Amazon Best Sellers Finder
browser-act/skills
Extracts structured Amazon product data for a keyword and marketplace through the BrowserAct API, including titles, prices, ratings, reviews, sales volume and promotions.
Cross-border e-commerce AI Agent with 41 specialized tools for Amazon/TikTok/eBay/Walmart product research, competitor analysis, keyword tracking, review insights, patent detection, trend analysis…
$ npx skills add LeoYeAI/openclaw-master-skills --skill linkfoxagent -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills linkfoxagent --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/linkfoxagent .claude/skills/linkfoxagent && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "linkfoxagent" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/linkfoxagent into .claude/skills/linkfoxagent/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "linkfoxagent", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/linkfoxagentType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add LeoYeAI/openclaw-master-skills --skill linkfoxagent -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills linkfoxagent --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/linkfoxagent .agents/skills/linkfoxagent && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "linkfoxagent" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/linkfoxagent into .agents/skills/linkfoxagent/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "linkfoxagent", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add LeoYeAI/openclaw-master-skills --skill linkfoxagent -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills linkfoxagent --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/linkfoxagent .cursor/skills/linkfoxagent && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "linkfoxagent" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/linkfoxagent into .cursor/skills/linkfoxagent/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "linkfoxagent", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/LeoYeAI/openclaw-master-skills.git --path skills/linkfoxagent--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add LeoYeAI/openclaw-master-skills --skill linkfoxagent -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills linkfoxagent --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/linkfoxagent .gemini/skills/linkfoxagent && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "linkfoxagent" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/linkfoxagent into .gemini/skills/linkfoxagent/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "linkfoxagent", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install LeoYeAI/openclaw-master-skills linkfoxagentInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add LeoYeAI/openclaw-master-skills --skill linkfoxagent -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/linkfoxagent .github/skills/linkfoxagent && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "linkfoxagent" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/linkfoxagent into .github/skills/linkfoxagent/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "linkfoxagent", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add LeoYeAI/openclaw-master-skills --skill linkfoxagent -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills linkfoxagent --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/linkfoxagent .opencode/skills/linkfoxagent && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "linkfoxagent" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/linkfoxagent into .opencode/skills/linkfoxagent/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "linkfoxagent", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
linkfoxagentCross-border e-commerce AI Agent with 41 specialized tools for Amazon/TikTok/eBay/Walmart product research, competitor analysis, keyword tracking, review insights, patent detection, trend analysis…
Linkfoxagent is an agent skill from LeoYeAI/openclaw-master-skills. Cross-border e-commerce AI Agent with 41 specialized tools for Amazon/TikTok/eBay/Walmart product research, competitor analysis, keyword tracking, review insights, patent detection, trend analysis, 1688 sourcing, and AI image generation. Use when: (1) product selection and market analysis, (2) competitor research and ASIN lookup, (3) keyword and traffic analysis, (4) review mining and consumer insights, (5) patent/trademark/copyright detection, (6) Google/TikTok trend research, (7) 1688 supplier sourcing, (8)…
Its SKILL.md is about 3.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 18 other files, including scripts and reference files (for example `_meta.json`, `references/1688.md` and `references/ai-tools.md`).
It sits in Legal & Compliance, covering Intellectual property, Competitor analysis and Image generation. It works with TikTok and 1688. The repository describes itself as: 🧠 Curated collection of 1209+ best OpenClaw skills — weekly updated by MyClaw.ai. The licence is MIT.
2 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit e5199b5. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Ships 1 file in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
python3From the folder's file list and the shell code blocks in SKILL.md.
Hosts in commands or code, which the agent is likely to contact:
yxgb3sicy7.feishu.cnFrom URLs in SKILL.md, links to its own repository left out.
Names these keys or tokens, usually read from environment variables:
LINKFOXAGENT_API_KEYFrom names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Linkfoxagent loads about 3.8k tokens when it runs, and up to ~25k if it reads all its reference files. Until then it costs about 180 tokens; SKILL.md has 988 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
The full file from LeoYeAI/openclaw-master-skills at commit e5199b5, republished under its MIT licence (© LeoYeAI). 988 words, ~3,814 tokens.
.claude/skills/linkfoxagent/SKILL.md (or your agent's skills folder). This skill also uses 16 other files; get the full folder from GitHub.LinkFoxAgent is a specialized AI agent for cross-border e-commerce with 42 built-in tools covering product research, competitor analysis, keyword tracking, review insights, patent detection, AI image generation, and more.
export LINKFOXAGENT_API_KEY=your-key-hereNEVER call linkfox.py directly from the main session. LinkFoxAgent tasks take 1-5 minutes. You MUST use sessions_spawn to dispatch every task to a sub-agent. This keeps the main session responsive and delivers results automatically when done.
Before calling sessions_spawn, tell the user in the main session:
「正在向 LinkFox Agent 提交任务,请稍候(通常需要 1-5 分钟)...」
Then dispatch the sub-agent:
sessions_spawn:
task: |
Run the following LinkFoxAgent task and report the results back.
Command (use heredoc to avoid shell injection):
python3 <skill>/scripts/linkfox.py --wait --timeout 600 --stdin <<'__LINKFOX_TASK_END__'
<TASK_PROMPT>
__LINKFOX_TASK_END__
The script prints to stderr: "Task submitted. messageId: <id>" if submission succeeds,
or an error message and exits with code 1 if submission fails.
After running the command, follow these rules strictly:
## If the command exits with a non-zero code OR stderr contains "Error" before any messageId:
- The task submission FAILED. Report back:
「任务发起失败。请检查 LINKFOXAGENT_API_KEY 是否已正确配置:
1. 确认环境变量已设置:export LINKFOXAGENT_API_KEY=your-key-here
2. 获取 API Key:https://yxgb3sicy7.feishu.cn/wiki/IlkawdQP9ifKv9k22xcc7rjmnkb
3. 重启 OpenClaw 网关使环境变量生效
错误详情:<stderr 内容>」
## If stderr contains "Task submitted. messageId: <id>":
- Submission SUCCEEDED. Do NOT send any intermediate message — the main agent has already told the user the task is dispatched. Wait silently for the command to finish (stdout).
## After the command completes (stdout):
1. Parse stdout — it contains a status line, an optional ShareURL, a reflection summary, and result entries.
2. If status is "error" or "cancel", report the error clearly.
3. If status is "finished", summarize the reflection and list all results.
4. HTML report URLs in results are available for your reference. Decide autonomously whether to share them with the user based on context — do not forward them blindly.
5. **ShareURL:** If the output contains a line `ShareURL: <url>`, always forward it to the user verbatim. This is the full conversation share link for this LinkFoxAgent run — the user can open it to review the complete execution process and download all related files from that page.
6. **CSV output (JSON results with columns):** When a result line says `CSV saved to: <path>`, the script has already converted the JSON data to a CSV file with Chinese column headers at that local path. Report the path to the user. Do NOT attempt to read or display the CSV contents unless the user explicitly asks. If the user wants to receive the file, send it using the file-sending skill.
label: "LinkFox: <short description>"
mode: "run"
runTimeoutSeconds: 600
cleanup: "keep"When the user's request involves multiple independent lookups (e.g., "search both Amazon US and Amazon JP"), spawn one sub-agent per task in parallel.
Before spawning, tell the user:
「正在同时向 LinkFox Agent 提交 N 个任务,请稍候...」
# Sub-agent 1
sessions_spawn:
task: |
Run (use heredoc to avoid shell injection):
python3 <skill>/scripts/linkfox.py --wait --timeout 600 --stdin <<'__LINKFOX_TASK_END__'
<task A>
__LINKFOX_TASK_END__
Apply the same submission success/failure reporting rules as the single-task template above.
label: "LinkFox: task A"
mode: "run"
runTimeoutSeconds: 600
# Sub-agent 2
sessions_spawn:
task: |
Run (use heredoc to avoid shell injection):
python3 <skill>/scripts/linkfox.py --wait --timeout 600 --stdin <<'__LINKFOX_TASK_END__'
<task B>
__LINKFOX_TASK_END__
Apply the same submission success/failure reporting rules as the single-task template above.
label: "LinkFox: task B"
mode: "run"
runTimeoutSeconds: 600When the user's request requires multiple sequential LinkFoxAgent calls (e.g., fetch data from two platforms then merge), follow this pattern:
sessions_spawn, one after another (or in parallel if independent). Collect the CSV paths returned by each.sessions_spawn to process or merge the CSVs using Python. Pass the absolute CSV paths as arguments.# Final merge/processing step — spawned after all data tasks complete
sessions_spawn:
task: |
Run the following Python script to process/merge the CSV files and report results.
python3 - <<'PYEOF'
import csv, sys, os
# Paths passed in from the data tasks above
csv_paths = [
"/absolute/path/to/result_1_xxx.csv",
"/absolute/path/to/result_2_yyy.csv",
]
# TODO: implement merge / analysis logic here
# Example: read all rows and write a combined CSV
all_rows = []
headers = None
for path in csv_paths:
with open(path, encoding="utf-8-sig") as f:
reader = csv.DictReader(f)
if headers is None:
headers = reader.fieldnames
for row in reader:
all_rows.append(row)
out_path = os.path.join(os.path.dirname(csv_paths[0]), "merged_output.csv")
with open(out_path, "w", newline="", encoding="utf-8-sig") as f:
writer = csv.DictWriter(f, fieldnames=headers)
writer.writeheader()
writer.writerows(all_rows)
print(f"Merged CSV saved to: {out_path}")
PYEOF
Report the output path back to the user. If the user wants the file, send it using the file-sending skill.
label: "LinkFox: merge/process CSVs"
mode: "run"
runTimeoutSeconds: 120
cleanup: "keep"sessions_spawn creates an isolated sub-agent sessionlinkfox.py --wait which blocks until the task finishes# The sub-agent uses --wait + --stdin mode (heredoc avoids shell injection)
python3 <skill>/scripts/linkfox.py --wait --stdin <<'__LINKFOX_TASK_END__'
task description here
__LINKFOX_TASK_END__
# Custom timeout (default 300s)
python3 <skill>/scripts/linkfox.py --wait --timeout 600 --stdin <<'__LINKFOX_TASK_END__'
task description here
__LINKFOX_TASK_END__
# JSON output for structured parsing
python3 <skill>/scripts/linkfox.py --wait --format json --stdin <<'__LINKFOX_TASK_END__'
task description here
__LINKFOX_TASK_END__Use @工具中文名 to invoke tools. Multiple tools can be chained in a single task (max 10).
Example: @卖家精灵-选产品 筛选亚马逊美国站的 "usb charger cable",返回前40条商品数据
Tool parameters may have maximum, minimum, and pattern constraints. Prompts must respect these or the call will fail. See the reference files below for details.
Chain multiple tools in numbered steps. LinkFoxAgent handles data flow between steps:
1、@亚马逊前端搜索模拟 帮我在美国亚马逊站搜索 "computer desk",返回前2页商品数据
2、@对商品标题进行分词 统计上一步商品标题中出现的功能点
3、按功能点统计月销量、月销售额、asin数When the user does not specify a tool, follow these rules:
Querying Amazon product data — all three tools are fast; choose by use case:
Aggregating / statistics (e.g., group by brand, price tier, sales rank):
| Classification | Tool Name | Use For |
|---|---|---|
| Keepa | @Keepa-亚马逊-商品搜索 | Product filtering by keywords, BSR, price, sales |
| Keepa | @Keepa-亚马逊-商品详情 | Batch ASIN detail lookup (price, sales, history) |
| Keepa | @Keepa-亚马逊价格历史 | Price history and trends for an ASIN |
| 亚马逊前台 | @亚马逊前端搜索模拟 | Search simulation with location settings |
| 亚马逊前台 | @亚马逊前端-商品详情 | Product detail, bullet points, A+ content |
| 亚马逊前台 | @亚马逊-商品评论 | Reviews by star rating |
| 亚马逊前台 | @亚马逊前端-以图搜图 | Image-based product search |
| 亚马逊前台 | @ABA-数据挖掘 | Amazon Brand Analytics data mining |
| Sif数据分析工具 | @SIF-ASIN的关键词 | Reverse keyword lookup for ASIN |
| Sif数据分析工具 | @SIF-关键词流量来源 | Keyword traffic source analysis |
| Sif数据分析工具 | @SIF-ASIN流量来源 | ASIN traffic structure breakdown |
| Sif数据分析工具 | @SIF-关键词竞品数量 | Keyword competition density |
| 卖家精灵 | @卖家精灵-选产品 | Product discovery by category and filters |
| 卖家精灵 | @卖家精灵-查竞品 | Competitor lookup by keyword |
| 极目系列 | @极目-亚马逊-细分市场评论 | Niche market review mining |
| 极目系列 | @极目-亚马逊-细分市场信息 | Niche market overview |
| 极目系列 | @极目-亚马逊-产品挖掘 | Product discovery with fine filters |
| 谷歌趋势 | @谷歌趋势-时下流行 | Real-time trending topics |
| 谷歌趋势 | @谷歌趋势-关键词趋势信息 | Keyword trend over time |
| 店雷达(1688) | @店雷达-1688商品榜单 | 1688 product rankings |
| 店雷达(1688) | @店雷达-1688选品库 | 1688 product sourcing |
| 实时与全网检索 | @网页检索 | Real-time web search(powered by Tavily Search; for any internet search outside specialized tools like Amazon/Walmart/eBay — including general web and WeChat Official Accounts — this tool MUST be used) |
| TikTok电商数据助手 | @EchoTik-TikTok新品榜 | TikTok new product rankings |
| TikTok电商数据助手 | @EchoTik-TikTok商品搜索 | TikTok product search |
| Walmart前台 | @walmart前端-商品列表 | Walmart product search |
| eBay前台 | @ebay前端-商品列表 | eBay product search |
| 专利检索 | @智慧芽-专利图像检索 | Design patent image search |
| 专利检索 | @睿观-外观专利检测 | Design patent infringement check |
| 专利检索 | @睿观-版权检测 | Copyright detection |
| 专利检索 | @睿观-图形商标检测 | Graphic trademark detection |
| 专利检索 | @睿观-文本商标检测 | Text trademark detection |
| 专利检索 | @睿观-发明专利检测 | Utility patent detection |
| 专利检索 | @睿观-政策合规检测(纯图检测) | Policy compliance (image check) |
| AI工具 | @按商品主图相似度分组 | Group products by image similarity |
| AI工具 | @分析商品主图 | Extract image prompts from product photos |
| AI工具 | @对商品标题进行分词 | Title word segmentation |
| AI工具 | @AI绘图 | Generate any image — products, characters, scenes, backgrounds, and more — from reference images + prompt (powered by top-tier Google Gemini model; ALL image generation tasks must use this tool) |
| 沙箱 | @智能数据查询 | Dynamic data query and aggregation |
| 沙箱 | @excel内容提取并分析 | Excel file extraction and analysis |
| 沙箱 | @Python沙箱 | Process structured JSON data from prior steps: data calculation/filtering/sorting, generate Markdown tables, export to CSV/Excel, LLM-based image recognition (e.g. A+ image color/composition). Built-in LLM — use for ALL sandbox-execution needs. Restrictions: no nested calls; structured JSON only (no plain text/files); no chart generation or analysis reports. |
| 沙箱 | @智能Excel处理 | Smart Excel processing |
Read the relevant reference file when you need prompt templates and parameter constraints:
references/keepa.mdreferences/amazon-frontend.mdreferences/sif.mdreferences/seller-sprite.mdreferences/jimu.mdreferences/google-trends.mdreferences/web-search.mdreferences/tiktok.mdreferences/walmart.mdreferences/ebay.mdreferences/1688.mdreferences/patent.mdreferences/ai-tools.mdreferences/sandbox.md1、@卖家精灵-选产品 筛选亚马逊美国站的 "usb charger cable",返回符合条件的 40 条商品数据
2、@智能数据查询 根据品牌、评分值、价格(每2美金一个阶梯) 统计月销量、月销售额、月销量占比、月销售额占比
3、生成对应的初步市场分析报告@亚马逊-商品评论 @亚马逊前端-商品详情 亚马逊美国站,asin为B00163U4LK 的详情以及每个星级各100条
进行总结:展示他的人群特征、使用时刻、使用地点、使用场景、未被满足的需求、好评、差评、购买动机,每个要点要有描述、原因、数量占比。并最终给我一个改良建议努力思考,选择适合以下场景的工具,完美完成以下任务:
亚马逊美国站,asin为:B0FPZHSLYR、B0CP9Z56SW、B0FFNF9TK1、B0FS7DRCLZ、B0CP9WRDFV、B0BWMZDCCN,我的竞品就是这些,你参考他们的五点描述和A+页面内容,生成我的商品的标题、五点描述
步骤:
1)查询以上所有asin的商品详情
2)查询每个asin的关键词
3)将上一步的全部关键词,构建关键词价值打分表
4)写作前再次查询亚马逊五点描述的写作要求和Amazon cosmo算法和经典营销理论FABE法则
5)生成5点描述,要求竞品的品牌词不能作为关键词,写出符合FABE法则和最新Amazon cosmo算法的五点描述,并且将关键词价值打分表价值高的词埋入1、@亚马逊前台模拟搜索工具 筛选亚马逊美国站的,关键词为necklaces for women,默认排序,第一页的商品
2、对上一步的商品主体,统计主图不同挂件形状的销售额,绘制出不同形状的销售额占比
3、进行总结:把步骤二的数据完整的用精美的html网页显示给我看(不要精简)1、@亚马逊前端搜索模拟 帮我在美国亚马逊站,以"computer desk"为关键词进行搜索,同时将配送地址设置为洛杉矶,最终返回搜索结果前2页的商品数据
2、@对商品标题进行分词 统计上一步商品标题中出现的功能点
3、按功能点统计月销量、月销售额、asin数If a tool call fails, the response includes error details. Retry with adjusted parameters based on the error message. Common issues:
© LeoYeAI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 16 other files (scripts, references) in skills/linkfoxagent of LeoYeAI/openclaw-master-skills.
Open the folder on GitHubat commit e5199b5
Linkfoxagent next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Linkfoxagent this skillLeoYeAI/openclaw-master-skills | 2.2k | — | ~3.8k | Automated safety check: Pass | MIT | |
| Amazon Best Sellers Finderbrowser-act/skills | 6.1k | 2 repos | ~1.5k | Automated safety check: Pass | MIT | |
| Memstack Product Feedback Analyzercwinvestments/memstack | 423 | — | ~2.7k | Automated safety check: Pass | Proprietary | |
| Product Description Generatornexscope-ai/eCommerce-Skills | 1.1k | — | ~3.3k | Automated safety check: Pass | MIT | |
| Tiktok Shop Cross Bordernexscope-ai/eCommerce-Skills | 1.1k | — | ~2.5k | Automated safety check: Pass | MIT | |
| Amazon Product Search Extractorbrowser-act/skills | 6.1k | 2 repos | ~1.5k | Automated safety check: Pass | MIT |
browser-act/skills
Extracts structured Amazon product data for a keyword and marketplace through the BrowserAct API, including titles, prices, ratings, reviews, sales volume and promotions.
cwinvestments/memstack
A skill your agent uses when the user says 'analyze feedback', 'feedback analysis', 'what are customers asking for', or has support tickets, reviews, or survey data to categorize, score, and…
nexscope-ai/eCommerce-Skills
E-commerce product description generator for any platform. An agent skill from nexscope-ai/eCommerce-Skills.
nexscope-ai/eCommerce-Skills
Cross-border selling on TikTok Shop. An agent skill from nexscope-ai/eCommerce-Skills.
browser-act/skills
Pulls structured Amazon search results (titles, ASINs, prices, ratings, specifications) for a keyword and brand through BrowserAct's Amazon Product API template.
browser-act/skills
Collects structured product data from Amazon search results for a keyword and optional brand, using a BrowserAct script, for market and competitor research.
LeoYeAI/openclaw-master-skills
Manages pipelines on a DevOps quality and efficiency platform through its OpenAPI: list workspaces and templates, create, update, run and cancel pipelines, and read run records.
LeoYeAI/openclaw-master-skills
Patches OpenClaw's Feishu extension so an edited document triggers an isolated agent session that reads the doc and replies inline, turning it into a live chat space.
LeoYeAI/openclaw-master-skills
Multi-context memory management system for OpenClaw agents with group-isolated storage, global shared memory, workspace organization, and group-specific skills isolation.
LeoYeAI/openclaw-master-skills
Runs a brand's AI-search visibility work end to end: diagnosing how AI platforms represent it, repositioning it, producing AI-optimized content and monitoring ongoing mentions.
LeoYeAI/openclaw-master-skills
Installs and authenticates the gws CLI, then automates Gmail, Drive, Sheets, Calendar, Docs, Chat and Tasks with ready-made recipes, persona bundles and security audits.
LeoYeAI/openclaw-master-skills
Runs four advisor roles, a fitness coach, nutritionist, data analyst and TCM practitioner, to build a health profile and track workouts, diet and wellness over time.
Cross-border e-commerce AI Agent with 41 specialized tools for Amazon/TikTok/eBay/Walmart product research, competitor analysis, keyword tracking, review insights, patent detection, trend analysis…. Linkfoxagent is an agent skill from LeoYeAI/openclaw-master-skills. Cross-border e-commerce AI Agent with 41 specialized tools for Amazon/TikTok/eBay/Walmart product research, competitor analysis, keyword tracking, review insights, patent detection, trend analysis, 1688 sourcing, and AI image generation.
Linkfoxagent fits situations like: product selection and market analysis; competitor research and ASIN lookup; keyword and traffic analysis; review mining and consumer insights.
Run `npx skills add LeoYeAI/openclaw-master-skills --skill linkfoxagent -a claude-code`. Or copy the skill folder (skills/linkfoxagent in LeoYeAI/openclaw-master-skills) into .claude/skills/linkfoxagent in your project. Claude Code loads it when a task matches its description.
Run `npx skills add LeoYeAI/openclaw-master-skills --skill linkfoxagent -a codex`. Or copy the skill folder (skills/linkfoxagent in LeoYeAI/openclaw-master-skills) into .agents/skills/linkfoxagent in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add LeoYeAI/openclaw-master-skills --skill linkfoxagent -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/linkfoxagent, .gemini/skills/linkfoxagent, .github/skills/linkfoxagent and .opencode/skills/linkfoxagent in your project.
Going by SKILL.md and its folder, Linkfoxagent needs Python for the scripts in its folder, the command-line tools its instructions call (python3) and credentials named LINKFOXAGENT_API_KEY. Our summary lists: Python 3; A credential in LINKFOXAGENT_API_KEY.
SKILL.md names 1 domain. In commands or code: yxgb3sicy7.feishu.cn; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
Linkfoxagent is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 3.8k tokens (SKILL.md is roughly 15k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 22k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Linkfoxagent: Amazon Best Sellers Finder (browser-act/skills, 6.1k stars), Memstack Product Feedback Analyzer (cwinvestments/memstack, 423 stars), Product Description Generator (nexscope-ai/eCommerce-Skills, 1.1k stars) and Tiktok Shop Cross Border (nexscope-ai/eCommerce-Skills, 1.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
LeoYeAI (a GitHub user) maintains it in LeoYeAI/openclaw-master-skills, which has 2,159 GitHub stars. The repository holds 972 skills in this directory. The repository was last updated on July 20, 2026.
Source: LeoYeAI/openclaw-master-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.