
Linkfox Jiimore Product Discovery
- 233 installs
- 64 repo stars
- Updated August 3, 2026
- linkfox-ai/linkfox-skills
Helps with ai & agent building tasks.
About
linkfox-jiimore-product-discovery is a Claude Code skill in the AI & Agent Building category.
- linkfox-jiimore-product-discovery
- AI & Agent Building
- AI-coding skill
Linkfox Jiimore Product Discovery by the numbers
- 233 all-time installs (skills.sh)
- +35 installs in the week ending Aug 2, 2026 (Skillselion tracking)
- Ranked #2,640 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
- Data as of Aug 4, 2026 (Skillselion catalog sync)
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| Installs | 233 |
|---|---|
| repo stars | ★ 64 |
| Last updated | August 3, 2026 |
| Repository | linkfox-ai/linkfox-skills ↗ |
What it does
Helps with ai & agent building tasks.
Files
Jiimore Product Discovery
This skill guides you on how to discover and mine high-potential Amazon products using the Jiimore product discovery engine, helping Amazon sellers find potential bestsellers through keyword-based filtering with conversion, click growth, and profitability indicators.
Core Concepts
Jiimore Product Discovery is a keyword-driven Amazon product mining tool. Given a search keyword, it returns a list of products matching specified performance criteria such as conversion rate, click growth rate, gross profit margin, pricing, reviews, and listing age. This makes it ideal for identifying emerging opportunities, validating product ideas, and competitive benchmarking.
Keyword is required: Every query must include a keyword. The keyword should be translated into the language of the target marketplace (e.g., Japanese for JP, German for DE).
Rate values are decimals: Conversion rates and growth rates are expressed as decimals between 0 and 1. For example, 0.1 means 10%, 0.25 means 25%. This is a common point of confusion when users specify percentages.
Marketplace support: Currently supports US (United States), JP (Japan), and DE (Germany). Default is US. Use US when the user doesn't specify a marketplace.
Parameter Guide
Required
| Parameter | Description | Example |
|---|---|---|
| keyword | Search keyword (must be translated to the target marketplace language) | wireless charger |
Filtering Parameters
| Parameter | Description | Value Format |
|---|---|---|
| priceMin / priceMax | Product price range | Number (e.g., 10.0, 50.0) |
| totalReviewsMin / totalReviewsMax | Review count range | Integer (e.g., 0, 500) |
| customerRatingMin / customerRatingMax | Customer rating range | Number (e.g., 4.0, 5.0) |
| clickConversionRateMin / clickConversionRateMax | Click-to-purchase conversion rate | Decimal 0-1 (0.1 = 10%) |
| clickConversionRateCompositeMin / clickConversionRateCompositeMax | Composite conversion rate | Decimal 0-1 (0.1 = 10%) |
| clickCountT7Min / clickCountT7Max | Weekly click count range | Integer |
| clickCountT30Min / clickCountT30Max | Monthly click count range | Integer |
| clickCountGrowthT7Min / clickCountGrowthT7Max | Weekly click growth rate | Decimal 0-1 (0.1 = 10%) |
| clickCountGrowthT30Min / clickCountGrowthT30Max | Monthly click growth rate | Decimal 0-1 (0.1 = 10%) |
| salesVolumeT360Min / salesVolumeT360Max | Annual sales volume range | Integer |
| grossProfitMarginMin / grossProfitMarginMax | Gross profit margin range | Number |
| fbaFeeMin / fbaFeeMax | FBA fee range | Number |
| launchDateMin / launchDateMax | Listing date range | String: yyyyMMdd000000 |
| nicheCountMin / nicheCountMax | Niche market count range | Integer |
| sellerCountry | Seller origin country code(s), comma-separated | CN,US |
| countryCode | Target marketplace (US, JP, DE) | US |
Sorting & Pagination
| Parameter | Description | Default |
|---|---|---|
| sortField | Sort by field (see options below) | purchasedClicksT360 |
| sortType | Sort direction: desc or asc | desc |
| page | Page number | 1 |
| pageSize | Results per page (10-100) | 50 |
Available sort fields: totalReviews, price, launchDate, clickCountT7, clickCountT30, clickCountT90, clickConversionRate, clickConversionRateComposite, customerRating, purchasedClicksT360, clickCountGrowthT7, clickCountGrowthT30, currentPrice, fbaFee, shippingFee, gpm
API Usage
This tool calls the LinkFox tool gateway API. See references/api.md for calling conventions, request parameters, and response structure. You can also execute scripts/jiimore_product_discovery.py directly to run queries.
Usage Examples
1. Find high-conversion wireless chargers in the US market
{
"keyword": "wireless charger",
"countryCode": "US",
"clickConversionRateMin": 0.1,
"sortField": "clickConversionRate",
"sortType": "desc"
}2. Discover fast-growing new products (listed within the last 6 months, weekly click growth > 20%)
{
"keyword": "desk lamp",
"countryCode": "US",
"launchDateMin": "20250901000000",
"clickCountGrowthT7Min": 0.2,
"sortField": "clickCountGrowthT7",
"sortType": "desc"
}3. Find underpriced high-margin products with low competition (few reviews)
{
"keyword": "phone stand",
"countryCode": "US",
"priceMin": 10,
"priceMax": 30,
"totalReviewsMax": 100,
"grossProfitMarginMin": 0.3,
"sortField": "gpm",
"sortType": "desc"
}4. Mine products from Chinese sellers with strong monthly click growth in the German market
{
"keyword": "Handyhuelle",
"countryCode": "DE",
"sellerCountry": "CN",
"clickCountGrowthT30Min": 0.15,
"sortField": "clickCountGrowthT30",
"sortType": "desc"
}5. Find high-rated products with strong annual sales in the Japanese market
{
"keyword": "ワイヤレスイヤホン",
"countryCode": "JP",
"customerRatingMin": 4.0,
"salesVolumeT360Min": 1000,
"sortField": "purchasedClicksT360",
"sortType": "desc"
}6. Identify niche opportunities with high composite conversion and multiple niche markets
{
"keyword": "yoga mat",
"countryCode": "US",
"clickConversionRateCompositeMin": 0.15,
"nicheCountMin": 3,
"sortField": "clickConversionRateComposite",
"sortType": "desc"
}Display Rules
1. Present data clearly: Show query results in well-structured tables, including product title, ASIN, price, ratings, conversion rates, click counts, and growth rates 2. Rate formatting: Always display rate values as percentages for readability (e.g., show 0.12 as 12%). Remind users that the API accepts decimals (0-1) 3. Image display: When product image URLs are available, display the main product image alongside the data 4. Pagination awareness: When results span multiple pages, inform the user of the total count and current page, and offer to fetch additional pages 5. Keyword translation reminder: Remind users that keywords must be in the target marketplace language (English for US, Japanese for JP, German for DE) 6. Error handling: When a query fails, explain the reason based on the response and suggest adjusting query criteria 7. No subjective advice: Present factual product data without making subjective business recommendations
Important Limitations
- Keyword is mandatory: Every query requires a keyword; browsing without a keyword is not supported
- Three marketplaces only: Currently limited to US, JP, and DE
- Page size cap: Maximum 100 results per page
- Rate values: All rate/percentage parameters must be passed as decimals (0-1), not percentages
- Launch date format: Must follow the
yyyyMMdd000000format exactly (e.g.,20250101000000)
User Expression & Scenario Quick Reference
Applicable -- Product discovery and mining tasks:
| User Says | Scenario |
|---|---|
| "Find hot products for keyword X" | Keyword-based product discovery |
| "High conversion products", "best sellers" | High-conversion product screening |
| "Fast growing products", "trending items" | Click growth-based discovery |
| "New products with high potential" | New listing + growth filtering |
| "Products with good margins", "profitable items" | Gross profit margin screening |
| "Low competition products", "few reviews" | Low-review opportunity mining |
| "Products from Chinese sellers" | Seller origin filtering |
| "Niche market opportunities" | Niche count-based discovery |
Not applicable -- Needs beyond product discovery:
- ABA search term data and keyword analysis (use ABA Data Explorer)
- Advertising / PPC campaign management
- Product reviews and listing optimization
- Inventory management and supply chain
- Comprehensive market reports with profit/pricing strategy
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/jiimore_product_discovery.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 -->
--- For more high-quality, professional cross-border e-commerce skills, set [LinkFox Skills](https://skill.linkfox.com/).
极目-亚马逊-产品挖掘 API 参考
调用规范
- 请求地址:
https://tool-gateway.linkfox.com/jiimore/productDiscovery - 请求方式:POST,Content-Type: application/json
- 认证方式:Header
Authorization: <api_key>,api_key 从环境变量LINKFOXAGENT_API_KEY读取(如未配置,提示用户前往 https://skill.linkfox.com/linkfoxskills/guide.htm 申请)
请求参数
POST Body(JSON):
必填参数
| 参数 | 类型 | 必填 | 说明 |
|---|---|---|---|
| keyword | string | 是 | 关键词(必填,并根据所选国家,翻译关键词为对应国家的语言) |
筛选参数
| 参数 | 类型 | 必填 | 说明 |
|---|---|---|---|
| countryCode | string | 否 | 国家,使用国家简称。默认 US。可选值:US、JP、DE |
| priceMin | number | 否 | 最低商品价格 |
| priceMax | number | 否 | 最高商品价格 |
| totalReviewsMin | integer | 否 | 最低评论数 |
| totalReviewsMax | integer | 否 | 最高评论数 |
| customerRatingMin | number | 否 | 最低评分 |
| customerRatingMax | number | 否 | 最高评分 |
| clickConversionRateMin | number | 否 | 最低点击购买转化率,数值范围为0-1,0.1表示10% |
| clickConversionRateMax | number | 否 | 最高点击购买转化率,数值范围为0-1,0.1表示10% |
| clickConversionRateCompositeMin | number | 否 | 最低综合转化率,数值范围为0-1,0.1表示10% |
| clickConversionRateCompositeMax | number | 否 | 最高综合转化率,数值范围为0-1,0.1表示10% |
| clickCountT7Min | integer | 否 | 最低周点击量 |
| clickCountT7Max | integer | 否 | 最高周点击量 |
| clickCountT30Min | integer | 否 | 最低月点击量 |
| clickCountT30Max | integer | 否 | 最高月点击量 |
| clickCountGrowthT7Min | number | 否 | 最低周点击增长率,数值范围为0-1,0.1表示10% |
| clickCountGrowthT7Max | number | 否 | 最高周点击增长率,数值范围为0-1,0.1表示10% |
| clickCountGrowthT30Min | number | 否 | 最低月点击增长率,数值范围为0-1,0.1表示10% |
| clickCountGrowthT30Max | number | 否 | 最高月点击增长率,数值范围为0-1,0.1表示10% |
| salesVolumeT360Min | integer | 否 | 最低年销售量 |
| salesVolumeT360Max | integer | 否 | 最高年销售量 |
| grossProfitMarginMin | number | 否 | 最低毛利率 |
| grossProfitMarginMax | number | 否 | 最高毛利率 |
| fbaFeeMin | number | 否 | 最低FBA佣金 |
| fbaFeeMax | number | 否 | 最高FBA佣金 |
| launchDateMin | string | 否 | 最小上架时间,格式为:yyyyMMdd000000 |
| launchDateMax | string | 否 | 最大上架时间,格式为:yyyyMMdd000000 |
| nicheCountMin | integer | 否 | 最低细分市场数量 |
| nicheCountMax | integer | 否 | 最高细分市场数量 |
| sellerCountry | string | 否 | 卖家国家地区编码,选择多个的情况下用逗号隔开,如:CN,US |
排序与分页
| 参数 | 类型 | 必填 | 说明 |
|---|---|---|---|
| sortField | string | 否 | 排序字段。默认 purchasedClicksT360。可选值:totalReviews(总评论数)、price(价格)、launchDate(上架时间)、clickCountT7(7天点击量)、clickCountT30(30天点击量)、clickCountT90(90天点击量)、clickConversionRate(点击购买转化率)、clickConversionRateComposite(综合点击购买转化率)、customerRating(评分)、purchasedClicksT360(360天购买量)、clickCountGrowthT7(周点击增长率)、clickCountGrowthT30(月点击增长率)、currentPrice(当前价格)、fbaFee(FBA佣金)、shippingFee(FBA运费)、gpm(毛利率) |
| sortType | string | 否 | 排序方式。默认 desc。可选值:desc(降序)、asc(升序) |
| page | integer | 否 | 页码。默认 1 |
| pageSize | integer | 否 | 每页数量(10-100)。默认 50 |
响应结构
| 字段 | 类型 | 说明 |
|---|---|---|
| total | integer | 总数 |
| sourceTool | string | 工具类型:jiimore |
| sourceType | string | 来源类型:amazon |
| type | string | 渲染的样式 |
| title | string | 标题 |
| costToken | integer | 消耗token |
| columns | array | 渲染的列 |
| products | array | 产品列表(详见下方) |
产品对象字段
| 字段 | 类型 | 说明 |
|---|---|---|
| asin | string | 亚马逊商品ASIN |
| parentAsin | string | 亚马逊商品父ASIN |
| title | string | 产品标题 |
| brand | string | 品牌 |
| price | number | 价格 |
| imageUrl | string | 产品主图 |
| productImageUrls | array | 产品图片链接列表 |
| asinUrl | string | ASIN链接 |
| ratings | integer | 评论数 |
| availableDate | string | 上架时间(时间戳) |
| availableDateString | string | 上架日期(字符串) |
| categoryNames | array | 类目信息 |
| marketplaceId | string | 站点ID |
| clickCountT7 | integer | 周点击量 |
| clickCountT30 | integer | 月点击量 |
| clickCountT90 | integer | 季度点击量 |
| clickConversionRate | number | 点击购买转化率 |
| clickConversionRateComposite | number | 综合转化率 |
| grossProfitMargin | number | 毛利率 |
| fbaFee | number | 亚马逊佣金 |
| shippingFee | number | FBA运费 |
| sourceTool | string | 工具类型:jiimore |
| sourceType | string | 来源类型:amazon |
错误码
正常情况下,接口的 HTTP 状态码均为 200,业务的成功与否通过响应体中的 errorCode 字段区分(errorCode = 200 表示成功,其他值表示业务错误)。当遇到未授权等情况时,HTTP 状态码为 401,且对应的 errorCode 也是 401。
| errcode | 含义 | 处理建议 |
|---|---|---|
| 200 | 成功 | 正常解析业务字段 |
| 401 | 认证失败 | 检查请求头 Authorization 是否正确携带 API Key;API Key 申请方式请参考上述调用规范下的认证方式。 |
| 其他非200值 | 业务异常 | 参考 errmsg 字段获取具体错误原因 |
错误响应示例:
{
"errcode": 401,
"errmsg": "authorized error"
}curl 示例
curl -X POST https://tool-gateway.linkfox.com/jiimore/productDiscovery \
-H "Authorization: $LINKFOXAGENT_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"keyword": "wireless charger",
"countryCode": "US",
"clickConversionRateMin": 0.1,
"priceMin": 10,
"priceMax": 50,
"sortField": "clickConversionRate",
"sortType": "desc",
"page": 1,
"pageSize": 20
}'---
Feedback API
This endpoint is separate from the tool API above. Do not mix the two base URLs.
- POST
https://skill-api.linkfox.com/api/v1/public/feedback - Content-Type:
application/json
{
"skillName": "linkfox-xxx-xxx",
"sentiment": "POSITIVE",
"category": "OTHER",
"content": "Results were accurate, user was satisfied."
}Field rules:
skillName: Use this skill'snamefrom the YAML frontmattersentiment: Choose ONE —POSITIVE(praise),NEUTRAL(suggestion without emotion),NEGATIVE(complaint or error)category: Choose ONE —BUG(malfunction or wrong data),COMPLAINT(user dissatisfaction),SUGGESTION(improvement idea),OTHERcontent: Include what the user said or intended, what actually happened, and why it is a problem or praise
#!/usr/bin/env python3
"""
Jiimore Product Discovery - LinkFox Skill
Calls the jiimore/productDiscovery API endpoint to find high-potential Amazon products.
Usage:
python jiimore_product_discovery.py '{"keyword": "wireless charger", "countryCode": "US", "clickConversionRateMin": 0.1}'
"""
import json
import os
import sys
from urllib.request import urlopen, Request
from urllib.error import HTTPError, URLError
API_URL = "https://tool-gateway.linkfox.com/jiimore/productDiscovery"
def get_api_key():
"""Retrieve the API key from environment, with a friendly prompt if missing."""
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 the environment variable: export LINKFOXAGENT_API_KEY=your-key-here",
file=sys.stderr,
)
sys.exit(1)
return key
def call_api(params: dict) -> dict:
"""Send a POST request to the Jiimore Product Discovery API."""
api_key = get_api_key()
# Ensure the required keyword parameter is present
if "keyword" not in params or not params["keyword"]:
return {"error": "Missing required parameter: keyword"}
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():
if len(sys.argv) < 2:
print("Usage: jiimore_product_discovery.py '<JSON parameters>'", file=sys.stderr)
print(
'Example: jiimore_product_discovery.py \'{"keyword": "wireless charger", "countryCode": "US", "clickConversionRateMin": 0.1}\'',
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()
#!/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())