tikhub-xiaohongshu-search

name	tikhub-xiaohongshu-search
description	Lightweight TikHub Xiaohongshu image-search workflow. Prioritizes single-request usage with curl or minimal Python, saves raw API JSON by default, and includes a small stdlib post-processor for CSV and simplified JSON. Use when the user wants Xiaohongshu keyword image search, page-based pagination, or structured note/image metadata from TikHub without a heavy wrapper.

TikHub Xiaohongshu Search

What this skill gives you

This skill is optimized for the common case: one keyword search request.

It provides:

1. Minimal request patterns

   • curl for quickest validation
   • tiny httpx example for people who prefer Python

2. Raw JSON saving

   • save the full TikHub response after each request
   • useful for audit, replay, and later post-processing

3. One optional post-processor

   • postprocess_xiaohongshu_raw.py
   • reads one raw file or a directory of raw files
   • writes xiaohongshu_search_summary.csv and xiaohongshu_search_summary.json

4. Optional pagination guidance

   • enough information for later page turning
   • intentionally brief, not the main path

Does not import TikHub-Multi-Functional-Downloader or any other project package.

API key requirement

This skill intentionally does not contain any API key.

Use one of these:

• environment variable: TIKHUB_API_KEY
• ask the user to provide an API key explicitly

If the key is missing, stop and ask for it instead of hardcoding one into scripts.

Install

pip install httpx

Post-processor: no extra packages.

API (for reference)

• Image search: GET https://api.tikhub.io/api/v1/xiaohongshu/app_v2/search_images?keyword=...&page=1&source=explore_feed
• Header: Authorization: Bearer <API_KEY>

Notes from real requests

• In curl, Chinese keywords should be URL-encoded. Directly putting 壁纸 into the query caused 400, while %E5%A3%81%E7%BA%B8 succeeded.
• A working minimal first-page request was: keyword=%E5%A3%81%E7%BA%B8&page=1&source=explore_feed
• The first-page response returns pagination context: search_id, search_session_id, word_request_id, and next_page
• Search results are in: data.data.items
• Useful nested sections include: image_info, note_info, share_info, and user_info

---

Preferred path: single request

1. Quickest: curl

First page:

curl --location --request GET "https://api.tikhub.io/api/v1/xiaohongshu/app_v2/search_images?keyword=%E5%A3%81%E7%BA%B8&page=1&source=explore_feed" \
--header "Authorization: Bearer $TIKHUB_API_KEY"

Another keyword example:

curl --location --request GET "https://api.tikhub.io/api/v1/xiaohongshu/app_v2/search_images?keyword=%E6%B2%BB%E6%84%88%E7%B3%BB&page=1&source=explore_feed" \
--header "Authorization: Bearer $TIKHUB_API_KEY"

2. Preferred Python pattern: tiny httpx

If the user wants Python, prefer a small request snippet, not a framework.

Search and save raw JSON:

import json
import os
import urllib.parse
import httpx

api_key = os.getenv("TIKHUB_API_KEY", "").strip()
if not api_key:
    raise SystemExit("Missing TIKHUB_API_KEY")

keyword = "壁纸"
url = "https://api.tikhub.io/api/v1/xiaohongshu/app_v2/search_images"
params = {
    "keyword": keyword,
    "page": 1,
    "source": "explore_feed",
}
headers = {"Authorization": f"Bearer {api_key}", "Accept": "*/*"}

with httpx.Client(timeout=30.0, follow_redirects=True) as client:
    raw = client.get(url, params=params, headers=headers).json()

safe_keyword = urllib.parse.quote(keyword, safe="")
with open(f"xiaohongshu_search_{safe_keyword}.json", "w", encoding="utf-8") as f:
    json.dump(raw, f, ensure_ascii=False, indent=2)

items = raw.get("data", {}).get("data", {}).get("items", [])
for item in items[:5]:
    note = item.get("note_info", {})
    share = item.get("share_info", {})
    user = item.get("user_info", {})
    print(note.get("title", ))
    (share.get(, ))
    (user.get(, ))

---

Save raw JSON by default

For this workflow, the recommended default is:

1. request the API
2. save the full raw JSON immediately
3. print only a few useful fields for quick inspection
4. optionally run the post-processor later

Suggested file naming:

• first page raw: search_<keyword>_page1_<request_id>.json
• next page raw: search_<keyword>_page2_<request_id>.json

If request_id is unavailable, hash the keyword plus page number.

---

Pagination

Only care about this if the user wants page 2 or beyond.

From the first response, keep these fields:

• search_id
• search_session_id
• word_request_id
• next_page

Then use them in the next request:

curl --location --request GET "https://api.tikhub.io/api/v1/xiaohongshu/app_v2/search_images?keyword=%E5%A3%81%E7%BA%B8&page=2&search_id=<search_id>&search_session_id=<search_session_id>&word_request_id=<word_request_id>&source=explore_feed" \
--header "Authorization: Bearer $TIKHUB_API_KEY"

If the endpoint behavior changes, trust the latest response fields over assumptions.

---

Post-process raw JSON

Save as postprocess_xiaohongshu_raw.py (stdlib only).

Input:

• one raw search JSON file
• or a directory containing multiple raw JSON files

Output:

• xiaohongshu_search_summary.csv
• xiaohongshu_search_summary.json

#!/usr/bin/env python3
from __future__ import annotations

import argparse
import csv
import json
import os
import sys
from glob import glob
from typing import Any, Dict, List


def collect_inputs(path: str) -> List[str]:
    if os.path.isfile(path):
        return [path]
    if os.path.isdir(path):
        return sorted(glob(os.path.join(path, "*.json")))
    raise FileNotFoundError(path)


def as_list(value: Any) -> List[dict]:
    return value if isinstance(value, list) else []


def flatten_for_csv(row: Dict[str, Any]) -> Dict[str, Any]:
    out: Dict[str, Any] = {}
    for k, v in row.items():
        if v  :
            out[k] = 
         (v, (, )):
            out[k] = json.dumps(v, ensure_ascii=)
        :
            out[k] = v
     out


 () -> [, ]:
    outer = raw.get()  {}
    inner = outer.get()  {}
    items = as_list(inner.get())
    first = items[]  items  {}
    note = first.get()  {}
    share = first.get()  {}
    user = first.get()  {}
    image = first.get()  {}
     {
        : os.path.basename(source_file),
        : raw.get(),
        : raw.get(),
        : raw.get(),
        : (raw.get()  {}).get(, ),
        : inner.get(),
        : inner.get(),
        : inner.get(, ),
        : inner.get(, ),
        : inner.get(, ),
        : (items),
        : note.get(, ),
        : note.get(, ),
        : note.get(, ),
        : note.get(),
        : note.get(),
        : note.get(),
        : share.get(, ),
        : user.get(, ),
        : user.get(, ),
        : image.get(, ),
        : image.get(, ),
    }


 () -> :
    ap = argparse.ArgumentParser(description=)
    ap.add_argument(, , required=, =)
    ap.add_argument(, , default=, =)
    args = ap.parse_args()

    :
        files = collect_inputs(args.)
     FileNotFoundError  e:
        (, e, file=sys.stderr)
         

      files:
        (, file=sys.stderr)
         

    out_dir = os.path.abspath(args.out_dir)
    os.makedirs(out_dir, exist_ok=)
    csv_path = os.path.join(out_dir, )
    json_path = os.path.join(out_dir, )

    rows: [[, ]] = []
     fp  files:
        :
             (fp, , encoding=)  f:
                raw = json.load(f)
         Exception  ex:
            rows.append({: os.path.basename(fp), : })
            
        rows.append(simplify_raw(raw, fp))

     (json_path, , encoding=)  f:
        json.dump(
            {
                : os.path.abspath(args.),
                : (rows),
                : rows,
            },
            f,
            ensure_ascii=,
            indent=,
        )

    flat = [flatten_for_csv(r)  r  rows]
    fieldnames = ({k  row  flat  k  row.keys()})
     (csv_path, , encoding=, newline=)  f:
        writer = csv.DictWriter(f, fieldnames=fieldnames, extrasaction=)
        writer.writeheader()
         row  flat:
            writer.writerow({k: row.get(k, )  k  fieldnames})

    (, csv_path)
    (, json_path)
     


 __name__ == :
     SystemExit(main())

Commands:

python postprocess_xiaohongshu_raw.py --input ./xiaohongshu_raw
python postprocess_xiaohongshu_raw.py --input ./search_%E5%A3%81%E7%BA%B8_page1.json --out-dir .

---

Optional: multiple pages or multiple keywords

Only use this when the user clearly needs:

• multiple keywords
• page 2+
• bulk result collection

Keep the batching layer thin:

1. accept a list of keywords
2. request page 1 first
3. store the returned pagination fields
4. fetch more pages only if needed
5. save one raw JSON per request
6. reuse postprocess_xiaohongshu_raw.py afterward

Recommended limits:

• start sequentially or with max_workers=2 to 3
• reduce concurrency if you hit 429
• avoid assuming pagination tokens are reusable across different keywords

Do not lead with a big wrapper if the task is only one keyword search.

---

End-to-end workflow

1. Provide TIKHUB_API_KEY.
2. Make a single image-search request with curl or a tiny httpx snippet.
3. Save the full raw response JSON.
4. Inspect a few important fields directly.
5. If needed, run postprocess_xiaohongshu_raw.py on one file or a directory of raw files.
6. Only then expand to page 2+ or multiple keywords.

Troubleshooting

• 401/403: invalid API key or missing Xiaohongshu scopes.
• 400 with Chinese keyword in curl: URL-encode the keyword.
• No items: keyword too narrow, source changed, or upstream result shape changed.
• 429: rate limit; retry later or reduce concurrency.
• Page 2 fails: confirm you passed the latest search_id, search_session_id, and word_request_id from the prior response.

What this skill does not cover

• note detail endpoints
• note comment crawling
• downloading all images from every note as a batch export
• non-search Xiaohongshu workflows

---