Shopping Skill

Use this skill for robust deal hunting across providers with market-baseline validation and savings analysis.

Pack Contents

• artifacts/deal-hunting-workflows.md
• assets/templates/deals-context.json
• assets/templates/deals-table.md
• assets/templates/market-analysis.json
• assets/templates/deal-thresholds.json
• scripts/run-shopping.sh
• scripts/normalize-offers.sh
• scripts/render-deals.sh
• scripts/analyze-market.sh
• scripts/run-deal-hunt.sh
• scripts/validate-skill-assets.sh
• Shared robustness matrix: ../opendevbrowser-best-practices/artifacts/browser-agent-known-issues-matrix.md

Fast Start

./skills/opendevbrowser-shopping/scripts/validate-skill-assets.sh

./skills/opendevbrowser-shopping/scripts/run-shopping.sh "wireless earbuds" context best_deal

./skills/opendevbrowser-shopping/scripts/run-deal-hunt.sh "wireless earbuds" "shopping/amazon,shopping/walmart"

Deal-Hunting Model

Use a two-layer check for each offer:

1. Provider discount check

• Compare listed total to provider anchor price (MSRP/list/original) when available.
• Capture absolute savings and percentage savings.

1. Market baseline check

• Compare listed total against cross-provider market average and median.
• Flag offers that are truly cheaper than market, not just marked "on sale".

Parallel Multitab Alignment

• Apply shared concurrency policy from ../opendevbrowser-best-practices/SKILL.md ("Parallel Operations").
• Validate shopping workflows across managed, extension, and cdpConnect when browser-backed provider paths are exercised.
• Keep one session per worker for parallel offer collection and avoid session-level target contention.

Savings Math

Per offer:

• total_price = item_price + shipping
• anchor_savings_abs = max(anchor_price - total_price, 0)
• anchor_savings_pct = anchor_savings_abs / anchor_price * 100

Per market group (same currency):

• market_avg = average(total_price)
• market_median = median(total_price)
• market_savings_abs = market_avg - offer_total
• market_savings_pct = market_savings_abs / market_avg * 100

This captures both:

• high-percentage discounts (for example 50% off)
• high-absolute savings (for example $500 saved)

Robustness Coverage (Known-Issue Matrix)

Matrix source: ../opendevbrowser-best-practices/artifacts/browser-agent-known-issues-matrix.md

• ISSUE-06: rate-limit/backoff handling while collecting offers
• ISSUE-09: dedupe and pagination drift controls in offer collection
• ISSUE-10: currency normalization and same-currency grouping
• ISSUE-11: weak/missing anchor price detection
• ISSUE-12: stale price and unsupported claim controls

Canonical Workflow

1. Run shopping search across selected providers.
2. Normalize offers into stable records.
3. Compute market analysis with analyze-market.sh.
4. Render markdown/json summary for user decision.

opendevbrowser shopping run --query "<query>" --providers shopping/amazon,shopping/walmart --mode json --output-format json

Classification Heuristics

Default tags from analysis script:

• high_percent when anchor discount percent exceeds threshold.
• high_absolute when anchor discount absolute exceeds threshold.
• high_value when both are true.
• market_beating when offer is materially below market average.

Threshold defaults are in assets/templates/deal-thresholds.json.

Confidence model:

• score combines sample size, anchor coverage, and freshness coverage.
• warnings call out low-sample, missing-anchor, and stale/missing timestamp risk.

Good Deal Decision Rules

Mark as strong only when at least one is true:

• anchor discount is material and market gap is positive
• market price is materially below average/median even without anchor

Avoid false positives:

• anchor discount exists but market total is not competitive
• low total due to unavailable stock or hidden constraints

References

• FTC Guides Against Deceptive Pricing (16 CFR 233): https://www.ecfr.gov/current/title-16/chapter-I/subchapter-B/part-233
• NIST SP 1181 (unit pricing and value comparison): https://www.nist.gov/publications/unit-pricing-guide
• Schema.org Offer (price metadata): https://schema.org/Offer