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name: amazon-listing-audit-pro description: > Comprehensive listing health check and optimization engine for Amazon sellers. Scores listings across 8 dimensions, benchmarks against category leaders, identifies keyword gaps, and generates data-backed improvement recommendations. Supports single ASIN or bulk audit (10-100+ ASINs for agencies). Uses all 11 ZooData API endpoints with cross-validation. Use when user asks about: listing audit, listing optimization, listing score, listing quality, improve my listing, listing review, listing diagnosis, title optimization, bullet point optimization, keyword gaps, listing benchmark, A+ content, listing health check, listing comparison. Requires ZOODATA_API_KEY. metadata: version: "1.0.6" author: SerendipityOneInc homepage: https://github.com/SerendipityOneInc/ZooData-Skills openclaw: {"requires": {"env": ["ZOODATA_API_KEY"]}, "primaryEnv": "ZOODATA_API_KEY"}

ZooData — Amazon Listing Audit Pro

8-dimension health check. Benchmark against leaders. Fix what matters most. 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

Credential

Required: ZOODATA_API_KEY. Get free key at zoodata.ai/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: listing-audit, product, products, market, 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: 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 listing-audit command executes ~18 API calls (~15-20 credits) in ONE invocation and has NO skip/trim flags — under a credit cap, use the granular commands instead.

Input

Required: my_asin. Optional: keyword, category. Category is auto-detected from ASIN via realtime/product if not provided. If category_source is inferred_from_search, confirm with user before proceeding.

API Pitfalls (CRITICAL)

1. Category auto-detection: categoryPath is auto-detected from ASIN. 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, opportunity=sampleOpportunityIndex
4. reviews/analysis: needs 50+ reviews; ASIN mode first, category fallback. Fallback chain when both fail:
   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 (8-dimension audit scores, title/bullet/A+ checks, category-leader benchmarks) remain valid — do not re-run them.
5. Sales null fallback: Monthly sales ≈ 300,000 / BSR^0.65, tag 🔍

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. listing-audit --my-asin X [--keyword Y] [--category Z] (composite, auto-detects category from ASIN)
2. Score 8 dimensions → generate report with improvements

8 Scoring Dimensions

Dimension	Weight	90-100	60-89	30-59	0-29
Title	15%	150+ chars, top 3 KW, brand first	100-150, 2 KW	<100 or stuffed	Missing key terms
Bullets	15%	5+, benefit-led, KW each	5, features only	3-4, generic	<3 bullets
Images	15%	7+, infographic+lifestyle	5-6, decent	3-4, basic	1-2 images
A+ Content	10%	Rich A+, comparison, brand story	Basic A+	No A+ w/ description	Nothing
Reviews	15%	1000+, 4.5+, <5% 1-star	200-1K, 4.0-4.5	50-200, 3.5-4.0	<50 or <3.5
Keywords	10%	Top 5 competitor KW covered	3-4 covered	1-2 covered	None matched
Category Fit	10%	Optimal category, top 1% BSR	Top 5%	Suboptimal	Wrong category
Pricing	10%	In opportunity band, margin >25%	Hottest band	Outside top bands	Overpriced/<10% margin

Score each 0-100, calculate weighted total. Include "Basis" column explaining each score.

Output Spec

Sections: Overall Score (X/100, A-F grade) → 8-Dimension Scorecard → Title Audit (analysis + suggested rewrite) → Bullets Audit (vs leaders, missing points, rewrites) → Image Audit → Review Health → Keyword Gap Analysis (vs Top 5 leader titles/bullets) → vs Category Leaders (side-by-side Top 3) → Priority Fix List (lowest scores first) → Data Provenance → API Usage.

Suggested rewrites should incorporate high-frequency positive review language.

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 📊. 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.

Bulk audit: share market data across ASINs, run audit per ASIN.

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: ~20-25 credits

Audit target(1) + Categories/Products/Competitors(3) + Realtime×5(5) + Market/Brand(3) + Price(2) + Reviews(2) + History(1) + Buffer(3-8).