数据驱动的亚马逊卖家定价策略引擎。请提供ASIN——自动识别叶子类目,分析定价格局,给出提价/降价建议。
Give me your ASIN(s). I'll tell you whether to raise, hold, or lower — with data.
{skill_base_dir}/scripts/zoodata.py — run --help for params{skill_base_dir}/references/reference.md (field names & response structure)Required: ZOODATA_API_KEY. Get free key at zoodata.ai/api-keys
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.{skill_base_dir}/scripts/zoodata.py (Python 3, stdlib-only). This skill allows categories, product, products, competitors, market, price-band-overview, price-band-detail, brand-overview, brand-detail, history, analyze, and check. 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.~/.zoodata/config.json.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.
For a terminal interface failure, respond in the user's language that the pricing assessment could not be completed, followed by the succeeded and failed endpoint identifiers. Do not issue RAISE/HOLD/LOWER, a recommended price, or a profit simulation. Keep control tokens, parameters, and retry logs internal unless diagnostics are requested.
On first interaction, tell user: "Give me your ASIN(s). I support single or batch analysis — I'll auto-detect each product's category and analyze the pricing landscape for you."
product --asin {asin} → extract bestsellersRank arraybestsellersRank = leaf (most specific) categorycategories --keyword "{leaf_category_name}" → get categoryPathsampleAvgMonthlyRevenue directly. NEVER calculate price×sales.monthlySalesFloor (lower bound)buyboxWinner.price, NOT top-level price--category once categoryPath is lockedWhen 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.
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.
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.
SignalConditionRAISEPrice below opportunity band AND rating ≥ category avg AND BSR stable/risingHOLDPrice in optimal band AND BSR stable AND no competitor price warLOWERPrice above hottest band AND BSR declining OR competitor undercut detected
Don't pick highest-sales band. Calculate per band: Sales/Competition Ratio = Avg Monthly Sales ÷ Avg Review Count Highest ratio = best entry point (strong demand + low review barriers).
3 scenarios: Conservative (current price), Moderate (±$1-2), Aggressive (±$3-5). Per scenario: Revenue = Price × Est. Sales − FBA Fee − Referral Fee (15%) − COGS = Net Profit & Margin.
Net MarginSignalInterpretation>30%🟢 HealthyStrong margin, room for ad spend and promotions 📊15-30%🟡 AcceptableViable but monitor costs closely 🔍5-15%🟠 ThinOne price war or cost increase away from loss 🔍<5%🔴 UnsustainableMust raise price, cut costs, or exit 💡
Respond in user's language.
Per ASIN: Price Signal (RAISE/HOLD/LOWER) → Current Position in Category → Price Band Heatmap (with Sales/Competition Ratio) → Competitor Price Map (top 10 in leaf category) → 30-Day Trend → Profit Simulation (3 scenarios) → BuyBox Analysis → Recommended Price.
Batch summary (if multiple ASINs): Overview table (ASIN | Product | Category | Current Price | Signal | Recommended) → Per-ASIN detail.
End with: Data Provenance → API Usage. Flag DB vs Realtime discrepancies as likely promotions.
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.
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.
Rules: Strategy recommendations and price signals (RAISE/HOLD/LOWER) are NEVER 📊. User criteria override AI judgment.
Include a table at the end of every report:
DataEndpointKey ParamsNotes(e.g. Market Overview)markets/searchcategoryPath, 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.
EndpointCallsCredits(each endpoint used)NNTotalNN
Extract from meta.creditsConsumed per response. End with Credits remaining: N.