# Amazon Opportunity Discoverer

> >

- **Type:** Skill
- **Install:** `agentstack add skill-serendipityoneinc-zoodata-skills-amazon-opportunity-discoverer`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [SerendipityOneInc](https://agentstack.voostack.com/s/serendipityoneinc)
- **Installs:** 0
- **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [SerendipityOneInc](https://github.com/SerendipityOneInc)
- **Source:** https://github.com/SerendipityOneInc/ZooData-Skills/tree/main/amazon-opportunity-discoverer
- **Website:** https://apiclaw.io/

## Install

```sh
agentstack add skill-serendipityoneinc-zoodata-skills-amazon-opportunity-discoverer
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# Amazon Opportunity Discoverer — Niche Scanner & Scoring

Tell me your budget and experience. I find opportunities, score them, and rank.

## Files
- **Script**: `{skill_base_dir}/scripts/zoodata.py` — run `--help` for params
- **Reference**: `{skill_base_dir}/references/reference.md` (field names & response structure)

## Credential
Required: `ZOODATA_API_KEY`. Get free key at [zoodata.ai/api-keys](https://zoodata.ai/en/api-keys)

## Input
- **Required**: keyword or category + budget (Low/Med/High) + experience (Beginner/Intermediate/Advanced)
- **Recommended**: risk tolerance (Conservative/Moderate/Aggressive)
- **Optional**: fulfillment preference (FBA/FBM), specific filter criteria

## API Pitfalls (see zoodata skill for full list)
- categoryPath is auto-resolved via `categories`, with fallback to top search result. If `category_source` is `inferred_from_search`, confirm with user — keyword-only queries contaminate results
- All keyword-based endpoints MUST include `--category` when locked
- Revenue = `sampleAvgMonthlyRevenue` directly. Sales = `monthlySalesFloor` (lower bound)
- `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 ''`
        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__$(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 (reviewCount10%, topBrandSalesRate50%, avgPrice $10-50, avgMonthlySales>200. Pick top 3-5.

### Opportunity Score (per candidate, 1-100)
| Dimension | Weight | Good | Medium | Warning |
|-----------|--------|------|--------|---------|
| Demand Signal | 20% | sales>300, rev>$5K | 100-300 | 1K, >60% |
| Price Opportunity | 15% | in best opp band, opp>1.0 | 0.5-1.0 | 30% | 15-30% |  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: ~50-60 credits

## Source & license

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

- **Author:** [SerendipityOneInc](https://github.com/SerendipityOneInc)
- **Source:** [SerendipityOneInc/ZooData-Skills](https://github.com/SerendipityOneInc/ZooData-Skills)
- **License:** MIT
- **Homepage:** https://apiclaw.io/

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

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-serendipityoneinc-zoodata-skills-amazon-opportunity-discoverer
- Seller: https://agentstack.voostack.com/s/serendipityoneinc
- Browse the marketplace: https://agentstack.voostack.com/browse

---
Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.