APIClaw — Amazon Daily Market Radar

Collect in ONE message: ✅ my_asins (1-10) | 💡 competitor_asins (up to 20) | 📌 alert_preferences. Optional: keyword, category. Category is auto-detected from first tracked ASIN if not provided.

API Pitfalls (CRITICAL)

1. Category auto-detection: categoryPath is auto-detected from tracked ASINs. If category_source in output is inferred_from_search, confirm with user
2. All keyword-based endpoints MUST include --category; ASIN-specific endpoints do NOT
3. Use API fields directly: revenue=sampleAvgMonthlyRevenue (NEVER price×sales), sales=monthlySalesFloor, concentration=sampleTop10BrandSalesRate
4. reviews/analysis: needs 50+ reviews. Fallback chain when sample is insufficient:
   1. Lightweight: realtime/product ratingBreakdown — only star distribution, no themes
   2. Full 11-dim insights — bypass /reviews/analysis entirely: a. apiclaw.py reviews-raw --asin X → fetch up to 100 raw reviews (10 credits, ~60s) b. For each review: render Map prompt via apiclaw.py review-tag-prompt --review '<json>' and have your own LLM produce JSON tags (sentiment + 11 dimensions) c. Collect candidate phrases per dimension; for each dimension render Reduce prompt via apiclaw.py review-reduce-prompt --label-type X --candidates '[...]' and have your LLM produce semantic clusters d. apiclaw.py review-aggregate --reviews R --tagged T --clusters C → consumerInsights output compatible with /reviews/analysis
   3. Fallback caveats (apply to the 4-step chain above — lessons from end-to-end validation):
      • Working dir: WORK=/tmp/review_<ASIN>_$(date +%s) && mkdir -p $WORK
      • Step b CLI behavior: review-tag-prompt RENDERS the prompt only; YOUR LLM produces the JSON. Render once to learn the schema, then produce tags for all N reviews in one in-context pass (don't call the CLI N times).
      • Step c candidate extraction (Python one-liner): candidates = {d: sorted({el.strip().lower() for t in tagged for el in (t.get(d) or [])}) for d in DIMS}
      • Small-sample rule (reviewCount<50): demote single-mention items 📊→🔍; NEVER attach table-level or section-header 📊 when any row inside is 🔍; suppress "🔴 Critical" verdicts on count=1
      • Scope: fallback replaces ONLY the /reviews/analysis aggregation. This skill's primary workflow outputs (price/BSR/sales deltas, alerts, watchlist baseline) remain valid — do not re-run them.
5. Aggregation without categoryPath: severely distorted data

Execution

1. daily-radar --asins "asin1,asin2,..." [--keyword X] [--category Y] (composite, auto-detects category from ASINs)
2. Compare against {skill_base_dir}/data/last-run.json for change detection (first run = baseline only, no alerts)
3. Generate alert-prioritized briefing → save snapshot to {skill_base_dir}/data/last-run.json

Alert Rules

Change Detection Logic

• Price change >5% → 🔴
• BSR move >20% → 🟡
• New ASINs in top 20 (vs last run) → 🟡

Growth signal validation:

• 📊 Sustained: 7+ days consistent direction
• 🔍 Possible signal: 2-3 days of change
• 💡 Single-day spike: could be promotion/restock

Change Interpretation Guide

Action Recommendations by Alert Level

• 🔴 RED: Require immediate response — check inventory, match price if needed, investigate quality issues 💡
• 🟡 YELLOW: Monitor for 3-5 days before acting — may be temporary fluctuation 💡
• 🟢 GREEN: Opportunity window — act within 1-2 weeks before competitors notice 💡

Output Spec

First run: "Baseline Established" — KPI Dashboard (current snapshot) only, no alerts.

Subsequent runs: Alert Summary → RED Alerts → YELLOW Alerts → GREEN Opportunities → KPI Dashboard (today vs yesterday) → Competitor Movement → Market Shifts → Action Items → Data Provenance → API Usage.

Language (required)

Output language MUST match the user's input language. If the user asks in Chinese, the entire report is in Chinese. If in English, output in English. Exception: API field names (e.g. monthlySalesFloor, categoryPath), endpoint names, technical terms (e.g. ASIN, BSR, CR10, FBA, credits) remain in English.

Disclaimer (required, at the top of every report)

Data is based on APIClaw API sampling as of [date]. Monthly sales (monthlySalesFloor) are lower-bound estimates. This analysis is for reference only and should not be the sole basis for business decisions. Validate with additional sources before acting.

Confidence Labels (required, tag EVERY conclusion)

• 📊 Data-backed — direct API data (e.g. "CR10 = 54.8% 📊")
• 🔍 Inferred — logical reasoning from data (e.g. "brand concentration is moderate 🔍")
• 💡 Directional — suggestions, predictions, strategy (e.g. "consider entering $10-15 band 💡")

Rules: Strategy recommendations are NEVER 📊. Anomalies (>200% growth) are always 💡. User criteria override AI judgment.

Aggregate-label rule (applies to ALL report output, not just fallback): NEVER attach 📊 to ANY element that aggregates or groups underlying content when ANY piece of that content is 🔍 or 💡. "Aggregate/grouping elements" include:

• Section headers at EVERY level (#, ##, ###, ####) — including top-level summary sections like "Overall Score", "Verdict", "Executive Summary"
• Summary/score lines anywhere in the report (e.g. ## Overall Score — 27/100 · Grade F 📊 is WRONG if any Basis row inside is 🔍)
• Table column headers in comparison tables (e.g. **Target ASIN** 📊 as a column label is WRONG if any cell in that column contains 🔍)
• Table row headers or row-aggregation labels (when the row aggregates multiple cells of mixed confidence)
• Any other visual grouping label — bullet-list group titles, callout box titles, etc.

A group-level 📊 implies the whole block/column/row is data-backed, which smuggles inferred/directional content into the 📊 tier via visual grouping. Either (a) omit the group-level label entirely (preferred when content mixes tiers), or (b) use the LOWEST confidence present inside (🔍 if any underlying content is 🔍; 💡 if any is 💡). This is a universal output-quality rule — it applies regardless of which fallback path (if any) was triggered.

Emoji reservation rule (closely related): The three confidence symbols 📊 🔍 💡 are RESERVED for confidence labeling. NEVER use them as decorative prefixes on section headers, table headers, or any aggregate element — even when you also include a correct confidence suffix on the same line. Example:

• ❌ WRONG: ## 📊 Overall Score — 27/100 · Grade F 🔍 (the leading 📊 reads as a data-backed claim even though the trailing 🔍 is correct)
• ✅ RIGHT: ## Overall Score — 27/100 · Grade F 🔍 (no decorative emoji, just the proper confidence suffix)
• ✅ RIGHT: ## 🎯 Overall Score — 27/100 · Grade F 🔍 (use non-reserved decorative icons like 🎯 🧭 📋 📝 📂 🏁 🚨 🏆 🔔 when a visual prefix is desired)

Decorative emoji ≠ confidence label — but from a reader's perspective, a leading 📊/🔍/💡 is indistinguishable from a confidence claim. Reserve these three symbols EXCLUSIVELY for confidence annotation to avoid ambiguity.

Sample bias: "Based on Top [N] by sales volume; niche/new products may be underrepresented."

Data Provenance (required)

Include a table at the end of every report:

Extract endpoint and params from _query in JSON output. Add notes: sampling method, T+1 delay, realtime vs DB, minimum review threshold, etc.

API Usage (required)

Extract from meta.creditsConsumed per response. End with Credits remaining: N.

API Budget: ~15-30 credits

Realtime×ASINs(5-15) + History(1-2) + Market/Brand(3) + Products(1) + Price(2) + Categories(1) + Reviews(1-3).