databases
• Statutory citation format (USC/CFR, BGB/StGB, НК РК, Code général des impôts…)
• Search language (native + English fallback)
• Local currency (with USD conversion for cross-country comparison)
• Industry-specific and profession-specific regulations

Tested with: US, DE, KZ, RU, UK, BR, IN, AU, FR, and more. Quality is highest for countries with strong online regulatory presence and degrades gracefully (with explicit coverage_gap notes) for low-online-coverage countries.

How is this different from ChatGPT?

Data Sources

UnfairGaps searches across:

• Court records - lawsuits, settlements, class actions
• SEC EDGAR - securities enforcement actions
• EPA ECHO - environmental violations and fines
• OSHA - workplace safety citations
• CFPB - consumer complaints (14M+ records)
• openFDA - product recalls and enforcement
• Country-specific - local regulatory databases based on your market

All public data. No scraping private databases. No paywalls.

Cost

With Perplexity’s free $5/month:

• ~4-5 full Industry Scans
• ~5-6 Idea Validations
• ~3-4 Site Audits
• ~2-3 Customer Pain Finder runs

One pipeline run costs roughly $0.50-1.50 depending on complexity.

Project Structure

unfairgaps-os/
  README.md
  .env.example          # Just one key: PERPLEXITY_API_KEY (not needed for profession-scan)
  run.py                # CLI entry point (covers 4 pipelines; profession-scan is native-only)
  prompts/
    shared/             # Reusable across all pipelines
    industry-scan/      # Pipeline 1: Find industry pain points
    idea-validator/     # Pipeline 2: Validate business ideas
    site-audit/         # Pipeline 3: Claims vs reality
    customer-pains/     # Pipeline 4: Your customers' pain points
    profession-scan/    # Pipeline 5: Pain bundles for professions (country-aware)
  skills/               # AI agent skill files (Claude Code, Cursor, etc.)
  data/
    professions/
      us/
        profiles/       # 130 US profession SCOPE profiles (BLS / O*NET / .gov)
        pains/          # 25 ready-made pain bundles with skill specs
        README.md       # Dataset docs
        _FORMAT.md      # JSON schema reference

Help Wanted

I’m not a professional programmer. I built this because I needed it. Here’s where I need help:

• Direct database connectors - Right now we search through Perplexity. Building direct connectors to PACER, SEC EDGAR, EPA ECHO, and OSHA databases would make results 10x more reliable and faster.
• Prompt engineering - The 20 prompts work, but they’re not perfect. I’d love prompt engineers to tear them apart.
• Python improvements - The CLI runs but it’s not elegant. Any Pythonista who wants to refactor - please do.
• Country adapters - Every country has its own regulatory databases. I know US and Kazakhstan well. Help me add yours.
• Fill the profession dataset - 130 US profiles are shipped, only 25 have pain bundles. Run /unfairgaps profession-scan in US on the others and PR the resulting JSON to data/professions/us/pains/.
• Generate other-country datasets - The profession-scan operation is country-aware. Pick a country, run /unfairgaps profession-scan in , and PR the resulting profiles + pains to data/professions//. UK, DE, FR, IN, BR, AU, NL especially welcome.
• Bug reports - Run it on your industry and tell me what breaks.

License

MIT

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Built by @AyanbekDos from Kazakhstan. 4 months of work, open-sourced because the world needs more people building boring, profitable businesses instead of chasing the next AI wrapper.

If this helped you find a real business opportunity, I’d love to hear about it.