Price Gap Monitor
by @leooooooow
Monitor product-level and category-level price gaps, promo shifts, and visible trend signals using browser-collected marketplace data or user-provided price...
clawhub install price-gap-monitorπ About This Skill
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
| Decision | Strong | Acceptable | Weak | |---|---|---|---| | Data source selection | Browser-collected live snapshots from 3+ platforms with normalized timestamps | User-provided snapshots from 2 platforms with date context | Single screenshot with no timestamp or platform context | | Price normalization | Unit price + currency + shipping aligned across all listings | Prices compared in same currency but shipping not factored | Raw prices compared across different units or currencies | | Trend evidence strength | 3+ time-separated snapshots showing consistent direction | 2 snapshots with clear delta and date labels | Single snapshot described as a "trend" | | Coverage labeling | Explicit platform list, search terms used, and gaps noted | Platforms listed but coverage gaps not mentioned | "Full market analysis" claimed from partial data | | Competitive context | Price positioned against 5+ visible competitors with ranking | Compared to 2-3 key competitors | Compared to a single competitor or no context | | Recommendation quality | Specific action with margin impact estimate and timeline | Directional recommendation with general reasoning | Vague "monitor the market" without actionable next steps | | Anomaly handling | Outliers flagged, investigated, and explained or excluded | Outliers noted but not investigated | Outliers silently included or excluded without mention | | Evidence honesty | Every claim tied to a visible, timestamped source | Most claims sourced but some inferred | Fabricated history or unverifiable claims presented as fact |
Solves
Mode A β Product-level price trend monitoring
Use this mode when the user asks about:
Mode B β Category price-band monitoring
Use this mode when the user asks about:
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:
Suggested user-facing reminder:
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:
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 β user-provided snapshots
Input type B β browser-collected public data
Workflow
Mode A β Product-level workflow
1. Define the exact product scope. - Confirm the specific product name, model, ASIN, or SKU. - Identify which platforms to check (default: Amazon, Walmart, Temu). - Record the user's current price and margin floor.
2. Collect visible public signals. - For each platform, search for the exact product or closest match. - Record: listing price, shipping cost, any visible coupons or promos, seller name, listing date if visible. - Take note of "Sponsored" vs organic placement. - Capture the timestamp of each observation.
3. Normalize comparison points. - Convert all prices to the same currency. - Calculate unit price if products come in different pack sizes. - Add shipping to get landed cost where visible. - Flag any listings that are clearly different products (wrong model, refurbished, etc.).
4. Determine evidence strength. - Single snapshot = "current position only, no trend." - Two snapshots with time gap = "directional signal, not confirmed trend." - Three or more time-separated snapshots = "visible trend with stated confidence."
5. Produce the result.
- Fill in the output template (see references/output-template.md).
- Include executive summary, snapshot data, comparison, trend assessment, and recommendation.
- Never claim more confidence than the evidence supports.
Mode B β Category-level workflow
1. Define the category scope. - Confirm the category keyword, price band of interest, and target platforms. - Ask whether the user wants top-10, top-20, or broader coverage.
2. Collect visible top listings. - Search each platform for the category keyword. - Record the first 10-20 organic results: price, title, seller, rating count, any promo badges. - Note any sponsored listings separately.
3. Cluster the market. - Group listings into price bands (e.g., budget < $15, mid $15-30, premium > $30). - Calculate band center, min, max for each cluster. - Identify where the user's product sits relative to clusters.
4. Determine evidence strength. - Apply the same snapshot vs. trend rules as Mode A. - For categories, also note: search result count, how many pages deep you went, any platform-specific filters applied.
5. Produce the result. - Fill in the category-level output template. - Include band analysis, competitive position, and recommended pricing action.
Trend interpretation rules
1. Single snapshot rule - If only one fresh snapshot is available, describe the result as "current observed position" β never as a trend, movement, or shift. - Recommended language: "As of [date], the visible price is..."
2. Two-snapshot rule - With two time-separated observations, label the change as a "directional signal" and explicitly note the time gap. - Recommended language: "Between [date1] and [date2], the visible price moved from X to Y β this is a directional signal, not a confirmed trend."
3. Trend-confirmed rule - Three or more consistent, time-separated observations in the same direction may be labeled a "visible trend." - Always state the number of observations, the time span, and the direction.
4. Partial coverage rule - If less than 60% of the market is visible, clearly label the result as partial coverage. - Never present partial scraping as full category or full brand coverage.
5. History rule - Never fabricate prior price history. - Never imply long-term movement when only current public pages were checked once.
Worked Example 1 β 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 β Define scope: Product: Silicone Baking Mat, Half Sheet Size. Platforms: Amazon US, Walmart US. Current price: $12.99. Margin floor: $9.50.
Step 2 β Collect signals (browser):
| Platform | Listing | Price | Ship | Promo | Seller | Timestamp | |---|---|---|---|---|---|---| | Amazon | Silicone Baking Mat Set (2pk) | $11.97 | Free (Prime) | 5% coupon | KitchenPro | 2025-05-01 14:30 UTC | | Amazon | Premium Silicone Mat - Half | $14.49 | Free (Prime) | None | BakeRight | 2025-05-01 14:31 UTC | | Amazon | Silicone Baking Mat | $9.99 | +$3.49 | Lightning Deal | ValueBake | 2025-05-01 14:31 UTC | | Walmart | Silicone Baking Mat | $10.88 | Free (W+) | Rollback | MainStay | 2025-05-01 14:35 UTC | | Walmart | Mainstays Silicone Mat 2pk | $12.47 | Free (W+) | None | Walmart | 2025-05-01 14:36 UTC |
Step 3 β Normalize:
Step 4 β Evidence strength: Single snapshot (one collection session on 2025-05-01). Result: "Current position only, no trend."
Step 5 β Result summary: "As of May 1 2025, your $12.99 single mat sits in the mid-range. Single-mat competitors range $9.99β$14.49 on Amazon and $10.88β$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 β 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 β Define scope: Category: "portable blender." Platform: Amazon US. User's planned launch price: $24.99. Coverage: top 15 organic results.
Step 2 β Collect top listings:
| Rank | Title (short) | Price | Rating Count | Promo | Seller | |---|---|---|---|---|---| | 1 | BlendJet 2 | $33.99 | 142,000 | None | BlendJet | | 2 | PopBabies Personal | $23.99 | 28,500 | 10% coupon | PopBabies | | 3 | Hamilton Beach | $19.99 | 15,200 | None | Hamilton | | 4 | Ninja Blast | $39.99 | 8,400 | None | Ninja | | 5 | KOIOS USB Blender | $21.99 | 12,100 | Lightning | KOIOS | | ... | (10 more listings) | $14.99β$45.99 | varies | varies | varies |
Step 3 β Cluster:
Step 4 β Evidence strength: Single snapshot, 15 of estimated 400+ results. Partial coverage (~4%).
Step 5 β Result summary: "As of this snapshot, the mid-band ($20β$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" β One price check is a position, not movement. Always label evidence strength honestly.
2. Ignoring pack-size differences β 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 β 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 β 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" β 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 β 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 β 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 β 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 β Some platforms (Walmart) have price parity requirements. Don't recommend platform-specific pricing without noting potential policy conflicts.
10. Presenting competitor prices without context β 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.