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Automated daily Amazon market digest. Given the user's own ASINs (1-10) and any competitor ASINs they want included (up to 20), produces a daily change-detec...
---
name: amazon-daily-market-radar
description: >
Automated daily Amazon market digest. Given the user's own ASINs (1-10) and
any competitor ASINs they want included (up to 20), produces a daily
change-detection briefing on Amazon: price moves, BSR shifts, new entrants
in the surrounding category, review wave detection, stockout signals. Output is
a triaged alert dashboard (RED/YELLOW/GREEN) comparing today against
yesterday's snapshot.
Designed for unattended scheduled automation (cron-style daily run) — set
it once, get an alert digest every day.
Use when the user wants ongoing OPERATIONAL daily monitoring of their
products and the surrounding market — a "what changed since yesterday"
digest delivered automatically every day.
Use when user asks: what changed in my category today, daily category
briefing, set up daily monitoring, emerging brands alert, BSR shifts
daily, stockout signals, set-it-and-forget-it market watch.
Requires ZOODATA_API_KEY.
metadata:
version: "1.0.4"
author: SerendipityOneInc
homepage: https://github.com/SerendipityOneInc/ZooData-Skills
openclaw: {"requires": {"env": ["ZOODATA_API_KEY"]}, "primaryEnv": "ZOODATA_API_KEY"}
---
# ZooData — Amazon Daily Market Radar
> Set it. Forget it. Get alerted when it matters. 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}/data/` | Runtime: watchlist.json, last-run.json (auto-created) |
## Credential
Required: `ZOODATA_API_KEY`. Get free key at [zoodata.ai/api-keys](https://zoodata.ai/en/api-keys).
## Input (First Run)
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. `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 '<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 `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`
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
## On Missing Key
When `ZOODATA_API_KEY` is not set (verify via `python {skill_base_dir}/scripts/zoodata.py check` — exits 2 if no key in env or `~/.zoodata/config.json`): follow the **"On Missing Key"** protocol in `zoodata/SKILL.md` — STOP before any call, link the user to https://zoodata.ai/en/api-keys, and DO NOT produce a "partial analysis from public knowledge" / "for reference only" fallback as a substitute.
## On 401 Invalid Key
When `zoodata.py` returns code 401: follow the **"On 401 Invalid Key"** protocol in `zoodata/SKILL.md` — STOP further calls, tell the user the key was rejected and direct them to api-keys, do not fabricate missing data.
## On 402 Credit Exhausted
When `zoodata.py` returns code 402: follow the **"On 402 Credit Exhausted"** protocol in `zoodata/SKILL.md` — STOP further calls, report partial findings already gathered, do not fabricate missing data.
## Execution
1. `daily-radar --asins "asin1,asin2,..." [--keyword X] [--category Y]` (composite, auto-detects category from ASINs)
3. Compare against `{skill_base_dir}/data/last-run.json` for change detection (first run = baseline only, no alerts)
4. Generate alert-prioritized briefing → save snapshot to `{skill_base_dir}/data/last-run.json`
## Alert Rules
| Level | Triggers |
|-------|----------|
| 🔴 RED | Price drop >10% by competitor; BSR crash >50% (yours); 1-star spike (3+ in 24h) |
| 🟡 YELLOW | New competitor in Top 20; competitor price change 5-10%; BSR change 20-50%; brand share shift >2% |
| 🟢 GREEN | Competitor stock-out; your review velocity up; price band opportunity shift |
## 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
| Metric | Normal Range | Action Trigger | Likely Cause |
|--------|-------------|----------------|-------------|
| Price change | ±3% | >5% sustained 3+ days | Repricing strategy or promotion 🔍 |
| BSR shift | ±15% daily | >30% sustained or >50% single day | Stockout, promotion, or algorithm change 🔍 |
| Rating drop | ±0.1 | >0.2 in 7 days | Product quality issue or review attack 🔍 |
| Review velocity | ±20% | >50% spike | Vine program, review manipulation, or viral moment 🔍 |
| New entrant in Top 20 | 0-1/week | 3+ in one week | Market shift or seasonal demand 🔍 |
### 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 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.
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:
| 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: ~15-30 credits
Realtime×ASINs(5-15) + History(1-2) + Market/Brand(3) + Products(1) + Price(2) + Categories(1) + Reviews(1-3).
don't have the plugin yet? install it then click "run inline in claude" again.