# Amazon Opportunity Discoverer

> Automated product opportunity scanner for Amazon sellers. Scans categories using 13 preset selection strategies, validates candidates with real-time data, brand analysis, and price structure, then ranks opportunities by composite score (1-100). Uses all 11 ZooData API endpoints. Use when user asks about: find products to sell, product opportunity, what should I sell, niche discovery, profitable products, selection strategy, product scanner, opportunity scan, winning products, untapped niches, product ideas, market gaps. Pick this to DISCOVER what to sell when the user has no specific target yet (ranked candidate list). To evaluate a niche they already named, use amazon-market-entry-analyzer; to track category trends over time, use amazon-market-trend-scanner. Requires ZOODATA_API_KEY.

- Skill: `serendipityoneinc/amazon-opportunity-discoverer` (Agent Skill, multi-file: 7 files)
- Install (CLI): `npx skillmds@latest add serendipityoneinc/amazon-opportunity-discoverer`
- Raw SKILL.md: https://api.skillmd.com/api/skills/serendipityoneinc/amazon-opportunity-discoverer/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Product & Planning
- Author: SerendipityOneInc (https://skillmd.com/u/serendipityoneinc)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/serendipityoneinc/amazon-opportunity-discoverer

---


# 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)

## 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 `opportunity-scan`, `categories`, `market`, `products`, `product`, `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**: 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 `opportunity-scan` command executes ~15+ API calls across up to 9 loops (~25-30 credits observed) 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 the opportunity scan could not be completed, followed by the succeeded and failed endpoint identifiers. Do not rank candidates, assign opportunity scores/tiers, or recommend samples or launches. Keep control tokens, parameters, and retry logs internal unless diagnostics are requested.

## 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 (CRITICAL)
- 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
- **`mode`/`--sales-min`/`--ratings-max` are CLI-local, expanded client-side** — NOT API fields. A raw request must use expanded API filters, must not send `mode`/`salesMin`/`ratingsMax`, and must distinguish `ratingMax` from `ratingCountMax`; otherwise the API returns 422.
- 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 '<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 (opportunity scoring, mode-based selection, ranked candidate list) remain valid — do not re-run them.
- Deduplicate ASINs across modes — same product appears in multiple scans
- Each mode has **built-in filters that STACK** with user filters (e.g. high-demand-low-barrier: sales≥300, reviews≤50)

## 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.

## Unique Logic

### Profile → Strategy Mapping
| Profile | Primary Modes | Price | Max Reviews |
|---------|--------------|-------|-------------|
| Beginner + Conservative | high-demand-low-barrier, long-tail, fbm-friendly | $15-60 | <50 |
| Beginner + Moderate | high-demand-low-barrier, emerging, low-price | $10-50 | <100 |
| Intermediate + Moderate | fast-movers, underserved, single-variant | $15-80 | <200 |
| Intermediate + Aggressive | high-demand-low-barrier, speculative | $10-100 | <500 |
| Advanced + Aggressive | fast-movers, speculative, top-bsr | any | any |

### User Criteria → Filter Params
Always translate: "300+ monthly sales" → `--sales-min 300`, "reviews <100" → `--ratings-max 100`, "$15-35" → `--price-min 15 --price-max 35`. If user has specific criteria, use custom filters (Approach B/C), NOT default modes. (`--sales-min`/`--ratings-max`/`--modes` are CLI-local — see API Pitfalls before any raw call.)

### Data-Driven Category Selection (no specific category given)
Scan with `market --keyword "{broad}" --topn 10`, rank subcategories by: newSkuRate>10%, topBrandSalesRate<60%, fbaRate>50%, 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 | <100 |
| Competition Gap | 20% | reviews<200, CR10<40% | 200-1K, 40-60% | >1K, >60% |
| Price Opportunity | 15% | in best opp band, opp>1.0 | 0.5-1.0 | <0.5 |
| Trend Momentum | 15% | BSR rising | stable | declining |
| Profit Margin | 15% | >30% | 15-30% | <15% |
| Differentiation | 10% | clear pain points | some gaps | none |
| Profile Fit | 5% | matches user profile | partial | mismatch |

### Tiers
| Score | Tier | Label |
|-------|------|-------|
| 80-100 | S | 🔥 Hot — act fast |
| 60-79 | A | ✅ Strong — worth pursuing |
| 40-59 | B | ⚠️ Moderate — needs differentiation |
| 0-39 | C | ❌ Weak — skip |

**Quick-Scan Mode** (~10 credits): 2 modes × 1 page, skip realtime/trend. Label as "directional only." **Implementation: run per-mode `products --mode <m> --page-size 20` calls — do NOT use the `opportunity-scan` composite for Quick-Scan** (it always executes the full 6-step pipeline including realtime×10 + trend + reviews, ~25-30 credits, and has no skip flags).

## Composite Command
```bash
python3 {skill_base_dir}/scripts/zoodata.py opportunity-scan --keyword "{kw}" --category "{path}" --modes "high-demand-low-barrier,emerging,underserved"
```
Or with custom filters: `--sales-min 300 --ratings-max 100 --price-min 15 --price-max 35`

## Output
Respond in user's language.

Sections: Scan Summary → Top 10 Opportunities Table → Detailed Analysis (Top 3) → Category Heatmap → Risk Alerts → Next Steps (S: buy sample, A: deep-dive, B: watch) → Data Provenance → API Usage

If user provides COGS, calculate profit. User criteria override: ANY fail → CAUTION/AVOID.

### 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.

### 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