
Linkfox Fastmoss Product Search
- 262 installs
- 64 repo stars
- Updated August 3, 2026
- linkfox-ai/linkfox-skills
Search FastMoss for trending TikTok Shop products, niches, and creators to spot early demand signals before committing to a SKU or supplier.
About
Agent skill that queries FastMoss to discover trending TikTok Shop products, niches, and seller opportunities. Use it during early ideation to shortlist categories, compare viral items, and feed downstream validation with Jungle Scout, Keepa, or compliance checks.
- FastMoss TikTok Shop product discovery
- Trend and niche scanning for new SKUs
- Creator-commerce demand signals
- Agent-callable marketplace research API
- Early-stage opportunity shortlisting
Linkfox Fastmoss Product Search by the numbers
- 262 all-time installs (skills.sh)
- +38 installs in the week ending Aug 2, 2026 (Skillselion tracking)
- Ranked #257 of 853 Sales & Marketing skills by installs in the Skillselion catalog
- Data as of Aug 4, 2026 (Skillselion catalog sync)
npx skills add https://github.com/linkfox-ai/linkfox-skills --skill linkfox-fastmoss-product-searchAdd your badge
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| Installs | 262 |
|---|---|
| repo stars | ★ 64 |
| Last updated | August 3, 2026 |
| Repository | linkfox-ai/linkfox-skills ↗ |
What it does
Search FastMoss for trending TikTok Shop products, niches, and creators to spot early demand signals before committing to a SKU or supplier.
Files
FastMoss - TikTok Product Search
This skill guides you on how to search and filter TikTok Shop product data using FastMoss, helping sellers and marketers discover product opportunities, evaluate sales performance, and identify influencer-driven products across 15 TikTok markets worldwide.
Core Concepts
FastMoss is a well-known TikTok e-commerce data platform that tracks product performance across multiple TikTok marketplaces. This tool provides keyword-based product search with rich filtering capabilities, returning detailed product data including multi-period sales (7-day/28-day/90-day/total), GMV (revenue), pricing, ratings, review counts, commission rates, influencer promotion statistics, and shop information.
Sales metrics: Products include multi-period sales data — 7-day, 28-day, 90-day, and total sales. The same granularity applies to GMV (Gross Merchandise Value) amounts.
Commission rate: Stored as a decimal (e.g., 0.10 means 10%). When displaying to the user, convert to percentage format.
Shop types: Products can be filtered by shop type — local shops (1) or cross-border shops (2). The isCrossBorder field (1=cross-border, 0=local) and isSShopText field (TikTok fully-managed shop) provide additional shop classification.
Parameter Guide
Search & Filtering
| Parameter | Type | Required | Description |
|---|---|---|---|
| keyword | string | No | Search keyword (product title fuzzy match) |
| region | string | No | Market region code. Supported: US, GB, MX, ES, DE, IT, FR, ID, VN, MY, TH, PH, BR, JP, SG |
| category | string | No | Category name in English, matched to TikTok category ID. Non-English should be translated first |
| shopType | integer | No | Shop type: 1=local shop, 2=cross-border shop |
Boolean Filters
| Parameter | Type | Required | Description |
|---|---|---|---|
| isTopSelling | boolean | No | Filter hot-selling products only |
| isNewListed | boolean | No | Filter new products only |
| isSshop | boolean | No | Filter TikTok fully-managed (S-shop) products only |
| isFreeShipping | boolean | No | Filter free-shipping products only |
| isLocalWarehouse | boolean | No | Filter local warehouse products only |
Range Filters
| Parameter | Type | Required | Description |
|---|---|---|---|
| unitsSoldRange | object | No | Sales volume range filter: {min, max} |
| commissionRateRange | object | No | Commission rate range filter: {min, max} |
| creatorCountRange | object | No | Creator/influencer count range filter: {min, max} |
Sorting & Pagination
| Parameter | Type | Required | Description |
|---|---|---|---|
| orderField | string | No | Sort field: day7_units_sold, day7_gmv, commission_rate, total_units_sold, total_gmv, creator_count. Default: descending |
| page | integer | No | Page number, default 1 |
| pageSize | integer | No | Items per page, max 10, default 10 |
Supported Markets (15)
US (United States), GB (United Kingdom), MX (Mexico), ES (Spain), DE (Germany), IT (Italy), FR (France), ID (Indonesia), VN (Vietnam), MY (Malaysia), TH (Thailand), PH (Philippines), BR (Brazil), JP (Japan), SG (Singapore)
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/fastmoss_product_search.py directly to run queries.
Data Fields
Key fields returned for each product:
| Field | Description |
|---|---|
| title | Product name |
| productId | Unique product identifier |
| region | Market region code |
| price, minPrice, maxPrice | Product price and price range |
| currency | Currency code |
| totalSaleCnt | Total cumulative sales |
| totalSale1dCnt, totalSale7dCnt, totalSale28dCnt, totalSale90dCnt | Sales by period |
| totalSaleGmvAmt, totalSaleGmv7dAmt, totalSaleGmv28dAmt | GMV by period |
| totalVideoCnt, totalLiveCnt, totalIflCnt | Video count, live count, influencer count |
| productCommissionRate | Commission rate (decimal, 0.10 = 10%) |
| productRating, reviewCount | Rating and review count |
| skuCount | Number of SKUs |
| shopName, shopSellerId, shopTotalUnitsSold | Shop information |
| isCrossBorder | 1=cross-border, 0=local |
| isSShopText, freeShippingText | Fully-managed shop flag, free shipping flag |
| salesTrendFlagText | Sales trend label |
| categoryName | Product category |
| tiktokUrl, fastmossUrl, imageUrl | Links and image |
Usage Examples
1. Basic Keyword Search — Find top-selling products
{
"keyword": "phone case",
"region": "US",
"orderField": "total_units_sold",
"pageSize": 10
}2. High-Commission Product Discovery — Products with commission >= 10%
{
"keyword": "beauty",
"region": "US",
"commissionRateRange": {"min": 0.10},
"orderField": "commission_rate"
}3. Cross-Border Shop Products — Filter by shop type
{
"keyword": "gadget",
"region": "US",
"shopType": 2,
"orderField": "day7_units_sold"
}4. Influencer-Hot Products — Products promoted by many creators
{
"keyword": "skincare",
"region": "US",
"creatorCountRange": {"min": 50},
"orderField": "creator_count"
}5. Hot-Selling New Products on TikTok
{
"keyword": "fashion",
"region": "GB",
"isTopSelling": true,
"isNewListed": true,
"orderField": "day7_gmv"
}Display Rules
1. Present data only: Show query results in organized tables with key columns — product name, price, total sales, 7-day sales, GMV, rating, commission rate, and number of promoting influencers. Do not make subjective business advice 2. Commission formatting: Commission rate is a decimal (0.10 = 10%) — always display as percentage for readability 3. Cross-border awareness: isCrossBorder: 1 = cross-border shop, 0 = local shop. Display clearly 4. Currency awareness: Include the currency field from the response when displaying prices and GMV 5. Trend labels: Display salesTrendFlagText directly as the trend indicator 6. Shop flags: Display freeShippingText and isSShopText directly (values are readable text) 7. Error handling: When a query fails, explain the reason based on the response and suggest adjusting parameters
Important Limitations
- No required parameters (all optional), but at minimum provide keyword or category for meaningful results
- Max 10 items per page
Applicable Scenarios
| User Says | Scenario |
|---|---|
| "Find trending products on TikTok" | Keyword search sorted by sales |
| "TikTok products with high commission" | Filter by commission rate range |
| "What's selling well on TikTok Shop US" | Regional product search by sales |
| "Search TikTok cross-border shop products" | Filter by shopType=2 |
| "Which products have many influencers promoting them" | Filter by creator count range |
| "TikTok fully-managed shop products" | Filter with isSshop=true |
| "TikTok product research for Southeast Asia" | Search specific SE Asian regions |
| "New hot-selling products on TikTok" | Use isTopSelling + isNewListed flags |
| "FastMoss product data" | Direct platform reference |
Not Applicable Scenarios
- TikTok influencer/creator analytics (follower counts, engagement rates of creators)
- TikTok video performance analytics (views, likes, shares on specific videos)
- TikTok advertising / ad campaign management
- Amazon, Shopee, or other non-TikTok platform product data
- TikTok Shop store-level analytics
- Product listing creation or optimization advice
- Logistics, fulfillment, or shipping analysis
Boundary judgment: When users say "product research" or "what should I sell on TikTok", if it involves searching and filtering products by sales data, pricing, or commission rates on TikTok Shop, then this skill applies. If they're asking about content strategy, video creation, or influencer outreach, it does not apply.
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/fastmoss_product_search.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, visit [LinkFox Skills](https://skill.linkfox.com/).
FastMoss-TikTok商品搜索 API 参考
调用规范
- 请求地址:
https://tool-gateway.linkfox.com/fastmoss/productSearch - 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):
| 参数 | 类型 | 必填 | 说明 |
|---|---|---|---|
| keyword | string | 否 | 搜索关键词(商品标题模糊匹配) |
| region | string | 否 | 市场区域代码。可选值:US(美国)、GB(英国)、MX(墨西哥)、ES(西班牙)、DE(德国)、IT(意大利)、FR(法国)、ID(印度尼西亚)、VN(越南)、MY(马来西亚)、TH(泰国)、PH(菲律宾)、BR(巴西)、JP(日本)、SG(新加坡) |
| category | string | 否 | 英文类目名称,系统自动匹配TikTok类目ID。非英文需先翻译为英文 |
| shopType | integer | 否 | 店铺类型:1=本地店铺,2=跨境店铺 |
| isTopSelling | boolean | 否 | 仅筛选热销商品 |
| isNewListed | boolean | 否 | 仅筛选新上架商品 |
| isSshop | boolean | 否 | 仅筛选TikTok全托管(S-shop)商品 |
| isFreeShipping | boolean | 否 | 仅筛选包邮商品 |
| isLocalWarehouse | boolean | 否 | 仅筛选本地仓发货商品 |
| unitsSoldRange | object | 否 | 销量范围筛选,格式:{"min": 100, "max": 5000} |
| commissionRateRange | object | 否 | 佣金率范围筛选,格式:{"min": 0.05, "max": 0.20}(小数,0.10=10%) |
| creatorCountRange | object | 否 | 带货达人数范围筛选,格式:{"min": 10, "max": 500} |
| orderField | string | 否 | 排序字段:day7_units_sold(7天销量)、day7_gmv(7天GMV)、commission_rate(佣金率)、total_units_sold(总销量)、total_gmv(总GMV)、creator_count(达人数)。默认降序排列 |
| page | integer | 否 | 页码,默认 1 |
| pageSize | integer | 否 | 每页条数,最大 10,默认 10 |
响应结构
顶层字段
| 字段 | 类型 | 说明 |
|---|---|---|
| total | integer | 符合条件的总记录数 |
| products | array | 商品信息列表(详见下方) |
| columns | array | 渲染列定义 |
| type | string | 渲染样式类型 |
| costToken | integer | 消耗 token 数 |
商品对象字段(products 数组)
| 字段 | 类型 | 说明 |
|---|---|---|
| title | string | 商品标题 |
| productId | string | 商品唯一标识ID |
| region | string | 市场区域代码 |
| price | number | 商品价格 |
| minPrice | number | 最低价格 |
| maxPrice | number | 最高价格 |
| currency | string | 货币代码 |
| totalSaleCnt | integer | 累计总销量 |
| totalSale1dCnt | integer | 1天销量 |
| totalSale7dCnt | integer | 7天销量 |
| totalSale28dCnt | integer | 28天销量 |
| totalSale90dCnt | integer | 90天销量 |
| totalSaleGmvAmt | number | 累计总GMV |
| totalSaleGmv7dAmt | number | 7天GMV |
| totalSaleGmv28dAmt | number | 28天GMV |
| totalVideoCnt | integer | 带货视频数 |
| totalLiveCnt | integer | 直播带货数 |
| totalIflCnt | integer | 带货达人数 |
| productCommissionRate | number | 商品佣金比例(小数,0.10=10%) |
| productRating | number | 商品评分 |
| reviewCount | integer | 评论数量 |
| skuCount | integer | SKU数量 |
| shopName | string | 店铺名称 |
| shopSellerId | string | 卖家ID |
| shopTotalUnitsSold | integer | 店铺总销量 |
| isCrossBorder | integer | 是否跨境:1=跨境,0=本地 |
| isSShopText | string | 是否全托管店铺(是/否) |
| freeShippingText | string | 是否包邮(是/否) |
| availableDate | string | 上架时间 |
| categoryName | string | 商品品类名称 |
| salesTrendFlagText | string | 销售趋势标记 |
| tiktokUrl | string | TikTok商品链接 |
| fastmossUrl | string | FastMoss商品详情链接 |
| imageUrl | string | 商品图片URL |
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/fastmoss/productSearch \
-H "Authorization: $LINKFOXAGENT_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"keyword": "phone case",
"region": "US",
"orderField": "day7_units_sold",
"pageSize": 10
}'带范围筛选的示例:
curl -X POST https://tool-gateway.linkfox.com/fastmoss/productSearch \
-H "Authorization: $LINKFOXAGENT_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"keyword": "beauty",
"region": "US",
"commissionRateRange": {"min": 0.10},
"creatorCountRange": {"min": 50},
"orderField": "commission_rate",
"pageSize": 10
}'---
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-fastmoss-product-search",
"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
"""
FastMoss TikTok Product Search - LinkFox Skill
Calls the fastmoss/productSearch API endpoint
Usage:
python fastmoss_product_search.py '{"keyword": "phone case", "region": "US"}'
"""
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/fastmoss/productSearch"
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:
"""Call the tool gateway API."""
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():
if len(sys.argv) < 2:
print("Usage: fastmoss_product_search.py '<JSON parameters>'", file=sys.stderr)
print(
'Example: fastmoss_product_search.py \'{"keyword": "phone case", "region": "US"}\'',
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())