
Linkfox Amazon Policy Feed
- 157 installs
- 64 repo stars
- Updated August 3, 2026
- linkfox-ai/linkfox-skills
Helps with ai & agent building tasks.
About
linkfox-amazon-policy-feed is a Claude Code skill for ai & agent building. It helps solo builders move faster with AI-assisted development.
- linkfox-amazon-policy-feed
- AI & Agent Building
- AI-coding skill
Linkfox Amazon Policy Feed by the numbers
- 157 all-time installs (skills.sh)
- +36 installs in the week ending Aug 2, 2026 (Skillselion tracking)
- Ranked #3,272 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 | 157 |
|---|---|
| repo stars | ★ 64 |
| Last updated | August 3, 2026 |
| Repository | linkfox-ai/linkfox-skills ↗ |
What it does
Helps with ai & agent building tasks.
Files
Amazon Policy & Regulation Feed
This skill retrieves Amazon's latest policy & regulation feed for cross-border sellers. It is a two-step (list then detail) flow: first list feed items by site / time window, then fetch the full article body by its id.
Core Concepts
- Source: Amazon official policy & regulation updates for sellers, curated by AI to surface items valuable to cross-border operations.
- AI summary: Each feed item includes a
summaryZhfield — an AI-generated 1-3 sentence Chinese summary for quick scanning. - Two coupled tools:
1. amazon/policyFeed — paginated list; returns structured records with title, AI summary, original URL, and publish time. 2. amazon/policyFeedDetail — full article body (Markdown) for a single record id obtained from the list.
- Time range: Defaults to the last 7 days; supports custom time windows via
publishedAtGte/publishedAtLte.
Parameters
List (amazon/policyFeed)
| Parameter | Type | Required | Description | Default |
|---|---|---|---|---|
| site | string | No | Marketplace code (uppercase); site filtering only applies to some feed item types, others are always returned regardless of site | US |
| publishedAtGte | string | No | Publish/change time lower bound (incl.), yyyy-MM-dd HH:mm:ss | last 7 days |
| publishedAtLte | string | No | Publish/change time upper bound (incl.), yyyy-MM-dd HH:mm:ss | now |
| page | integer | No | Page number, starting at 1 | 1 |
| pageSize | integer | No | Items per page, 1-100 | 20 |
Detail (amazon/policyFeedDetail)
| Parameter | Type | Required | Description |
|---|---|---|---|
| id | string | Yes | Record ID (32-char string) from the list response data[].id |
Supported Marketplaces (for site)
US, JP, UK, AU, BE, BR, CA, EG, FR, DE, IN, IT, MX, NL, PL, SA, SG, ES, SE, TR, AE, ZA, IE. Default is US when not specified. Note: site filtering only applies to some feed item types; others are always returned regardless of site.
API Usage
See references/api.md for calling conventions, request parameters, response structure, and error codes. Run scripts directly:
python scripts/amazon_policy_feed.py '{"site": "US", "pageSize": 20}'
python scripts/amazon_policy_feed_detail.py '{"id": "<id from list>"}'How to Build Queries
1. Set the time window: convert user's time reference into publishedAtGte / publishedAtLte. Leave empty for the default last 7 days. 2. Pick the marketplace: map user's target country to the site code (default US). Note this only filters some feed item types. 3. Paginate: increase page to scan deeper; max 100 items per page. 4. Drill into a record: take a record's id from the list and call the detail script to read the full body.
Usage Examples
1. Recent feed (last 7 days, US)
{"site": "US", "pageSize": 20}2. Custom date range
{"site": "US", "publishedAtGte": "2026-05-01 00:00:00", "publishedAtLte": "2026-05-31 23:59:59"}3. Japan site feed, page 2
{"site": "JP", "page": 2, "pageSize": 50}4. Full body of one record
{"id": "a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4"}Display Rules
1. List view: present results as a table with title, AI summary (summaryZh), publish time, and original URL link. 2. Detail view: render the stdout Markdown as-is; the response also includes title and summaryZh for context. 3. Only present data: report what the feed says; do not add subjective business advice or speculate on future policy. 4. Timeliness note: data may lag the live page by a short period; the Amazon original is authoritative. 5. Error handling: on a failed call, explain the reason from the error response (e.g. invalid id -> re-fetch from the list) instead of guessing.
Important Limitations
- Default window is 7 days: without explicit time params, only the last 7 days are returned.
- Max 100 items per page:
pageSizerange is 1-100. - Detail needs a valid list `id`:
amazon/policyFeedDetailonly accepts anidreturned byamazon/policyFeed; unknown ids return an error. - Not for aggregation: this skill's output is long-form text and metadata — not suited for second-pass statistical/aggregation analysis via
_dataQuery_executeDynamicQuery.
User Expression & Scenario Quick Reference
Applicable — Amazon official policy & regulation feed:
| User Says | Scenario |
|---|---|
| "最近亚马逊有什么政策变化" | Recent policy feed overview |
| "亚马逊美国站近一周的政策新闻" | Site-filtered policy news |
| "亚马逊最近有什么政策法规更新" | General policy/regulation updates |
| "亚马逊 FBA 最新政策法规" | Topic-specific policy lookup |
| "查看这条政策资讯的全文" | Fetch full article body by id |
| "Amazon latest policy updates" | English trigger |
Not applicable — beyond policy & regulation feed:
- Product / keyword / sales analytics, listing optimization, review analysis
- Real-time storefront search results or product detail
- Account-specific notifications inside an individual seller account
- Historical patent or trademark searches
Boundary judgment: if the user wants Amazon's officially published policy, regulation, or compliance updates for sellers (and its full text), this skill applies. If they want product/keyword/sales data, use the corresponding data skills.
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/amazon_policy_feed.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.
This skill exposes multiple entry scripts:amazon_policy_feed.py,amazon_policy_feed_detail.py. Pass--script scripts/<name>.pyto choose the one you need.
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/).
亚马逊最新政策法规与资讯 API 参考
本 skill 封装两个串联接口:政策法规资讯列表(amazon/policyFeed)与资讯详情(amazon/policyFeedDetail)。先用列表接口拿到资讯 id,再用详情接口获取完整正文。
调用规范
- 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) - 业务成功判定:HTTP 状态码 200,业务成功以响应体
errcode字段为准(errcode = 200成功,其他值为业务错误,errmsg给出原因)
---
一、政策法规资讯列表
- 请求地址:
https://tool-gateway.linkfox.com/amazon/policyFeed - 脚本:
scripts/amazon_policy_feed.py
请求参数
| 参数 | 类型 | 必填 | 说明 |
|---|---|---|---|
| site | string | 否 | 亚马逊站点代码(大写),默认 US。站点筛选仅对部分资讯类型生效,部分资讯不区分站点始终返回。可选值:US/JP/UK/AU/BE/BR/CA/EG/FR/DE/IN/IT/MX/NL/PL/SA/SG/ES/SE/TR/AE/ZA/IE |
| publishedAtGte | string | 否 | 发布/变更时间下界(含),格式 yyyy-MM-dd HH:mm:ss。未传默认近 7 天 |
| publishedAtLte | string | 否 | 发布/变更时间上界(含),格式 yyyy-MM-dd HH:mm:ss。未传默认当前时间 |
| page | integer | 否 | 页码,从 1 开始,默认 1 |
| pageSize | integer | 否 | 每页条数,默认 20,取值范围 1-100 |
响应结构
| 字段 | 类型 | 说明 |
|---|---|---|
| errcode | integer | 网关响应码(200 成功) |
| errmsg | string | 提示信息 |
| code | string | 业务响应码("200" 成功) |
| msg | string | 业务提示信息 |
| total | integer | 本次返回的条数 |
| type | string | 渲染样式,固定 tableListWorkbenches |
| data | array | 资讯列表,按发布/变更时间倒序(见下表) |
| costTime | integer | 总处理耗时(毫秒) |
| costToken | integer | token 消耗量 |
| columns | array | 前端列定义 |
data 资讯对象字段
| 字段 | 类型 | 说明 |
|---|---|---|
| id | string | 记录 ID(32 位字符串),用作 amazon/policyFeedDetail 入参 |
| title | string | 资讯标题 |
| summaryZh | string | 中文摘要,AI 生成的 1-3 句话概括 |
| originalUrl | string | 原文链接 |
| publishedAt | string | 发布/变更时间,格式 yyyy-MM-dd HH:mm:ss |
curl 示例
curl -X POST https://tool-gateway.linkfox.com/amazon/policyFeed \
-H "Authorization: $LINKFOXAGENT_API_KEY" \
-H "Content-Type: application/json" \
-d '{"site": "US", "pageSize": 20}'---
二、资讯详情
- 请求地址:
https://tool-gateway.linkfox.com/amazon/policyFeedDetail - 脚本:
scripts/amazon_policy_feed_detail.py
请求参数
| 参数 | 类型 | 必填 | 说明 |
|---|---|---|---|
| id | string | 是 | 资讯记录 ID(32 位字符串),来自列表接口响应的 data[].id |
响应结构
| 字段 | 类型 | 说明 |
|---|---|---|
| errcode | integer | 网关响应码(200 成功) |
| errmsg | string | 提示信息/错误信息 |
| type | string | 响应类型,固定 stdoutWorkbenches(前端按 Markdown 渲染 stdout) |
| stdout | string | 资讯完整正文(Markdown 格式) |
| title | string | 资讯标题 |
| summaryZh | string | 中文摘要(AI 生成的 1-3 句话概括) |
| costTime | integer | 总处理耗时(毫秒) |
| costToken | integer | token 消耗量 |
curl 示例
curl -X POST https://tool-gateway.linkfox.com/amazon/policyFeedDetail \
-H "Authorization: $LINKFOXAGENT_API_KEY" \
-H "Content-Type: application/json" \
-d '{"id": "a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4"}'---
错误码
| code | 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 |
| 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(详情接口传入无效 id):
{
"errcode": 400,
"errmsg": "未找到该资讯记录。"
}---
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-amazon-policy-feed",
"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
"""
Amazon Policy & Regulation Feed (detail) - LinkFox Skill
调用 amazon/policyFeedDetail 接口,按资讯记录 ID 查询完整正文(Markdown)。
入参 id 来自 amazon_policy_feed.py 列表响应中的 data[].id 字段。
Usage:
python amazon_policy_feed_detail.py '{"id": "a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4"}'
"""
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/amazon/policyFeedDetail"
def get_api_key():
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:
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:
result = json.loads(response.read().decode("utf-8"))
if isinstance(result, dict) and "errcode" in result and result["errcode"] != 200:
print(
f"Business error: errcode={result['errcode']}, errmsg={result.get('errmsg', '')}",
file=sys.stderr,
)
return result
except HTTPError as e:
body = e.read().decode("utf-8") if e.fp else ""
try:
parsed = json.loads(body) if body else None
except (json.JSONDecodeError, ValueError):
parsed = None
if isinstance(parsed, dict) and "code" in parsed:
return parsed
errmsg = f"HTTP {e.code}: {e.reason}"
if body:
errmsg += f" - {body}"
return {"code": str(e.code), "msg": errmsg}
except URLError as e:
return {"code": "-1", "msg": f"Connection failed: {e.reason}"}
def main():
if len(sys.argv) < 2:
print("Usage: amazon_policy_feed_detail.py '<JSON parameters>'", file=sys.stderr)
print(
'Example: amazon_policy_feed_detail.py \'{"id": "a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4"}\'',
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
"""
Amazon Policy & Regulation Feed (list) - LinkFox Skill
调用 amazon/policyFeed 接口,按站点 / 时间区间分页查询
亚马逊最新政策法规与资讯列表(含 AI 中文摘要)。
Usage:
python amazon_policy_feed.py '{"site": "US", "pageSize": 20}'
"""
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/amazon/policyFeed"
def get_api_key():
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:
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:
result = json.loads(response.read().decode("utf-8"))
if isinstance(result, dict) and "errcode" in result and result["errcode"] != 200:
print(
f"Business error: errcode={result['errcode']}, errmsg={result.get('errmsg', '')}",
file=sys.stderr,
)
return result
except HTTPError as e:
body = e.read().decode("utf-8") if e.fp else ""
try:
parsed = json.loads(body) if body else None
except (json.JSONDecodeError, ValueError):
parsed = None
if isinstance(parsed, dict) and "code" in parsed:
return parsed
errmsg = f"HTTP {e.code}: {e.reason}"
if body:
errmsg += f" - {body}"
return {"code": str(e.code), "msg": errmsg}
except URLError as e:
return {"code": "-1", "msg": f"Connection failed: {e.reason}"}
def main():
if len(sys.argv) < 2:
print("Usage: amazon_policy_feed.py '<JSON parameters>'", file=sys.stderr)
print(
'Example: amazon_policy_feed.py \'{"site": "US", "pageSize": 20}\'',
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())