Pricing Finder

Pricing Finder

Tell it your product URL or description. It finds 5 competitors, fetches their actual pricing pages, and returns a complete pricing intelligence report: dominant model in your space, benchmark price table, feature gate analysis, positioning map, and a concrete pricing recommendation for your product.

Zero required API keys. Runs entirely on free pip dependencies. Optional API keys improve quality.

---

Zero-hallucination policy: Every price point, tier name, and feature gate in the output must trace to fetched pricing page content or a DuckDuckGo search snippet. This applies to:

• Competitor prices: extracted verbatim from fetched page content only
• "Contact Sales": recorded as-is, never estimated or replaced with a number
• Tier names: copied exactly from the page, not paraphrased
• Feature lists: extracted from page content, not inferred from product knowledge
• Positioning observations: derived from the benchmark table data only

---

Common Mistakes

| The agent will want to... | Why that's wrong | |---|---| | Fill in "Contact Sales" with an estimated price | Never estimate enterprise pricing. Record it as "Contact Sales" exactly. | | Use training knowledge for competitor prices | Every price must trace to fetched page content or a search snippet. | | Skip the competitor confirmation step | Always show discovered competitors and wait for confirmation. Wrong competitors = wrong benchmarks. | | Recommend a price without referencing benchmark data | Every price recommendation must cite a specific number from the benchmark table. | | Mark a page as high quality when content /dev/null \ || echo "ERROR: Missing dependencies. Run: pip install ddgs requests beautifulsoup4 html2text"

**If dependencies are missing:** Stop immediately. Tell the user: "Missing Python dependencies. Run this to install them: `pip install ddgs requests beautifulsoup4 html2text` -- all free, no accounts needed. Then try again."

**If only API keys are missing:** Continue. DuckDuckGo and requests+BS4 are the free defaults.

Derive product slug:

```bash
PRODUCT_SLUG=$(python3 -c "
from urllib.parse import urlparse
import sys, re
url = 'URL_HERE'
if url.startswith('http'):
    host = urlparse(url).netloc.replace('www.', '')
    print(host.split('.')[0])
else:
    print(re.sub(r'[^a-z0-9]', '-', url[:30].lower()).strip('-'))
")
echo "Product slug: $PRODUCT_SLUG"

---

Step 2: Parse Input

Collect from the conversation:

• product_url: the URL to fetch (required, unless user pastes a description directly)
• geography: optional -- US / Europe / India / global. Default: US

If the user provides only a pasted description (no URL): Skip Steps 3 and 4. Go directly to Step 4 (product analysis) using the pasted text as product_content. Set page_source to user_description and note in data_quality_flags.

If neither URL nor description: Ask: "What is the URL of your product or startup? Or paste a short description: what it does, who it's for, and what makes it different."

---

Step 3: Fetch Product Page

Primary: Firecrawl (if FIRECRAWLAPIKEY is set)

curl -s -X POST https://api.firecrawl.dev/v1/scrape \
  -H "Authorization: Bearer $FIRECRAWL_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"url": "URL_HERE", "formats": ["markdown"], "onlyMainContent": true}' \
  | python3 -c "
import sys, json
d = json.load(sys.stdin)
content = d.get('data', {}).get('markdown', '') or d.get('markdown', '')
print(f'Fetched via Firecrawl: {len(content)} characters')
open('/tmp/pf-product-raw.md', 'w').write(content)
"

Fallback: requests + BS4 (free, always available)

python3 ', a['industry_taxonomy']['l2'], '>', a['industry_taxonomy']['l3'])
print('Differentiators:')
for d in a['differentiators']:
    print(f'  - {d}')
"

---

Step 4b: Phase 1 -- Competitor Discovery

ls scripts/research.py 2>/dev/null && echo "script found" || echo "ERROR: scripts/research.py not found -- cannot continue"

python3 scripts/research.py \
  --phase discover \
  --product-analysis /tmp/pf-product-analysis.json \
  --output /tmp/pf-competitors-raw.json

Print results for AI review:

python3 -c "
import json
data = json.load(open('/tmp/pf-competitors-raw.json'))
print(f'Searches run: {len(data[\"competitor_searches\"])}')
for s in data['competitor_searches']:
    print(f'\nQuery: {s[\"query\"]}')
    for r in s.get('results', [])[:6]:
        print(f'  - {r[\"title\"]} | {r[\"url\"]}')
        print(f'    {r.get(\"snippet\",\"\")[:150]}')
"

AI instructions: Read the search results above. Pick exactly 5 competitor companies that:

1. Are named in the search result titles or snippets
2. Are in the same L3 niche as the product being analyzed
3. Are actual software products (not agencies, list articles, or review sites)
4. Are distinct from each other

For each competitor write: name, url, pricing_url (their pricing page -- infer as [url]/pricing if not found in snippets), description (one sentence from snippet), source_url.

---

Step 5: Competitor Confirmation

python3  0:
    failures.append(f'INFO: {nf} field(s) marked "not found in page data"')

if 'data_quality_flags' not in result:
    result['data_quality_flags'] = []
result['data_quality_flags'].extend(failures)

json.dump(result, open('/tmp/pf-final.json', 'w'), indent=2)
print(f'QA complete. {len(failures)} issues.')
for f in failures:
    print(f'  - {f}')
if not failures:
    print('All QA checks passed.')
PYEOF

Present the output:

## Pricing Intel: [product_name]
Date: [today] | Competitors: [list] | Geography: [geography]

---

### Your Product
[one_line_description]
Differentiators: [list]

---

### 1. Pricing Model Analysis
Dominant model: [dominant_model] ([N]/5 competitors)
[model_explanation -- 2-3 sentences on why this model dominates the space]

Free tier: [N]/5 competitors | Free trial: [N]/5 | Annual discount: typical [X]%

---

### 2. Price Point Benchmark Table
| Competitor | Model | Entry | Mid | Top | Free tier | Free trial | Data quality |
|---|---|---|---|---|---|---|---|
[one row per competitor from benchmark_table]

Market ranges:
- Entry tier: $[min]-$[max]/mo (median $[median])
- Mid tier:   $[min]-$[max]/mo (median $[median])
- Enterprise: [enterprise_floor]

---

### 3. Feature Gate Analysis
Always free: [always_free list]
Always behind paid: [always_paid list]
Most variable across competitors: [most_variable list]

---

### 4. Competitive Positioning Map
Cheap + simple: [competitor] at $[X]/mo
Middle market:  [competitors] at $[X]-$[Y]/mo
Enterprise:     [competitor] (Contact Sales)
Underserved gap: [underserved_gap -- specific observation]

---

### 5. Recommended Pricing for [product_name]
Model: [model] -- [model_justification]
Entry: [entry_price] -- [entry_justification]
Mid:   [mid_price] -- [mid_justification]
Top:   [top_price] -- [top_justification]
Free tier: [Yes/No] -- [free_tier_justification]
Annual discount: [annual_discount] -- [annual_justification]
Gate behind paid: [gate_behind_paid] -- [gate_justification]

---
Data notes: [data_quality_flags or "None"]
Saved to: docs/pricing-intel/[PRODUCT_SLUG]-[DATE].md

Save to file and clean up:

DATE=$(date +%Y-%m-%d)
OUTPUT_FILE="docs/pricing-intel/${PRODUCT_SLUG}-${DATE}.md"
mkdir -p docs/pricing-intel
echo "Saved to: $OUTPUT_FILE"

rm -f /tmp/pf-product-raw.md /tmp/pf-product-analysis.json \
      /tmp/pf-competitors-raw.json /tmp/pf-competitors-confirmed.json \
      /tmp/pf-pricing-raw.json /tmp/pf-pricing-extracted.json \
      /tmp/pf-patterns.json /tmp/pf-final.json
echo "Temp files cleaned up."

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: Varnan-Tech
• Source: Varnan-Tech/opendirectory
• License: MIT
• Homepage: https://www.opendirectory.dev

Install and usage instructions live in the source repository linked above.