Fastmoss Rpa

fastmoss-rpa (unified)

One skill, seven boards. This consolidates the previous seven sister skills (fastmoss-products, fastmoss-creators, fastmoss-shops, fastmoss-ads, fastmoss-creatives, fastmoss-livestreams, fastmoss-market) into a single entry point. The scraping logic lives in one generic engine (scripts/core.py); all board differences are data in scripts/sections.py. The old .claude/skills/fastmoss-* directories are kept as backup and are not deleted.

Architecture

fastmoss_rpa.py            # single CLI: scrape / filter / analyze / market
  ├─ core.py           # generic paginating engine (fixed + dynamic parse modes)
  ├─ market_api.py     # FastMoss JSON-API fetchers (page-context fetch)
  ├─ analyze.py        # 7 report engines, dispatched by --section
  ├─ sections.py       # CONFIG: URLs, parsers, fields, filter dims (no logic to rewrite)
  ├─ bridge_browserskill.py  # transport: call / evaluate / session_stop
  └─ references/
        ├─ api_notes.md      # FastMoss API contract (market)
        └─ reports/<section>.md  # 7 Markdown report templates

Prerequisites

• BrowserSkill (bsk) installed and connected; the BrowserSkill extension is installed in Chrome/Edge and the user is logged into fastmoss.com. Verify with bsk status → browsers connected: N (N ≥ 1).
• No local HTTP daemon — bsk drives the real browser and reuses the login session (cookies + origin), which is why the market API calls work.

Full environment/shell quirks are in references/environment.md.

Critical gotchas (each cost real time on first run)

1. Never click via @e snapshot refs — they go stale after every navigation. All bundled clicks use text-label evaluate JS.
2. Bash heredoc + JS regex breaks (backslash layer eaten). Prefer .split('\n').
3. SPA hydration lag — wait ≥5 s after navigate, ≥3.5 s between pages. Too fast → 0 rows. (--nav-sleep / --page-sleep tune this.)
4. Python encoding on Windows — always read produced CSVs with encoding='utf-8-sig'.
5. Empty filler top row on page 1 — parsers drop rows whose key field is empty.

Boards at a glance

Country label vocabulary (16 markets)

Used for --country filter clicks (Chinese labels FastMoss displays):

美国 印度尼西亚 英国 越南 泰国 马来西亚 菲律宾 西班牙
墨西哥 德国 法国 意大利 巴西 日本 新加坡 沙特

market uses region codes instead: US ID GB VN TH MY PH ES MX DE FR IT BR JP SG SA (see references/api_notes.md).

Commands

All commands run from the skill's scripts/ directory (or with that dir on PYTHONPATH). Replace <out> with any path; parent dirs are created.

1) scrape — one ranking, no filter

# Products (fixed schema, no --ranking needed)
python fastmoss_rpa.py scrape --section products --pages 5 --out <out>/products.csv

# Dynamic boards need --ranking
python fastmoss_rpa.py scrape --section creators   --ranking fans          --pages 5 --out <out>/fans.csv
python fastmoss_rpa.py scrape --section shops      --ranking sales         --pages 5 --out <out>/sales.csv
python fastmoss_rpa.py scrape --section ads        --ranking keyword       --pages 5 --out <out>/keywords.csv
python fastmoss_rpa.py scrape --section creatives  --ranking video         --pages 5 --out <out>/videos.csv
python fastmoss_rpa.py scrape --section livestreams--ranking tiktok         --pages 5 --out <out>/tiktok.csv

# Custom URL (overrides --ranking)
python fastmoss_rpa.py scrape --section creators --url "https://www.fastmoss.com/zh/influencer/tiktok/fans" --out <out>/x.csv

2) filter — with country / category / time filter

Products supports one of country / category / shop_type per run and writes a per-label CSV + a combined CSV:

python fastmoss_rpa.py filter --section products --category "美妆个护,女装与女士内衣" --pages 3 --out <out>/by_category.csv
python fastmoss_rpa.py filter --section products --country 美国,印度尼西亚 --pages 3 --out <out>/by_country.csv
python fastmoss_rpa.py filter --section products --shop-type 跨境店 --pages 3 --out <out>/by_shop_type.csv

Dynamic boards support --country (and creators also --time, e.g. 周榜):

python fastmoss_rpa.py filter --section creators --ranking fans --country 美国,印度尼西亚,泰国,马来西亚 --pages 3 --out <out>/fans_by_country.csv
python fastmoss_rpa.py filter --section creators --ranking commerceTop --country 美国 --time 周榜 --pages 3 --out <out>/commerce_us_weekly.csv
python fastmoss_rpa.py filter --section shops    --ranking sales --country 美国,泰国 --pages 3 --out <out>/sales_by_country.csv
python fastmoss_rpa.py filter --section ads      --ranking keyword --country 美国,印度尼西亚 --pages 3 --out <out>/keywords_by_country.csv

3) analyze — multi-dimensional Markdown report

# Products (top50 + optional by-country / by-category / per-shop)
python fastmoss_rpa.py analyze products \
    --top50 <out>/top50.csv --by-country <out>/by_country.csv \
    --by-category <out>/by_category.csv --shop <out>/shop_X.csv \
    --out-md <report>/products_report.md

# Creators / shops / ads / creatives / livestreams
python fastmoss_rpa.py analyze creators --fans <out>/fans.csv --commerce <out>/commerce.csv \
    --blue-v <out>/blue-v.csv --popular <out>/popular.csv --horse <out>/horse.csv --out-md <report>/creators_report.md
python fastmoss_rpa.py analyze shops --sales <out>/sales.csv --hot <out>/hot.csv --out-md <report>/shops_report.md
python fastmoss_rpa.py analyze ads --tag <out>/tags.csv --keyword <out>/keywords.csv --category <out>/categories.csv --out-md <report>/ads_report.md
python fastmoss_rpa.py analyze creatives --video <out>/videos.csv --song <out>/songs.csv --hashtag <out>/hashtags.csv --out-md <report>/creatives_report.md
python fastmoss_rpa.py analyze livestreams --tiktok <out>/tiktok.csv --hot-product <out>/hot_product.csv --live-commerce <out>/live_commerce.csv --out-md <report>/livestreams_report.md

# Combine a multi-filter CSV via --filtered
python fastmoss_rpa.py analyze creators --filtered <out>/fans_by_country.csv --out-md <report>/creators_report.md

4) market — FastMoss category-distribution API

# Industry pattern (categoryDistribution) — multi-region comparison
python fastmoss_rpa.py market distribution --region US,ID,TH,MY --time month --out <out>/categories_by_region.csv
python fastmoss_rpa.py market distribution --region US --pcid 14 --time month --out <out>/us_beauty.csv

# Market overview (GoodCategory/base) + top products
python fastmoss_rpa.py market base --region US --out <out>/us_market_base.json --top-products-csv <out>/us_top_products.csv

# Daily sales time-series (GoodCategory/salesChart)
python fastmoss_rpa.py market sales-chart --region US --out <out>/us_sales_chart.csv

# Dump category/country vocabulary (pcid codes etc.)
python fastmoss_rpa.py market filter-info --out <out>/filter_info.json

# Then render the market report
python fastmoss_rpa.py analyze market --distribution <out>/categories_by_region.csv --out-md <report>/market_report.md

CSV schemas

• products: page, rank, product_name, price, listed_at, country, shop, shop_total_sales, category, commission, sales_period, gmv_period, total_sales, total_gmv
• creators: page, ranking, rank, creator_name, creator_id, creator_category, country + dynamic header columns
• shops: page, ranking, rank, shop_name, shop_legal_name, shop_category, shop_rating + dynamic header columns
• ads / creatives / livestreams: page, ranking, rank, entity_name + dynamic header columns
• filtered: adds filter (products) or filter_country (+ filter_time for creators) as the leading column(s).
• market: see references/api_notes.md for API field names.

The 7 report templates in references/reports/ are auto-filled by analyze.py — do not rename their {placeholder} tokens.

Migration note

The previous 7 skills under .claude/skills/fastmoss-*/ are preserved as a backup. This unified skill is the maintained entry point; once you've validated it against your workflows you may delete the old directories. Behavior is intended to be byte-for-byte identical for the CSV outputs.