---
name: shopping
slug: shopping
version: 1.0.0
description: Help users make better purchase decisions — research, comparison, timing, and avoiding buyer's remorse.
homepage: https://clawic.com/skills/shopping
metadata:
  category: lifestyle
  skills:
  - shopping
  - buying
  - deals
  - comparison
  - consumer
  clawdbot:
    emoji: 🛍️
    displayName: Shopping
---

## Before Recommending Products

- Ask budget range first — recommendations without budget waste time
- Ask use case specifically — "laptop" means different things for gaming vs email
- Ask what they've tried or owned — past experience reveals preferences
- Ask timeline — urgent need vs can wait for sale changes strategy
- One recommendation with reasoning beats list of options

## Research Approach

- Check reviews from multiple sources — single source can be biased/paid
- Filter 1-star reviews for real issues — ignore "shipping was slow" complaints
- Look for long-term reviews — 6-month updates reveal durability
- Reddit/forums often more honest than YouTube — less sponsorship pressure
- Check if newer model coming — buying at end of cycle means faster obsolescence

## When to Suggest Not Buying

- "I might need this someday" — future need isn't current need
- Upgrading something that works fine — marginal improvement, full price
- Buying to solve problem that isn't the product — new running shoes won't create running habit
- Emotional purchase after bad day — suggest waiting 48 hours
- Sale pressure: "70% off ends tonight" — if didn't need it yesterday, don't need it today

## Price and Timing

- Track price history: CamelCamelCamel for Amazon, Honey for others — "sale" might be normal price
- Major sales: Black Friday, Prime Day, end of season — worth waiting if not urgent
- Refurbished/open-box: often 20-40% off, same warranty — underrated option
- Credit card price protection — check if card offers it before buying
- Price match policies — many stores match competitors, just ask

## Comparison Framework

| Factor | Questions |
|--------|-----------|
| Must-haves | What features are non-negotiable? |
| Nice-to-haves | What would be bonus but not essential? |
| Deal-breakers | What would make you return it? |
| Total cost | Accessories, subscription, maintenance? |
| Longevity | How long until you need to replace? |

Two good options? Pick the one easier to return.

## Red Flags

- Too many 5-star reviews with similar language — likely fake
- Brand new product with hundreds of reviews — suspicious timing
- "Amazon's Choice" means nothing about quality — just algorithm pick
- Influencer discount codes — they profit from your purchase
- Pressure tactics: countdown timers, "only 2 left" — manufactured urgency

## Return Policy Awareness

- Check return policy before buying — some categories no returns
- Keep packaging until sure — needed for returns
- Credit card extended return windows — some cards add 90 days
- Restocking fees on electronics — factor into decision
- "Final sale" means final — no exceptions

## Category-Specific Guidance

- **Electronics:** Refresh cycles matter, buy early in cycle not end
- **Clothing:** Size charts vary wildly, read size reviews specifically
- **Furniture:** Measure twice, assembly difficulty in reviews
- **Appliances:** Check repair frequency ratings (not just features)
- **Subscriptions:** Calculate yearly cost, check cancellation ease

## Post-Purchase

- Don't keep researching after buying — decision fatigue, regret spiral
- Found cheaper after purchase? Many stores price-match within window
- Problem with product? Contact support before leaving bad review
- Actually use the thing — purchase isn't the goal, use is


---
Source attribution: clawic
Original project: https://github.com/clawic/skills/blob/f825206afbf1c697202533a9187be68c7b697e0e/skills/shopping/SKILL.md
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2026 Ivan G. Davila

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: clawic
Original project: https://github.com/clawic/skills/blob/f825206afbf1c697202533a9187be68c7b697e0e/skills/shopping/SKILL.md
Attribution basis: Source repository record

Fields of work: Marketing, Sales
Roles: Customer Success Manager, Marketing Analyst, Sales Engineer
Specialties: Specialty not specified
Tools: Tool not specified
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: b5a256286c72476f6687508f8f77e06cce4883f02df0dc451dd5a56d482a72e6