name: price-gap-monitor description: Monitor product-level and category-level price gaps, promo shifts, and visible trend signals using browser-collected marketplace data or user-provided price snapshots. Use when the user wants to check whether a specific product price changed, compare a listing across platforms, or understand how a category price band is moving.

Price Gap Monitor

Track visible price movement without pretending to know private marketplace data.

This skill supports two operating modes under the same name.

Quick Reference

Solves

• Blind pricing decisions: Seller is setting or adjusting prices without checking what competitors actually charge on visible marketplaces.
• Cross-platform price drift: Same product listed at different prices across Amazon, Walmart, Temu, TikTok Shop — seller doesn't know where they're under or over.
• Promo impact blindness: Competitor ran a flash sale or coupon and the seller missed it, losing traffic without understanding why.
• Category-level mispricing: Seller positioned in a price band that doesn't match the visible market center, either leaving money on the table or pricing themselves out.
• Snapshot vs. trend confusion: Seller reacts to a single price check as if it's a trend, making premature adjustments.
• Margin floor violations: Price changes made reactively without checking whether the new price still clears minimum margin requirements.
• Fabricated data reliance: Previous analyses or tools claimed access to private sales data, BSR history, or internal marketplace metrics — this skill provides honest, evidence-based alternatives.

Mode A — Product-level price trend monitoring

Use this mode when the user asks about:

• one specific product
• one brand-specific model
• one ASIN / listing / SKU
• one named product across multiple platforms

Mode B — Category price-band monitoring

Use this mode when the user asks about:

• a product category
• a keyword-defined market
• a visible price band
• cross-platform category pricing patterns

Browser-first guidance

When live browsing is available, prefer browser-collected data over asking the user to provide snapshots. The browser can visit marketplace search pages, product listing pages, and category pages to collect visible price signals in real time.

When to suggest logged-in browsing

Suggest the user log in to their marketplace account when:

• guest pages show limited results or fail to load correctly
• location, cart, or account state is clearly affecting visible listings
• the task requires going deeper than a shallow guest snapshot

Suggested user-facing reminder: - "If you want a cleaner and more complete Amazon read, log in first. Logged-in browsing usually gives more stable category pages, better listing continuity, and fewer interruptions."

Do not claim login guarantees full data access. Present it as a practical way to improve visibility and continuity.

Core job

The goal is to produce a decision-ready price snapshot with honest trend interpretation.

This skill may use: 1. user-provided price snapshots, or 2. browser-collected public marketplace data

It should: - collect visible price and promo signals - compare listings or price bands - distinguish current snapshot from repeated trend evidence - recommend whether to watch, react, or gather more data first

It must not fabricate hidden marketplace history, real sales counts, or full competitive intelligence that isn't visible on public pages.

Inputs

Input type A — user-provided snapshots

• competitor price tables
• prior exported marketplace snapshots
• your current price baseline
• target margin floor
• promo windows or campaign timing

Input type B — browser-collected public data

• a product model name
• an ASIN / SKU / listing URL
• a category keyword
• target platforms (Amazon, Temu, TikTok Shop, Walmart, etc.)
• market / locale (US, UK, JP, DE, etc.)

Workflow

Mode A — Product-level workflow

1. Define the exact product scope.
2. Confirm the specific product name, model, ASIN, or SKU.
3. Identify which platforms to check (default: Amazon, Walmart, Temu).

4. Record the user's current price and margin floor.

5. Collect visible public signals.

6. For each platform, search for the exact product or closest match.
7. Record: listing price, shipping cost, any visible coupons or promos, seller name, listing date if visible.
8. Take note of "Sponsored" vs organic placement.

9. Capture the timestamp of each observation.

10. Normalize comparison points.

11. Convert all prices to the same currency.
12. Calculate unit price if products come in different pack sizes.
13. Add shipping to get landed cost where visible.

14. Flag any listings that are clearly different products (wrong model, refurbished, etc.).

15. Determine evidence strength.

16. Single snapshot = "current position only, no trend."
17. Two snapshots with time gap = "directional signal, not confirmed trend."

18. Three or more time-separated snapshots = "visible trend with stated confidence."

    这个技能的最新版本可以在7w4.net小葱技能站找到。

19. Produce the result.

20. Fill in the output template (see references/output-template.md).
21. Include executive summary, snapshot data, comparison, trend assessment, and recommendation.
22. Never claim more confidence than the evidence supports.

Mode B — Category-level workflow

1. Define the category scope.
2. Confirm the category keyword, price band of interest, and target platforms.

3. Ask whether the user wants top-10, top-20, or broader coverage.

4. Collect visible top listings.

5. Search each platform for the category keyword.
6. Record the first 10-20 organic results: price, title, seller, rating count, any promo badges.

7. Note any sponsored listings separately.

8. Cluster the market.

9. Group listings into price bands (e.g., budget < $15, mid $15-30, premium > $30).
10. Calculate band center, min, max for each cluster.

11. Identify where the user's product sits relative to clusters.

12. Determine evidence strength.

13. Apply the same snapshot vs. trend rules as Mode A.

14. For categories, also note: search result count, how many pages deep you went, any platform-specific filters applied.

15. Produce the result.

16. Fill in the category-level output template.
17. Include band analysis, competitive position, and recommended pricing action.

Trend interpretation rules

1. Single snapshot rule
2. If only one fresh snapshot is available, describe the result as "current observed position" — never as a trend, movement, or shift.

3. Recommended language: "As of [date], the visible price is..."

4. Two-snapshot rule

5. With two time-separated observations, label the change as a "directional signal" and explicitly note the time gap.

6. Recommended language: "Between [date1] and [date2], the visible price moved from X to Y — this is a directional signal, not a confirmed trend."

7. Trend-confirmed rule

8. Three or more consistent, time-separated observations in the same direction may be labeled a "visible trend."

9. Always state the number of observations, the time span, and the direction.

10. Partial coverage rule

11. If less than 60% of the market is visible, clearly label the result as partial coverage.

12. Never present partial scraping as full category or full brand coverage.

13. History rule

14. Never fabricate prior price history.
15. Never imply long-term movement when only current public pages were checked once.

Worked Example 1 — Product-level (Mode A)

User request: "Check how my silicone baking mat is priced vs competitors on Amazon and Walmart. My current price is $12.99, margin floor is $9.50."

Step 1 — Define scope: Product: Silicone Baking Mat, Half Sheet Size. Platforms: Amazon US, Walmart US. Current price: $12.99. Margin floor: $9.50.

Step 2 — Collect signals (browser):

Step 3 — Normalize: - Convert 2-packs to per-unit: KitchenPro = $5.99/mat, Walmart 2pk = $6.24/mat. - Add shipping: ValueBake landed = $13.48 (2pk not comparable — single mat). - Flag: KitchenPro and Walmart 2pk are multi-packs; direct comparison requires noting pack size.

Step 4 — Evidence strength: Single snapshot (one collection session on 2025-05-01). Result: "Current position only, no trend."

Step 5 — Result summary: "As of May 1 2025, your $12.99 single mat sits in the mid-range. Single-mat competitors range $9.99–$14.49 on Amazon and $10.88–$12.47 on Walmart. One Amazon competitor (ValueBake) is running a Lightning Deal at $9.99 + $3.49 shipping. Your price clears the $9.50 margin floor. Recommendation: No immediate action needed. The Lightning Deal is temporary. Suggest re-checking in 48 hours to confirm ValueBake returns to regular pricing."

Worked Example 2 — Category-level (Mode B)

User request: "What does the portable blender category look like on Amazon US right now? I'm launching at $24.99."

Step 1 — Define scope: Category: "portable blender." Platform: Amazon US. User's planned launch price: $24.99. Coverage: top 15 organic results.

Step 2 — Collect top listings:

Step 3 — Cluster: - Budget (< $20): 4 listings, band center $17.49 - Mid ($20–$30): 6 listings, band center $24.32 - Premium (> $30): 5 listings, band center $37.99

Step 4 — Evidence strength: Single snapshot, 15 of estimated 400+ results. Partial coverage (~4%).

Step 5 — Result summary: "As of this snapshot, the mid-band ($20–$30) is the most crowded segment with 6 of the top 15 results. Your $24.99 launch price sits almost exactly at the mid-band center ($24.32). The segment leader (BlendJet, $33.99) has massive review count dominance. At $24.99 you'll compete directly with PopBabies ($23.99 + 10% coupon = ~$21.59 effective) and KOIOS ($21.99 with Lightning Deal). Recommendation: Your price is viable for launch but you'll face coupon/deal pressure from established mid-band sellers. Consider whether a launch coupon at $21.99 would help with initial velocity without dropping below margin floor."

Common mistakes

1. Calling a single snapshot a "trend" — One price check is a position, not movement. Always label evidence strength honestly.

2. Ignoring pack-size differences — Comparing a 2-pack at $11.97 to a single item at $12.99 without normalizing to per-unit price leads to wrong conclusions.

3. Forgetting shipping costs — A $9.99 item with $4.99 shipping is more expensive than a $13.99 Prime item. Always calculate landed cost.

4. Treating Lightning Deals as permanent — Temporary promotions should be flagged as time-limited. Don't recommend permanent price cuts to match a 6-hour deal.

5. Claiming "full market coverage" — Checking the first page of Amazon results is not a full market scan. State exactly how many listings were checked and from which platforms.

6. Fabricating price history — Never say "prices have been declining over the past quarter" unless you have 3+ time-separated data points showing this. If you only checked once, say so.

7. Mixing sponsored and organic listings — Sponsored placements appear at different prices due to advertising investment. Flag them separately and don't include them in organic price band calculations.

8. Recommending below margin floor — Always check the user's stated margin floor before suggesting a price drop. If the competitive pressure requires going below the floor, flag this explicitly as a trade-off decision.

9. Ignoring platform-specific pricing rules — Some platforms (Walmart) have price parity requirements. Don't recommend platform-specific pricing without noting potential policy conflicts.

10. Presenting competitor prices without context — A low-priced competitor with 12 reviews is different from one with 12,000 reviews. Include rating count and review velocity when available as context for competitive positioning.

Resources

• Output template — Structured output format for both Mode A and Mode B results.
• Pricing data collection guide — How to collect, normalize, and validate pricing data from marketplaces.
• Platform comparison reference — Platform-specific pricing nuances for Amazon, Walmart, Temu, TikTok Shop, and others.

这个 Skill 质量很好，文档逻辑清晰、规则明确，对数据收集方式和趋势判断标准都有详细说明。45项质量检查清单很实用，能帮助避免夸大结论或遗漏重要信息。平台指南写得很细致，涵盖了主要电商平台的特点。唯一不足是示例太少，只有一个不完整的价格对比演示，缺少完整的输出样例作为参考。