Amazon Competitor Intelligence Monitor

ZooData — Competitor Intelligence Monitor

> Know your enemy. Two modes: Full Scan + Quick Check. Respond in user's language.

Files

| File | Purpose | |------|---------| | {skill_base_dir}/scripts/zoodata.py | Execute for all API calls (run --help for params) | | {skill_base_dir}/references/reference.md | Load for exact field names or response structure | | {skill_base_dir}/monitor-data/ | Runtime storage (auto-created): config.json, baseline.json, history/, alerts.json |

Credential

Required: ZOODATA_API_KEY. Get free key at zoodata.ai/api-keys.

Input

Required: keyword or ASIN(s). Optional: myasin, competitorasins, brand. If only ASIN given → derive keyword via product --asin then ask user to confirm. Brand queries MUST also include confirmed --category.

API Pitfalls (CRITICAL)

1. Category auto-detection: categoryPath is auto-detected from keyword, ASIN, or top search result. If category_source in output is inferred_from_search, MUST confirm with user before trusting results
2. All keyword-based endpoints MUST include --category; ASIN-specific endpoints do NOT need it
3. Brand + category: a brand sells across categories — only analyze within locked subcategory
4. Use API fields directly: revenue=sampleAvgMonthlyRevenue (NEVER price×sales), sales=monthlySalesFloor, concentration=sampleTop10BrandSalesRate
5. reviews/analysis: needs 50+ reviews. Fallback chain when sample is insufficient:
6. Lightweight: realtime/product ratingBreakdown — only star distribution, no themes
7. Full 11-dim insights — bypass /reviews/analysis entirely:

a. zoodata.py reviews-raw --asin X → fetch up to 100 raw reviews (10 credits, ~60s) b. For each review: render Map prompt via zoodata.py review-tag-prompt --review '' and have your own LLM produce JSON tags (sentiment + 11 dimensions) c. Collect candidate phrases per dimension; for each dimension render Reduce prompt via zoodata.py review-reduce-prompt --label-type X --candidates '[...]' and have your LLM produce semantic clusters d. zoodata.py review-aggregate --reviews R --tagged T --clusters C → consumerInsights output compatible with /reviews/analysis

1. Fallback caveats (apply to the 4-step chain above — lessons from end-to-end validation):

• Working dir: WORK=/tmp/review__$(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 threshold | FBA↔FBM switch | Competitor stock-out |

| BSR crash > threshold | Rating change | Bullet/image changes | | Buy Box owner changed | Abnormal review growth | Variant added/removed | | | Title modified | |

Competitive Score (per competitor, 1-100)

| Dimension | Weight | 80-100 (Strong) | 50-79 (Moderate) | 0-49 (Weak) | |-----------|--------|-----------------|-------------------|-------------| | Sales Dominance | 25% | Top 3 in category, >5K units/mo 📊 | Top 20, 1K-5K units/mo 📊 | Below Top 20, 8% 1-star 📊 | | Trend Momentum | 15% | BSR improving 30d, sales growth >10% 🔍 | BSR stable, flat sales 🔍 | BSR declining, sales drop 🔍 |

Competitive Threat Level

| Total Score | Threat | Interpretation | |-------------|--------|---------------| | 80-100 | 🔴 Dominant | Hard to compete head-on; find differentiation or avoid price band 💡 | | 50-79 | 🟡 Competitive | Beatable with better listing, pricing, or reviews 💡 | | 0-49 | 🟢 Vulnerable | Weak competitor; opportunity to capture share 💡 |

Market Structure Analysis

• CR10 > 70%: Concentrated market — new entrants need strong differentiation or niche positioning 🔍
• CR10 40-70%: Moderately competitive — room for well-positioned products 🔍
• CR10 25%: Category leader dominance — avoid direct competition in their price band 💡
• New SKU rate > 15%: Active market with frequent new entrants 📊
• **New SKU rate Data is based on ZooData 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.

Data Provenance (required)

Include a table at the end of every report:

| Data | Endpoint | Key Params | Notes | |------|----------|------------|-------| | (e.g. Market Overview) | markets/search | categoryPath, topN=10 | 📊 Top N sampling, sales are lower-bound | | ... | ... | ... | ... |

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)

| Endpoint | Calls | Credits | |----------|-------|---------| | (each endpoint used) | N | N | | Total | N | N |

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

API Budget

Full Scan: ~28-35 credits (all 11 endpoints via composite). Quick Check: ~5-10 credits (realtime/product × N ASINs).

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: SerendipityOneInc
• Source: SerendipityOneInc/ZooData-Skills
• License: MIT
• Homepage: https://apiclaw.io/

Install and usage instructions live in the source repository linked above.