pricing-finder

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Install

npx "@opendirectory.dev/skills" install pricing-finder --target claude

npx skills add Varnan-Tech/opendirectory --skill pricing-finder

/plugin marketplace add Varnan-Tech/opendirectory
/plugin install opendirectory-gtm-skills@opendirectory-marketplace

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Setup

# Set up free Python dependencies (required)
pip install ddgs requests beautifulsoup4 html2text

cp .env.example .env
# Uncomment TAVILY_API_KEY or FIRECRAWL_API_KEY if you have them

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Usage

Find pricing benchmarks for my startup: https://yourstartup.com

Find pricing benchmarks. We build [what you do] for [who]. [Stage], [geography].

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What It Does

1. Fetches your product page to understand what you’re building
2. Analyzes your product: category, differentiators, ICP
3. Discovers 5 competitors in your space via DuckDuckGo search
4. Asks you to confirm the competitor list before fetching any data
5. Fetches each competitor’s pricing page (3-tier fallback: direct fetch → Google cache → search snippet)
6. Extracts structured pricing data: tiers, prices, limits, feature gates
7. Analyzes patterns across all 5 competitors
8. Maps competitive positioning and identifies underserved gaps
9. Recommends a concrete pricing strategy for your product

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Output

• Pricing Model Analysis — dominant model in your space (per-seat / flat-rate / usage-based / freemium) and why
• Price Point Benchmark Table — every competitor’s tiers with exact prices, free tier, free trial
• Market ranges — entry tier median, mid tier median, enterprise floor
• Feature Gate Analysis — what’s always free, what’s always paid, what varies
• Competitive Positioning Map — who owns cheap+simple, middle market, enterprise, and where the gap is
• Recommended Pricing Strategy — model, entry price, mid price, top price, free tier decision, annual discount, what to gate behind paid

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Cost

• Tavily API (free tier: 1000 credits/month) — better search results
• Firecrawl API (free tier: 500 credits/month) — better JS-heavy page rendering

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Zero-Hallucination Policy

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How Pricing Page Fetching Works

1. Direct fetch (requests + beautifulsoup4 + html2text) — works for most pricing pages since companies make them SEO-friendly and server-rendered
2. Google cache — fallback for recently-changed pages or soft blocks
3. Search snippet — last resort; marks data quality as “low” in the output

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Project Structure

pricing-finder/
├── SKILL.md                        -- 10-step workflow for Claude Code
├── README.md                       -- this file
├── .env.example                    -- optional API key template
├── requirements.txt                -- free pip dependencies
├── scripts/
│   └── research.py                 -- two-phase data collector
├── evals/
│   └── evals.json                  -- 5 test cases
└── references/
    ├── pricing-models.md           -- per-seat, flat, usage, freemium definitions + signals
    ├── extraction-guide.md         -- how to read a pricing page
    └── positioning-guide.md        -- how to map competitors and find gaps

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Standalone Script Usage

# Phase 1: competitor discovery
python3 scripts/research.py \
    --phase discover \
    --product-analysis /tmp/pf-product-analysis.json \
    --output /tmp/pf-competitors-raw.json

# Phase 2: fetch pricing pages
python3 scripts/research.py \
    --phase fetch-pricing \
    --competitors /tmp/pf-competitors-confirmed.json \
    --output /tmp/pf-pricing-raw.json

# With optional API key upgrades
python3 scripts/research.py \
    --phase fetch-pricing \
    --competitors /tmp/pf-competitors-confirmed.json \
    --firecrawl-key "$FIRECRAWL_API_KEY" \
    --tavily-key "$TAVILY_API_KEY" \
    --output /tmp/pf-pricing-raw.json