amazon-daily-market-radar

SKILL.md

---
name: amazon-daily-market-radar
description: >
  Automated daily Amazon market digest. Given the user's own ASINs (1-10)
  and any competitor ASINs (up to 20), produces a daily change-detection
  briefing: 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).
  Use when the user EXPLICITLY requests ongoing OPERATIONAL daily
  monitoring of their products and the surrounding market — a "what
  changed since yesterday" digest.
  Use when user asks: set up daily market monitoring for my ASINs, run
  my daily radar, what changed in my tracked market since yesterday,
  daily briefing on my tracked ASINs and competitors, emerging-brand or
  stockout alerts on my watchlist. Establishing monitoring and recurring
  runs always require the user's explicit opt-in — do not activate on
  vague update questions.
  Requires ZOODATA_API_KEY.
metadata:
  version: "1.0.9"
  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). This skill allows `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 — the bundled manifest `{skill_base_dir}/scripts/allowed-commands.json` enforces this: the CLI refuses out-of-scope subcommands with a structured `COMMAND_NOT_ALLOWED` error before any API request.
- **Local files**: baseline snapshots `{skill_base_dir}/data/last-run.json` and `{skill_base_dir}/data/watchlist.json`; a private temporary working dir (created with `mktemp -d`, removed when the fallback completes) 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.

## Shared CLI Contract

Before selecting or invoking the first command, read and apply the local `references/cli-contract.md`. Reapply it after every granular or composite result and before any fallback, additional call, state write, interpretation, or user-facing report. Use this skill's fallback logic only when the shared contract classifies the result as non-terminal.

### Local Interface Failure Output

For a terminal interface failure, respond in the user's language that today's radar could not be completed, then list succeeded and failed endpoint identifiers and state that the previous baseline remains unchanged. Do not emit RED/YELLOW/GREEN alerts or write `last-run.json`, watchlists, history, or baselines. Keep control tokens, parameters, and retry logs internal unless diagnostics are requested.

## 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.

Activation requires clear monitoring intent. Do not start a baseline run, update the watchlist, or enable scheduled/recurring execution from a vague or merely related request ("any updates?") — confirm explicitly with the user first; recurring monitoring always needs the user's explicit opt-in.

## 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=$(mktemp -d)` (private, 0700 — not a predictable path); remove it with `rm -rf "$WORK"` after `review-aggregate` succeeds or the fallback aborts
      - **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`), stop before any evidence call. Tell the user that a ZooData API key is required, link to https://zoodata.ai/en/api-keys, and explain that the key may be set in the environment or local config. Do not substitute public knowledge or a "for reference only" analysis.
## On 401 Invalid Key

When `_transport.status=401`, stop further calls, tell the user that the configured key was rejected, direct them to https://zoodata.ai/en/api-keys, and do not fabricate missing data.

## On 402 Credit Exhausted

When `_transport.status=402`, stop further calls. Report where the workflow stopped, any compatible partial findings already gathered, and returned credit metadata when present; direct the user to https://zoodata.ai/en/pricing and 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).