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Clawsavings

מספק מידע על כרטיסי אשראי, מועדונים ושוברים לחסכון בכל חנות ישראלית, כולל השוואת הנחות ומחירים מעודכנים.

personAuthor: yhyatthubclawhub
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ClawSavings — Israeli Discounts & Price Comparison

USP: Knows which Israeli card saves you most at any store — 72 stores, 24 verified deals, one answer.

When to Use This Skill

Activate when the user asks about:

  • Best card/club to use at a specific store ("באיזה כרטיס לויקטורי?")
  • Discounts, vouchers, deals for Israeli stores
  • Saving money on purchases in Israel
  • Cheapest place to buy a product ("איפה הכי זול?")
  • Keywords: "הנחה", "חיסכון", "כרטיס", "מועדון", "שובר", "זול"

Loading the Knowledge Base — LOAD ONLY WHAT YOU NEED

The full discounts.json is in the skill directory (same folder as this SKILL.md). It is ~20KB. Do not load the whole file.
Instead, use targeted extraction to keep token cost minimal.

Step 1 — Identify the category (always first)

python3 -c "
import json
d = json.load(open('discounts.json'))
store = 'שם_החנות'  # replace with the queried store
cat = d['quick_lookup']['store_to_category'].get(store)
best = d['quick_lookup']['best_sources_by_category'].get(cat, [])
print('category:', cat)
print('best_sources:', best)
"

Cost: reads only quick_lookup — ~200 tokens total.

Step 2 — Load just that category + source metadata

python3 -c "
import json
d = json.load(open('discounts.json'))
cat = 'supermarkets'  # from step 1
out = {
    'category_data': d['categories'][cat],
    'sources_meta': {k: d['sources'][k] for k in d['quick_lookup']['best_sources_by_category'][cat]},
    'cache_ttl_days': d['meta']['cache_ttl_days']
}
import sys; json.dump(out, sys.stdout, ensure_ascii=False, indent=2)
" 2>/dev/null

Cost: ~800–1,200 tokens (one category) vs ~5,000 (full file).
Savings: ~75% fewer tokens per query.

When the store is not in quick_lookup

Load only quick_lookup.store_to_category and tell the user the store isn't indexed yet, offer to check the HTZone or Poalim Wonder site.

Decision Logic

"Which card/club should I use at store X?"

  1. Extract category via Step 1 above
  2. Load category section via Step 2
  3. For each source in best_sources_by_category[category]:
    • Check type: pos_discount = automatic at checkout · voucher = buy in advance · cashback = retroactive
    • Check deals[] and cached_at — is the deal data fresh? (TTL = 30 days)
    • Use max_pct as fallback if deals are empty/stale
  4. Rank by effective_pct (deals) or max_pct (structural)
  5. Answer concisely in Hebrew

Response template:

ל[חנות]:

1. **[מקור]** — [עסקה ספציפית או % מקסימום]
2. **[מקור]** — [עסקה ספציפית או % מקסימום]

💡 [טיפ מעשי אחד]

"Where's cheapest for product X?"

General supermarket ranking (baked-in knowledge, no lookup needed):

  • Cheapest: רמי לוי, אושר עד
  • Good value: ויקטורי, יינות ביתן
  • Mid-range: שופרסל, מגה
  • Expensive: AM:PM, שופרסל אקספרס

For exact product prices → government XML (point to scraper, don't run it): https://github.com/OpenIsraeliSupermarkets/israeli-supermarket-scarpers

"How do I save most on X category?"

Load category → list all sources → explain the best combo (e.g. "buy a Poalim Wonder voucher AND pay with HTZone card if you have both").

Cache Refresh (On-Demand)

When to refresh: cached_at is null OR more than 30 days old AND user needs exact deal price.

How:

  1. Answer immediately using structural data (max_pct) — don't block the response
  2. Note: "המחיר המדויק דורש בדיקה"
  3. If user confirms they want live price → use browser to fetch source URL
  4. Parse deal rows → update deals[] + cached_at in discounts.json → save

Refresh URLs (verified): | Source | URL | |--------|-----| | poalim_wonder | https://www.bankhapoalim.co.il/he/Poalim-Wonder + /Shopping sub-page | | htzone_club | https://www.htzone.co.il/sale/1147 (requires login + JS render) | | htzone_pos | https://www.htzone.co.il/sale/62 (requires login + JS render) |

Model for cache refresh:

  • Simple pages (Poalim Wonder — no login required) → current model is sufficient
  • HTZone pages (login + JS required) → browser automation with logged-in session needed; if not available, respond with structural data only and note the limitation

Update the file after refresh:

python3 -c "
import json
path = 'discounts.json'
d = json.load(open(path))
from datetime import date
d['categories']['CATEGORY']['sources']['SOURCE']['deals'] = [/* new deals */]
d['categories']['CATEGORY']['sources']['SOURCE']['cached_at'] = str(date.today())
json.dump(d, open(path,'w'), ensure_ascii=False, indent=2)
print('updated')
"

Output Format

  • Language: Hebrew (group speaks Hebrew)
  • Tone: Short, practical, no fluff
  • Numbers: Always show exact ₪ amounts when available (₪300 → ₪259)
  • POS vs Voucher: Always clarify — automatic at checkout vs. buy in advance
  • Max length: 5 lines unless multiple options worth listing

Examples

"באיזה כרטיס כדאי בויקטורי?"

לויקטורי:
1. **פועלים וונדר** — שובר ₪300 ב-₪259 + 50 נקודות (13.7%)
2. **כרטיס הייטקזון** — עד 20% אוטומטית בקופה

💡 אם יש לך את שניהם — קנה שובר וונדר ושלם איתו עם כרטיס הייטקזון

"יש הנחה לוולט?"

וולט — פועלים וונדר: שובר ₪100 ב-₪80 + 25 נקודות (20% 🔥)

"איפה הכי זול לקנות?"

לסל קניות רגיל: רמי לוי / אושר עד הכי זולים.
אם יש לך פועלים וונדר — שובר רמי לוי ₪700 ב-₪620 (11.4%).

Maintenance

Quarterly (structural layer): Check if HTZone/Poalim benefits structure changed → update max_pct → update last_reviewed

On-demand (deal layer): Refresh only when user asks + cache is stale → update deals[] + cached_at

Limitations

  1. HTZone specific deals require login + JS render — structural max_pct used as fallback
  2. Gov XML price comparison not yet wired — reference only
  3. Max / Isracard deals not yet researched