
Linkfox Sellersprite Market Research
- 244 installs
- 64 repo stars
- Updated August 3, 2026
- linkfox-ai/linkfox-skills
Run SellerSprite Amazon market research to size niches, review competition density, and estimate revenue potential before sourcing or launching a private-label SKU.
About
Invokes SellerSprite to research Amazon categories and niches with sales estimates, competitor counts, price bands, and trend context. Helps agents compare opportunities, avoid oversaturated keywords, and document validate-stage business cases for FBA or merchant-fulfilled launches.
- Amazon niche demand metrics
- Competition concentration
- Revenue and BSR proxies
- Category tree navigation
- Agent-driven market scans
Linkfox Sellersprite Market Research by the numbers
- 244 all-time installs (skills.sh)
- +38 installs in the week ending Aug 2, 2026 (Skillselion tracking)
- Ranked #908 of 1,879 Marketing & SEO skills by installs in the Skillselion catalog
- Data as of Aug 4, 2026 (Skillselion catalog sync)
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| Installs | 244 |
|---|---|
| repo stars | ★ 64 |
| Last updated | August 3, 2026 |
| Repository | linkfox-ai/linkfox-skills ↗ |
What it does
Run SellerSprite Amazon market research to size niches, review competition density, and estimate revenue potential before sourcing or launching a private-label SKU.
Files
SellerSprite Market Research
This skill helps screen and rank Amazon category markets using SellerSprite market-research data.
Core Concepts
- 类目市场级分析:不是商品级列表,而是按类目/节点聚合后的市场画像。
- 市场规模:月均销量、月均销售额、商品数量等。
- 竞争结构:卖家/品牌集中度、头部集中度、自营占比、FBA/FBM 占比。
- 入参刻度:筛选用的 GoodsCrn / BrandCrn / SellerCrn / EbcProportion / FbaProportion / FbmProportion / AmazonSelfProportion(
min*/max*)须为 0~1 小数,见下文参数表与references/api.md。 - 新品机会:新品数量、新品占比、新品均价/评分/销量等。
API Usage
- Endpoint:
POST https://tool-gateway.linkfox.com/sellersprite/market/research - Auth: Header
Authorization: <api_key>(LINKFOXAGENT_API_KEY) - 完整说明见
references/api.md:含marketplace/month/orderField枚举,sellerLocation/newProduct/topNum,以及集中度、新品、头部、重量体积等全部筛选入参;响应含顶层字段与data[]类目市场指标、top10Images[]等。 - Runnable script:
scripts/sellersprite_market_research.py
Key Parameters
接口筛选项与工具 _sellersprite_market_research 一致(70+);下表为常用子集,完整参数与出参字段见 `references/api.md`。| 参数 | 类型 | 必填 | 说明 |
|---|---|---|---|
| marketplace | string | 是 | 站点编码,默认 US |
| month | string | 否 | nearly 或 yyyyMM |
| nodeIdPath | string | 否 | 类目节点路径 |
| departmentKeyword | string | 否 | 类目关键字路径 |
| page / size | integer | 否 | 分页,默认 1/50,size 最大 200 |
| orderField / orderDesc | string/boolean | 否 | 排序字段与方向;orderDesc 默认 true(降序) |
| minAvgRevenue / maxAvgRevenue | number | 否 | 月均销售额范围 |
| minAvgUnits / maxAvgUnits | integer | 否 | 月均销量范围 |
| minGoodsCount / maxGoodsCount | integer | 否 | 商品数量范围 |
| minGoodsCrn / maxGoodsCrn | number | 否 | 商品集中度(小数 0~1,如 0.4 表示 40%,勿用整数 40) |
| minSellerCrn / maxSellerCrn | number | 否 | 卖家集中度(小数 0~1) |
| minBrandCrn / maxBrandCrn | number | 否 | 品牌集中度(小数 0~1) |
| minAmazonSelfProportion / maxAmazonSelfProportion | number | 否 | Amazon 自营占比(小数 0~1) |
| minFbaProportion / maxFbaProportion | number | 否 | FBA 占比(小数 0~1) |
| minFbmProportion / maxFbmProportion | number | 否 | FBM 占比(小数 0~1) |
| minEbcProportion / maxEbcProportion | number | 否 | A+ 数量占比(小数 0~1) |
| minNewProportion / maxNewProportion | number | 否 | 新品占比(刻度可能与上列不同,以 references/api.md / schema 为准) |
| minAvgPrice / maxAvgPrice | number | 否 | 平均价格范围 |
| minAvgRating / maxAvgRating | number | 否 | 平均评分范围 |
| minAvgProfit / maxAvgProfit | number | 否 | 平均毛利率(%) |
Usage Example
{
"marketplace": "US",
"month": "nearly",
"minAvgRevenue": 10000,
"maxGoodsCrn": 0.4,
"minNewProportion": 10,
"maxSellerCrn": 0.5,
"orderField": "total_amount",
"orderDesc": true,
"page": 1,
"size": 50
}Display Rules
1. 先给出市场候选 Top N,再展示核心指标(市场规模、集中度、新品占比)。 2. 入参回显:GoodsCrn / BrandCrn / SellerCrn / EbcProportion / FbaProportion / FbmProportion / AmazonSelfProportion 对应筛选为 0~1 小数;向用户说明时可换算为百分数(如传 0.4 可表述为「商品集中度上限 40%」)。响应 data[] 里若仍带「(%)」字段,与入参刻度可能不同,以返回为准。 3. 其它比例/毛利率等字段的单位以 references/api.md 为准。 4. 显示筛选条件回显,便于用户复现。 5. 若结果过少或过多,建议用户调整关键阈值(如集中度、规模阈值)。
Important Limitations
- 必填参数:
marketplace - 每页最多 200 条
- 历史月份范围受第三方限制(通常近24个月)
Feedback:
Auto-detect and report feedback via the Feedback API when any of the following apply: 1. The functionality or purpose described in this skill does not match actual behavior 2. The skill's results do not match the user's intent 3. The user expresses dissatisfaction or praise about this skill 4. Anything you believe could be improved
Call the feedback API as specified in references/api.md. Do not interrupt the user's flow.
<!-- LF_LARGE_RESPONSE_BLOCK -->
Handling Large Responses
To avoid overflowing the agent context, persist the response to disk and extract only the fields you need:
python scripts/response_io.py run --script scripts/sellersprite_market_research.py --out-dir <DIR> '<params>'
python scripts/response_io.py read <file> --fields "<paths>" # or --path "<JMESPath>"Pick--out-diroutside any git working tree (e.g./tmp/...on Unix,%TEMP%/...on Windows). Persisted responses may contain PII, pricing, or auth-sensitive data — do not commit them. Files are not auto-deleted; clean up when the task is done.
run writes the full response to a file and emits only a schema preview + file path. read projects specific fields, with --limit/--offset for slicing and --format json|jsonl|csv|table for output.
When to prefer this pattern — apply your judgment based on the response characteristics, e.g.:
- High field count per record, or fields you don't need
- Batch/paginated results (multiple items per call)
- Long-text fields (descriptions, reviews, HTML, time series)
- Output reused across later steps rather than consumed immediately
For small, single-use responses, calling the main script directly is fine.
⚠️ The preview is a truncated schema + sample, not the full data. Any field-level decision must read from the persisted file via read. <!-- /LF_LARGE_RESPONSE_BLOCK -->
卖家精灵-选市场列表 API 参考
本文档与工具 _sellersprite_market_research 的 inputSchema / outputSchema(见 temp/tools20260430.txt)对齐。
调用规范
- 请求地址:
https://tool-gateway.linkfox.com/sellersprite/market/research - 请求方式:POST,
Content-Type: application/json - 认证方式:Header
Authorization: <api_key>,从环境变量LINKFOXAGENT_API_KEY读取
请求参数
必填:仅 marketplace。
说明:带「毛利率」等且 schema 写明「输入 N 表示 N%」的数值参数,取值范围一般为 0–100。例外:下列 GoodsCrn / BrandCrn / SellerCrn / EbcProportion / FbaProportion / FbmProportion / AmazonSelfProportion 的 min* / max* 入参须传 小数,见 集中度与结构占比。
类目、地域与头部样本
| 参数 | 类型 | 必填 | 约束 | 说明 |
|---|---|---|---|---|
| marketplace | string | 是 | maxLength 1000,默认 US | 站点编码,见 marketplace |
| nodeIdPath | string | 否 | maxLength 1000 | 类目节点 ID 路径,如 172282:281407 |
| departmentKeyword | string | 否 | maxLength 1000 | 类目关键字路径,如 Electronics:Accessories & Supplies |
| sellerLocation | string | 否 | maxLength 1000 | 卖家所属地,多个英文逗号分隔;取值见卖家精灵表 1.3 |
| newProduct | integer | 否 | 默认 3 | 新品定义(月) |
| topNum | integer | 否 | 默认 10 | 头部 Listing 数量 |
时间与分页、排序
| 参数 | 类型 | 必填 | 约束 | 说明 |
|---|---|---|---|---|
| month | string | 否 | 见 month | 筛选日期:nearly 或 yyyyMM |
| page | integer | 否 | 默认 1 | 页码,从 1 开始 |
| size | integer | 否 | 默认 50,最小 1,最大 200 | 每页条数 |
| orderField | string | 否 | maxLength 1000 | 排序字段,见 orderField |
| orderDesc | boolean | 否 | 默认 true | true 降序,false 升序 |
市场规模与主体数量
| 参数 | 类型 | 说明 |
|---|---|---|
| minAvgRevenue / maxAvgRevenue | number | 最低 / 最高月均销售额 |
| minAvgUnits / maxAvgUnits | integer | 最低 / 最高月均销量 |
| minGoodsCount / maxGoodsCount | integer | 最低 / 最高商品数量 |
| minSellers / maxSellers | integer | 最小 / 最大卖家数量 |
| minBrands / maxBrands | integer | 最小 / 最大品牌数量 |
| minAvgSellers / maxAvgSellers | number | 最小 / 最大平均卖家数量 |
集中度与结构占比
以下 7 组筛选入参(对应卖家精灵字段 GoodsCrn、BrandCrn、SellerCrn、EbcProportion、FbaProportion、FbmProportion、AmazonSelfProportion)须传 小数,约定为 0~1 之间的比例(例如 `0.35` 表示 35%)。不要按整数百分数传 0~100(例如勿用 40 表示 40%,除非已与实网行为核对)。
| 参数 | 类型 | 说明 |
|---|---|---|
| minGoodsCrn / maxGoodsCrn | number | 最小 / 最大商品集中度(小数 0~1) |
| minSellerCrn / maxSellerCrn | number | 最小 / 最大卖家集中度(小数 0~1) |
| minBrandCrn / maxBrandCrn | number | 最小 / 最大品牌集中度(小数 0~1) |
| minAmazonSelfProportion / maxAmazonSelfProportion | number | 最小 / 最大 Amazon 自营占比(小数 0~1) |
| minFbaProportion / maxFbaProportion | number | 最小 / 最大 FBA 占比(小数 0~1) |
| minFbmProportion / maxFbmProportion | number | 最小 / 最大 FBM 占比(小数 0~1) |
| minEbcProportion / maxEbcProportion | number | 最小 / 最大 A+ 数量占比(小数 0~1) |
新品数量占比(入参刻度以 schema 为准)
| 参数 | 类型 | 说明 |
|---|---|---|
| minNewProportion / maxNewProportion | number | 最小 / 最大新品数量占比(与其它占比字段刻度可能不同,以工具 schema / 实网为准) |
价格、评分、毛利、BSR(市场平均)
| 参数 | 类型 | 说明 |
|---|---|---|
| minAvgPrice / maxAvgPrice | number | 最低 / 最高平均价格 |
| minAvgRating / maxAvgRating | number | 最低 / 最高平均评分值 |
| minAvgRatings / maxAvgRatings | integer | 最低 / 最高平均评分数 |
| minAvgProfit / maxAvgProfit | number | 最低 / 最高平均毛利率(输入 N 表示 N%,0–100) |
| minAvgBsr / maxAvgBsr | integer | 最低 / 最高平均 BSR 排名 |
新品维度
| 参数 | 类型 | 说明 |
|---|---|---|
| minNewCount / maxNewCount | integer | 最小 / 最大新品数量 |
| minNewAvgPrice / maxNewAvgPrice | number | 最小 / 最大新品平均价格 |
| minNewAvgRating / maxNewAvgRating | number | 最小 / 最大新品平均星级 |
| minNewAvgRatings / maxNewAvgRatings | integer | 最小 / 最大新品平均评分数 |
| minNewAvgUnits / maxNewAvgUnits | number | 最低 / 最高新品月均销量 |
| minNewAvgRevenue / maxNewAvgRevenue | number | 最低 / 最高新品月均销售额 |
头部 Listing 指标
| 参数 | 类型 | 说明 |
|---|---|---|
| minTopAvgUnits / maxTopAvgUnits | integer | 最低 / 最高头部月均销量 |
| minTopAvgRevenue / maxTopAvgRevenue | number | 最低 / 最高头部月均销售额 |
| minTopAvgBsr / maxTopAvgBsr | integer | 最低 / 最高头部平均 BSR |
重量与体积
| 参数 | 类型 | 说明 |
|---|---|---|
| minWeight / maxWeight | number | 最低 / 最高重量 |
| minVolume / maxVolume | number | 最低 / 最高体积 |
marketplace 可选值
| 取值 | 含义 |
|---|---|
| US | 美国站 USD($) |
| JP | 日本站 JPY(¥) |
| UK | 英国站 GBP(£) |
| DE | 德国站 EUR(€) |
| FR | 法国站 EUR(€) |
| IT | 意大利站 EUR(€) |
| ES | 西班牙站 EUR(€) |
| CA | 加拿大站 C$($) |
| IN | 印度站 INR(₹) |
month
- 格式:正则
^(nearly|(19|20)\d{2}(0[1-9]|1[0-2]))$ - `nearly`:最近 30 天
- `yyyyMM`:具体月份(如
202507);最多支持当前月往前共 24 个月内的月份
orderField 可选值
与工具 schema「表 1.6」一致。
| 取值 | 含义 |
|---|---|
| total_units | 月销量 |
| total_amount | 月销售额 |
| bsr_rank | BSR 排名 |
| price | 价格 |
| rating | 评分 |
| reviews | 评分数 |
| profit | 毛利率 |
| reviews_rate | 留评率 |
| available_date | 上架时间 |
| questions | Q&A |
| total_units_growth | 月销量增长率 |
| total_amount_growth | 月销售额增长率 |
| reviews_increasement | 月新增评分数 |
| bsr_rank_cv | 近 7 天 BSR 增长数 |
| bsr_rank_cr | 近 7 天 BSR 增长率 |
| amz_unit | 子体销量 |
响应结构
顶层字段
| 字段 | 类型 | 说明 |
|---|---|---|
| total | integer | 总条数 |
| marketplace | string | 站点编码 |
| data | array | 类目市场列表(对应第三方 data.items) |
| columns | array | 渲染的列 |
| costToken | integer | 消耗 token |
| type | string | 渲染的样式 |
data[] 元素(单条类目市场)
| 字段 | 类型 | 说明 |
|---|---|---|
| nodeId | string | 节点 ID |
| nodeIdPath | string | 节点 ID 路径 |
| nodeLabelName | string | 节点名称 |
| nodeLabelPath | string | 节点名称路径 |
| nodeLabelLocale | string | 节点名称翻译 |
| nodeLabelPathLocale | string | 节点名称路径翻译 |
| marketplace | string | 市场标志 |
| currency | string | 该市场的货币类型 |
| ranking | integer | 排名 |
| totalProducts | integer | 商品总数 |
| topProducts | integer | 样本数量 |
| sellers | integer | 卖家数量 |
| brands | integer | 品牌数量 |
| avgSellers | number | 平均卖家数 |
| avgUnits | integer | 月均销量 |
| totalUnits | integer | 月总销量 |
| avgRevenue | number | 月均销售额 |
| totalRevenue | number | 月总销售额 |
| avgPrice | number | 平均价格 |
| avgRating | number | 平均评分值 |
| avgRatings | integer | 平均评分数 |
| avgBsr | integer | 平均 BSR |
| avgProfit | number | 平均利润率(%) |
| fbaProportion | number | FBA 占比(%) |
| fbmProportion | number | FBM 占比(%) |
| amazonSelfProportion | number | Amazon 自营占比(%) |
| ebcProportion | number | A+ 商品占比(%) |
| returnRatio | number | 退货率(%) |
| avgReturnRatio | number | 退货率类目平均值(%) |
| searchToPurchaseRatio | number | 搜索购买比(千分比) |
| sellerNation | string | 最多卖家归属地 code |
| sellerNationLabel | string | 最多卖家归属地 label |
| sellerProportion | number | 最多卖家归属地占比(%) |
| avgWeight | number | 平均重量(pound) |
| baseAvgWeight | number | 平均重量(g) |
| avgVolume | number | 平均体积(in³) |
| baseAvgVolume | number | 平均体积(cm³) |
| top10Images | array | 前 10 商品图片,元素见下表 |
top10Images[] 元素
| 字段 | 类型 | 说明 |
|---|---|---|
| image | string | 图片链接 |
| asin | string | ASIN |
curl 示例
curl -X POST https://tool-gateway.linkfox.com/sellersprite/market/research -H "Authorization: $LINKFOXAGENT_API_KEY" -H "Content-Type: application/json" -d '{
"marketplace": "US",
"month": "nearly",
"minAvgRevenue": 10000,
"maxGoodsCrn": 0.4,
"orderField": "total_amount",
"orderDesc": true,
"page": 1,
"size": 50
}'---
Feedback API
- POST
https://skill-api.linkfox.com/api/v1/public/feedback - Content-Type:
application/json
{
"skillName": "linkfox-sellersprite-market-research",
"sentiment": "POSITIVE",
"category": "OTHER",
"content": "Results were accurate, user was satisfied."
}#!/usr/bin/env python3
"""
Skill response I/O helper — wraps any main script to persist large API
responses to disk, then offers a `read` subcommand to extract specific fields
from those persisted files. Generic, business-agnostic.
This script is bundled into each skill's scripts/ directory by tools/response_io/sync.py.
The agent must pass --script <path> to identify which main script to execute.
Usage:
python scripts/response_io.py run --script <PATH> --out-dir <DIR> '<json_params>' [--label NAME] [--timeout SEC]
python scripts/response_io.py read <file> (--path "<JMESPath>" | --fields "f1,f2,...") [--limit N] [--offset M] [--format json|jsonl|csv|table]
"""
from __future__ import annotations
import sys
if sys.version_info < (3, 10):
sys.exit(
"Error: Python 3.10+ required (current: "
f"{sys.version_info.major}.{sys.version_info.minor}). "
"Please upgrade Python."
)
import argparse
import csv
import io
import json
import os
import re
import secrets
import subprocess
from datetime import datetime
from pathlib import Path
from typing import Any
# Force UTF-8 stdout/stderr so non-ASCII chars in previews and API responses
# print correctly on Windows (default cp936 / gbk).
for stream in (sys.stdout, sys.stderr):
try:
stream.reconfigure(encoding="utf-8") # type: ignore[attr-defined]
except (AttributeError, OSError):
pass
try:
import jmespath # type: ignore
HAS_JMESPATH = True
except ImportError:
HAS_JMESPATH = False
MAX_STRING_LEN = 120
MAX_DEPTH = 3
SAMPLE_KEY_CAP = 15
RAW_TEXT_PEEK = 500
DEFAULT_TIMEOUT_SEC = 300
# ---------------------------------------------------------------------------
# Shared helpers
# ---------------------------------------------------------------------------
def _err(msg: str, code: int = 1) -> None:
print(msg, file=sys.stderr)
sys.exit(code)
def _resolve_script(script_arg: str) -> Path:
p = Path(script_arg).expanduser()
if not p.is_absolute():
# Resolve relative to the current working directory the agent invoked from.
p = (Path.cwd() / p).resolve()
else:
p = p.resolve()
if not p.is_file():
_err(f"--script path not found: {p}")
return p
def _resolve_skill_name(main_script: Path) -> str:
"""Best-effort skill name extraction for filename prefixing.
main_script lives at <skill_dir>/scripts/<name>.py — return <skill_dir>'s
folder name. Fall back to the script's stem if structure differs.
"""
try:
if main_script.parent.name == "scripts":
return main_script.parents[1].name
except IndexError:
pass
return main_script.stem
def _sanitize_label(label: str) -> str:
"""Allow only safe filename chars in --label to prevent path traversal."""
cleaned = re.sub(r"[^\w\-]", "_", label)
return cleaned[:64] # cap length
def _truncate_string(s: str) -> str:
if len(s) <= MAX_STRING_LEN:
return s
return s[:MAX_STRING_LEN] + f"...(truncated, total {len(s)} chars)"
def _truncate_value(value: Any, depth: int = 0) -> Any:
"""Recursively truncate strings, deep nesting, and large arrays for preview."""
if depth >= MAX_DEPTH:
if isinstance(value, dict):
return f"<truncated nested object, keys: {list(value.keys())[:10]}>"
if isinstance(value, list):
return f"<truncated nested array, length: {len(value)}>"
if isinstance(value, str):
return _truncate_string(value)
return value
if isinstance(value, str):
return _truncate_string(value)
if isinstance(value, dict):
out = {k: _truncate_value(v, depth + 1) for k, v in value.items()}
return out
if isinstance(value, list):
if not value:
return []
truncated = [_truncate_value(value[0], depth + 1)]
if len(value) > 1:
# Note total length on the parent — keep the array type-homogeneous
# so downstream consumers can iterate without special-casing strings.
truncated.append({"_omitted_items": len(value) - 1})
return truncated
return value
def _shape_of(value: Any, top: bool = False) -> Any:
"""Lightweight schema description for the preview block."""
if isinstance(value, dict):
keys = list(value.keys())
out: dict[str, Any] = {"type": "object", "top_keys" if top else "keys": keys}
if top:
for k in keys[:8]:
out[k] = _shape_of(value[k])
return out
if isinstance(value, list):
out = {"type": "array", "length": len(value)}
if value and isinstance(value[0], dict):
out["item_keys"] = list(value[0].keys())
elif value:
out["item_type"] = type(value[0]).__name__
return out
return {"type": type(value).__name__}
def _build_sample(value: Any) -> Any:
"""First-record sample with explicit truncation marker."""
if isinstance(value, list):
if not value:
return {"_truncated_record": True, "_note": "array is empty"}
first = value[0]
if isinstance(first, dict):
sample = {"_truncated_record": True, "_note": f"first of {len(value)} items"}
sample.update(_truncate_value(first, depth=1))
return sample
return {"_truncated_record": True, "_note": f"first of {len(value)} items", "value": _truncate_value(first, depth=1)}
if isinstance(value, dict):
sample = {"_truncated_record": True, "_note": "top-level object (truncated)"}
sample.update(_truncate_value(value, depth=1))
return sample
return {"_truncated_record": True, "value": _truncate_value(value, depth=1)}
def _shrink_preview(preview: dict) -> dict:
"""Cap the sample's value fields when it has many keys.
`shape.*.item_keys` is the single source of truth for the full key list
(always complete, no truncation). The sample only ever shows up to
SAMPLE_KEY_CAP fields with their concrete values, since the agent only
needs a feel for value shapes — for the full menu of available fields,
they read `shape`.
"""
sample = preview.get("sample")
if isinstance(sample, dict):
meta_keys = {"_truncated_record", "_note"}
data_keys = [k for k in sample.keys() if k not in meta_keys]
if len(data_keys) > SAMPLE_KEY_CAP:
kept = data_keys[:SAMPLE_KEY_CAP]
new_sample = {k: v for k, v in sample.items() if k in meta_keys or k in kept}
base_note = sample.get("_note", "")
extra = (
f"showing first {SAMPLE_KEY_CAP} of {len(data_keys)} fields "
f"(see `shape` for the complete key list)"
)
new_sample["_note"] = f"{base_note}; {extra}" if base_note else extra
preview["sample"] = new_sample
return preview
# ---------------------------------------------------------------------------
# `run` subcommand
# ---------------------------------------------------------------------------
def cmd_run(args: argparse.Namespace) -> int:
main_script = _resolve_script(args.script)
skill_name = _resolve_skill_name(main_script)
out_dir = Path(args.out_dir).expanduser().resolve()
try:
out_dir.mkdir(parents=True, exist_ok=True)
except OSError as e:
_err(f"Failed to create --out-dir {out_dir}: {e}")
if not os.access(out_dir, os.W_OK):
_err(f"--out-dir is not writable: {out_dir}")
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
rand = secrets.token_hex(3)
safe_label = _sanitize_label(args.label) if args.label else ""
label_part = f"__{safe_label}" if safe_label else ""
out_file = out_dir / f"{skill_name}__{timestamp}_{rand}{label_part}.json"
# Force the child process to emit UTF-8 regardless of the host console
# encoding (Windows defaults to cp936 / gbk and would otherwise corrupt
# non-ASCII bytes when we read them back).
child_env = os.environ.copy()
child_env["PYTHONIOENCODING"] = "utf-8"
timed_out = False
try:
proc = subprocess.run(
[sys.executable, str(main_script), args.params],
capture_output=True,
text=True,
encoding="utf-8",
errors="replace",
env=child_env,
timeout=args.timeout,
)
stdout_text = proc.stdout or ""
stderr_text = proc.stderr or ""
returncode = proc.returncode
except subprocess.TimeoutExpired as e:
timed_out = True
stdout_text = (e.stdout.decode("utf-8", errors="replace") if isinstance(e.stdout, bytes) else (e.stdout or "")) or ""
stderr_text = (e.stderr.decode("utf-8", errors="replace") if isinstance(e.stderr, bytes) else (e.stderr or "")) or ""
returncode = 124 # convention for timeout
# Always write the captured stdout to disk, even if not JSON.
try:
out_file.write_text(stdout_text, encoding="utf-8")
except OSError as e:
_err(f"Failed to write output file {out_file}: {e}")
if stderr_text:
sys.stderr.write(stderr_text)
# Try to parse the captured stdout as JSON for the preview.
try:
parsed = json.loads(stdout_text) if stdout_text.strip() else None
format_kind = "json"
except json.JSONDecodeError:
parsed = None
format_kind = "raw_text"
preview: dict[str, Any] = {
"_preview": {
"is_preview": True,
"warning": (
"PREVIEW ONLY — NOT FULL DATA. The full response is saved to `file`. "
"Use `python scripts/response_io.py read <file> --fields '...'` to extract "
"specific fields, or `--path '<JMESPath>'` for complex projections."
),
},
}
# Surface failures prominently so agents don't mistake a stub preview for success.
if returncode != 0 or timed_out:
stderr_snippet = stderr_text[-500:] if stderr_text else ""
preview["_error"] = {
"exit_code": returncode,
"timed_out": timed_out,
"stderr_snippet": stderr_snippet,
"hint": "The wrapped script failed or timed out. The output file may be empty or partial.",
}
preview.update({
"file": str(out_file),
"size_bytes": out_file.stat().st_size,
"skill": skill_name,
"exit_code": returncode,
"format": format_kind,
"label": safe_label or None,
"next_steps_hint": (
"use: python scripts/response_io.py read <file> --fields '...' | --path '...'"
),
})
if format_kind == "json":
preview["shape"] = _shape_of(parsed, top=True)
preview["sample"] = _build_sample(parsed)
else:
peek = stdout_text[:RAW_TEXT_PEEK]
preview["raw_text_peek"] = peek
preview["raw_text_total_chars"] = len(stdout_text)
preview["sample"] = {
"_truncated_record": True,
"_note": f"stdout was not valid JSON; first {RAW_TEXT_PEEK} chars shown above in raw_text_peek",
}
preview = _shrink_preview(preview)
print(json.dumps(preview, ensure_ascii=False, indent=2))
return returncode
# ---------------------------------------------------------------------------
# `read` subcommand
# ---------------------------------------------------------------------------
def _load_json(path: Path) -> Any:
try:
text = path.read_text(encoding="utf-8")
except OSError as e:
_err(f"Failed to read file {path}: {e}")
try:
return json.loads(text)
except json.JSONDecodeError as e:
_err(f"File is not valid JSON: {path}\n{e}")
def _basic_dot_path(data: Any, path: str) -> Any:
"""Pure-stdlib dot-path resolver. No [*] support — callers fall back here only when jmespath is unavailable AND the path has no [*]."""
cur = data
for part in path.split("."):
if isinstance(cur, dict):
cur = cur.get(part)
else:
return None
return cur
def _resolve_field(data: Any, expr: str) -> Any:
if HAS_JMESPATH:
return jmespath.search(expr, data)
if "[" in expr or "*" in expr:
_err(
f"jmespath is required for expression '{expr}'. "
f"Install with: pip install jmespath"
)
return _basic_dot_path(data, expr)
def _project_fields(data: Any, fields: list[str]) -> Any:
"""Run each field expr; if any returns a list, zip them into list-of-dicts."""
resolved: dict[str, Any] = {f: _resolve_field(data, f) for f in fields}
list_lengths = [len(v) for v in resolved.values() if isinstance(v, list)]
if not list_lengths:
return resolved
# All list values must be same length to zip cleanly.
if len(set(list_lengths)) > 1:
# Fallback: return the dict as-is so caller can inspect mismatches.
return resolved
n = list_lengths[0]
rows = []
for i in range(n):
row = {}
for f, v in resolved.items():
row[f] = v[i] if isinstance(v, list) else v
rows.append(row)
return rows
def _apply_slice(value: Any, limit: int | None, offset: int | None) -> Any:
if not isinstance(value, list):
return value
start = offset or 0
end = (start + limit) if limit is not None else None
return value[start:end]
def _format_output(value: Any, fmt: str) -> str:
if fmt == "json":
return json.dumps(value, ensure_ascii=False, indent=2)
if fmt == "jsonl":
if isinstance(value, list):
return "\n".join(json.dumps(item, ensure_ascii=False) for item in value)
return json.dumps(value, ensure_ascii=False)
if fmt in ("csv", "table"):
if not isinstance(value, list) or not value:
_err(f"--format {fmt} requires a non-empty list result")
if not all(isinstance(item, dict) for item in value):
_err(f"--format {fmt} requires list-of-objects, got list of {type(value[0]).__name__}")
keys: list[str] = []
for item in value:
for k in item.keys():
if k not in keys:
keys.append(k)
if fmt == "csv":
buf = io.StringIO()
writer = csv.DictWriter(buf, fieldnames=keys, extrasaction="ignore")
writer.writeheader()
for item in value:
writer.writerow({k: _stringify(item.get(k)) for k in keys})
return buf.getvalue().rstrip("\n")
# table: simple aligned columns
rows = [[_stringify(item.get(k)) for k in keys] for item in value]
widths = [len(k) for k in keys]
for row in rows:
for i, cell in enumerate(row):
widths[i] = max(widths[i], len(cell))
lines = [
" ".join(k.ljust(widths[i]) for i, k in enumerate(keys)),
" ".join("-" * widths[i] for i in range(len(keys))),
]
for row in rows:
lines.append(" ".join(row[i].ljust(widths[i]) for i in range(len(keys))))
return "\n".join(lines)
_err(f"Unknown --format: {fmt}")
return "" # unreachable
def _stringify(v: Any) -> str:
if v is None:
return ""
if isinstance(v, (dict, list)):
return json.dumps(v, ensure_ascii=False)
return str(v)
def cmd_read(args: argparse.Namespace) -> int:
if not args.path and not args.fields:
_err("read: either --path or --fields is required")
if args.path and args.fields:
_err("read: --path and --fields are mutually exclusive")
file_path = Path(args.file).expanduser().resolve()
data = _load_json(file_path)
if args.path:
result = _resolve_field(data, args.path)
else:
fields = [f.strip() for f in args.fields.split(",") if f.strip()]
if not fields:
_err("--fields parsed to empty list")
result = _project_fields(data, fields)
result = _apply_slice(result, args.limit, args.offset)
print(_format_output(result, args.format))
return 0
# ---------------------------------------------------------------------------
# CLI
# ---------------------------------------------------------------------------
def main() -> int:
parser = argparse.ArgumentParser(
prog="response_io.py",
description="Persist large skill API responses to disk and read fields on demand.",
)
sub = parser.add_subparsers(dest="cmd", required=True)
p_run = sub.add_parser(
"run",
help="Execute a main script and persist its stdout to a file; "
"print only a lightweight preview to stdout.",
)
p_run.add_argument("params", help="JSON params string passed verbatim to the main script (argv[1]).")
p_run.add_argument("--script", required=True, help="Path to the main script to execute, e.g. scripts/my_api.py")
p_run.add_argument("--out-dir", required=True, help="Directory to write the response file into (created if missing).")
p_run.add_argument("--label", default=None, help="Optional filename suffix; sanitized to safe filename characters.")
p_run.add_argument("--timeout", type=int, default=DEFAULT_TIMEOUT_SEC, help=f"Subprocess timeout in seconds (default: {DEFAULT_TIMEOUT_SEC}).")
p_run.set_defaults(func=cmd_run)
p_read = sub.add_parser(
"read",
help="Extract specific fields from a previously persisted response file.",
)
p_read.add_argument("file", help="Path to the persisted JSON response file.")
g = p_read.add_mutually_exclusive_group()
g.add_argument("--path", default=None, help="JMESPath expression, e.g. 'data[*].{asin: asin, title: title}'.")
g.add_argument("--fields", default=None, help="Comma-separated field paths, e.g. 'data[*].asin,data[*].title'.")
p_read.add_argument("--limit", type=int, default=None, help="Take at most N items (when result is a list).")
p_read.add_argument("--offset", type=int, default=None, help="Skip the first M items (when result is a list).")
p_read.add_argument("--format", choices=["json", "jsonl", "csv", "table"], default="json", help="Output format (default: json).")
p_read.set_defaults(func=cmd_read)
args = parser.parse_args()
return args.func(args)
if __name__ == "__main__":
sys.exit(main())
#!/usr/bin/env python3
"""
SellerSprite Market Research - LinkFox Skill
Calls sellersprite/market/research to query category-level market opportunities.
入参提示:`min/max` 对应的 **GoodsCrn、BrandCrn、SellerCrn、EbcProportion、FbaProportion、FbmProportion、AmazonSelfProportion** 须传 **0~1 小数**(如 `0.4`),勿用整数百分数;详见 `references/api.md`。
Usage:
python sellersprite_market_research.py '{"marketplace": "US", "page": 1, "size": 50}'
"""
import json
import os
import sys
from urllib.request import Request, urlopen
from urllib.error import HTTPError, URLError
API_URL = "https://tool-gateway.linkfox.com/sellersprite/market/research"
def get_api_key() -> str:
key = os.environ.get("LINKFOXAGENT_API_KEY")
if not key:
print(
"API Key not configured. Please complete authorization first:\n"
"1. Visit https://skill.linkfox.com/linkfoxskills/guide.htm to obtain your Key\n"
"2. Set environment variable: export LINKFOXAGENT_API_KEY=your-key-here",
file=sys.stderr,
)
sys.exit(1)
return key
def call_api(params: dict) -> dict:
api_key = get_api_key()
data = json.dumps(params).encode("utf-8")
req = Request(
API_URL,
data=data,
headers={
"Authorization": api_key,
"Content-Type": "application/json",
"User-Agent": "LinkFox-Skill/1.0",
},
method="POST",
)
try:
with urlopen(req, timeout=60) as response:
return json.loads(response.read().decode("utf-8"))
except HTTPError as e:
body = e.read().decode("utf-8") if e.fp else ""
return {"error": f"HTTP {e.code}: {e.reason}", "details": body}
except URLError as e:
return {"error": f"Connection failed: {e.reason}"}
def main() -> None:
if len(sys.argv) < 2:
print("Usage: sellersprite_market_research.py '<JSON parameters>'", file=sys.stderr)
print(
"Example: sellersprite_market_research.py "
"'{\"marketplace\": \"US\", \"minAvgRevenue\": 5000, \"page\": 1, \"size\": 50}'",
file=sys.stderr,
)
sys.exit(1)
try:
params = json.loads(sys.argv[1])
except json.JSONDecodeError as e:
print(f"Invalid parameter format: {e}", file=sys.stderr)
sys.exit(1)
result = call_api(params)
print(json.dumps(result, indent=2, ensure_ascii=False))
if __name__ == "__main__":
main()