---
slug: "amazon-daily-market-radar-x-6"
source_type: "clawhub"
source_url: "https://clawhub.ai/skills/amazon-daily-market-radar"
repo: ""
source_file: "description"
---
---
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: "1.0.8"
  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).

## Capabilities & Data Flow

- **Network**: only `https://api.zoodata.ai` (Bearer `ZOODATA_API_KEY`). Setting `ZOODATA_BASE_URL` to an untrusted host (anything other than `api.zoodata.ai` / `*.zoodata.ai` / localhost) makes the CLI **refuse the request and withhold the key** — the Bearer token is never sent to an untrusted host.
- **Execution**: bundled shared ZooData CLI `{skill_base_dir}/scripts/zoodata.py` (Python 3, stdlib-only). The shared CLI exposes ALL ZooData endpoints as subcommands; this skill's workflows use: `daily-radar`, `market`, `products`, `competitors`, `product`, `price-band-overview`, `history`, `check`, plus the review fallback toolkit (`reviews-raw` / `review-tag-prompt` / `review-reduce-prompt` / `review-aggregate`). Do not invoke unrelated subcommands for this skill's tasks.
- **Local files**: baseline snapshots `{skill_base_dir}/data/last-run.json` and `{skill_base_dir}/data/watchlist.json`; a temporary `/tmp/review_<ASIN>_<timestamp>/` working dir during the review fallback; reads the optional credential store `~/.zoodata/config.json`.
- **Sent to the API**: keywords, category paths, ASINs, marketplace/date and numeric filter values only. **Never sent**: budget, experience level, risk tolerance, or any other user-profile text — profile inputs map client-side to numeric filters.
- **Credits**: every API call consumes account credits. For broad or ambiguous requests, state the estimated credit cost and confirm with the user before running multi-call scans. The composite `daily-radar` command executes ~14+ API calls (~15-30 credits) in ONE invocation and has NO skip/trim flags — under a credit cap, use the granular commands instead.

## 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).