Slm Web Access
qualixar/superlocalmemory
Set up and troubleshoot SuperLocalMemory Web access, the optional link that lets web apps (ChatGPT, Claude on the web, Muse, Composio, other remote MCP clients) use the memory on this computer.
Agent skill
Sealeap Tianlu Amazon AI Agent MCP Product Research
Configure an AI coding/agent tool with a third-party Amazon-data MCP connector to retrieve category, ASIN sales-structure, keyword, review, and price-band evidence, and drive a structured…
Install Sealeap Tianlu Amazon AI Agent MCP Product Research
$ npx skills add xjli360/sealeap-amazon-skills --skill sealeap-tianlu-amazon-ai-agent-mcp-product-research -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install xjli360/sealeap-amazon-skills sealeap-tianlu-amazon-ai-agent-mcp-product-research --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/xjli360/sealeap-amazon-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/amazon-skills/weixin/tianlu/sealeap-tianlu-amazon-ai-agent-mcp-product-research .claude/skills/sealeap-tianlu-amazon-ai-agent-mcp-product-research && 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 "sealeap-tianlu-amazon-ai-agent-mcp-product-research" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/weixin/tianlu/sealeap-tianlu-amazon-ai-agent-mcp-product-research into .claude/skills/sealeap-tianlu-amazon-ai-agent-mcp-product-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-tianlu-amazon-ai-agent-mcp-product-research", 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/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/weixin/tianlu/sealeap-tianlu-amazon-ai-agent-mcp-product-researchType 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 xjli360/sealeap-amazon-skills --skill sealeap-tianlu-amazon-ai-agent-mcp-product-research -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install xjli360/sealeap-amazon-skills sealeap-tianlu-amazon-ai-agent-mcp-product-research --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/xjli360/sealeap-amazon-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/amazon-skills/weixin/tianlu/sealeap-tianlu-amazon-ai-agent-mcp-product-research .agents/skills/sealeap-tianlu-amazon-ai-agent-mcp-product-research && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "sealeap-tianlu-amazon-ai-agent-mcp-product-research" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/weixin/tianlu/sealeap-tianlu-amazon-ai-agent-mcp-product-research into .agents/skills/sealeap-tianlu-amazon-ai-agent-mcp-product-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-tianlu-amazon-ai-agent-mcp-product-research", 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 xjli360/sealeap-amazon-skills --skill sealeap-tianlu-amazon-ai-agent-mcp-product-research -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install xjli360/sealeap-amazon-skills sealeap-tianlu-amazon-ai-agent-mcp-product-research --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/xjli360/sealeap-amazon-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/amazon-skills/weixin/tianlu/sealeap-tianlu-amazon-ai-agent-mcp-product-research .cursor/skills/sealeap-tianlu-amazon-ai-agent-mcp-product-research && 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 "sealeap-tianlu-amazon-ai-agent-mcp-product-research" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/weixin/tianlu/sealeap-tianlu-amazon-ai-agent-mcp-product-research into .cursor/skills/sealeap-tianlu-amazon-ai-agent-mcp-product-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-tianlu-amazon-ai-agent-mcp-product-research", 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/xjli360/sealeap-amazon-skills.git --path amazon-skills/weixin/tianlu/sealeap-tianlu-amazon-ai-agent-mcp-product-research--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 xjli360/sealeap-amazon-skills --skill sealeap-tianlu-amazon-ai-agent-mcp-product-research -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install xjli360/sealeap-amazon-skills sealeap-tianlu-amazon-ai-agent-mcp-product-research --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/xjli360/sealeap-amazon-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/amazon-skills/weixin/tianlu/sealeap-tianlu-amazon-ai-agent-mcp-product-research .gemini/skills/sealeap-tianlu-amazon-ai-agent-mcp-product-research && 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 "sealeap-tianlu-amazon-ai-agent-mcp-product-research" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/weixin/tianlu/sealeap-tianlu-amazon-ai-agent-mcp-product-research into .gemini/skills/sealeap-tianlu-amazon-ai-agent-mcp-product-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-tianlu-amazon-ai-agent-mcp-product-research", 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 xjli360/sealeap-amazon-skills sealeap-tianlu-amazon-ai-agent-mcp-product-researchInstalls 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 xjli360/sealeap-amazon-skills --skill sealeap-tianlu-amazon-ai-agent-mcp-product-research -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/xjli360/sealeap-amazon-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/amazon-skills/weixin/tianlu/sealeap-tianlu-amazon-ai-agent-mcp-product-research .github/skills/sealeap-tianlu-amazon-ai-agent-mcp-product-research && 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 "sealeap-tianlu-amazon-ai-agent-mcp-product-research" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/weixin/tianlu/sealeap-tianlu-amazon-ai-agent-mcp-product-research into .github/skills/sealeap-tianlu-amazon-ai-agent-mcp-product-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-tianlu-amazon-ai-agent-mcp-product-research", 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 xjli360/sealeap-amazon-skills --skill sealeap-tianlu-amazon-ai-agent-mcp-product-research -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install xjli360/sealeap-amazon-skills sealeap-tianlu-amazon-ai-agent-mcp-product-research --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/xjli360/sealeap-amazon-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/amazon-skills/weixin/tianlu/sealeap-tianlu-amazon-ai-agent-mcp-product-research .opencode/skills/sealeap-tianlu-amazon-ai-agent-mcp-product-research && 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 "sealeap-tianlu-amazon-ai-agent-mcp-product-research" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/weixin/tianlu/sealeap-tianlu-amazon-ai-agent-mcp-product-research into .opencode/skills/sealeap-tianlu-amazon-ai-agent-mcp-product-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-tianlu-amazon-ai-agent-mcp-product-research", 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.
Facts
- Skill name
sealeap-tianlu-amazon-ai-agent-mcp-product-research- GitHub stars
- 251
- Token cost
- ~756 tokens
- SKILL.md length
- 142 words
- Files
- 5 (incl. scripts, references)
- Skills in repo
- 179
- Repo updated
- First seen
- Licence
- MIT
At a glance
Configure an AI coding/agent tool with a third-party Amazon-data MCP connector to retrieve category, ASIN sales-structure, keyword, review, and price-band evidence, and drive a structured…
- Works in 4 steps: 诊断：读取现状、证据和缺口，不生成线上写入动作。 → 方案草案：输出可审核的结构、参数范围、实验和回退值。 → 执行准备：只生成待批准变更表或 API/控制台操作草案。 → …
- 配置AI选品工作流、用MCP连接器批量拉取选品数据、让Agent做可溯源的选品分析
- SKILL.md covers 目标, 不可妥协的边界, 先判断任务模式 and 开始前要拿到, plus 3 more sections
- Runs Python scripts from its folder
What it does
Sealeap Tianlu Amazon AI Agent MCP Product Research is an agent skill from xjli360/sealeap-amazon-skills. Configure an AI coding/agent tool with a third-party Amazon-data MCP connector to retrieve category, ASIN sales-structure, keyword, review, and price-band evidence, and drive a structured, source-traceable product-selection analysis. Estimates from the connector are directional third-party proxies, not first-party Amazon data. Use for 配置AI选品工作流、用MCP连接器批量拉取选品数据、让Agent做可溯源的选品分析. Do not use its output as a final selection decision without cross-checking an independent source.
Its SKILL.md is about 760 tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files, including scripts and reference files (for example `agents/openai.yaml`, `references/mcp-data-plan.md` and `references/playbook.md`).
It sits in Productivity & Automation, covering App automation through connectors and MCP servers. It works with Model Context Protocol. The repository describes itself as: Reusable Agent Skills for Amazon product research, listings, advertising, inventory, and operations. The licence is MIT.
When your agent uses it
- 配置AI选品工作流、用MCP连接器批量拉取选品数据、让Agent做可溯源的选品分析
- Tasks that involve App automation through connectors
- Tasks that involve MCP servers
Example prompts
- “/sealeap-tianlu-amazon-ai-agent-mcp-product-research”
Requirements
- Python 3
Workflow steps
4 steps, taken from the first numbered list in SKILL.md.
- 诊断：读取现状、证据和缺口，不生成线上写入动作。
- 方案草案：输出可审核的结构、参数范围、实验和回退值。
- 执行准备：只生成待批准变更表或 API/控制台操作草案。
- 已批准执行：仅对用户在当前会话明确批准的对象和字段执行，并立即回读核验。
What it can do on your machine
Read from SKILL.md and the folder at commit 497d4b8. It shows what the files ask for, not the result of running them.
Tool permissions
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.
Runs code
Ships 1 file in scripts/ (Python), which the agent can run.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Sealeap Tianlu Amazon AI Agent MCP Product Research loads about 756 tokens when it runs, and up to ~3.1k if it reads all its reference files. Until then it costs about 132 tokens; SKILL.md has 142 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.
Safety
Auto-check passed
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.
SKILL.md
The full file from xjli360/sealeap-amazon-skills at commit 497d4b8, republished under its MIT licence (© xjli360). 142 words, ~756 tokens.
Download SKILL.mdSave it as
.claude/skills/sealeap-tianlu-amazon-ai-agent-mcp-product-research/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.- name
- sealeap-tianlu-amazon-ai-agent-mcp-product-research
- description
- Configure an AI coding/agent tool with a third-party Amazon-data MCP connector to retrieve category, ASIN sales-structure, keyword, review, and price-band evidence, and drive a structured, source-traceable product-selection analysis. Estimates from the connector are directional third-party proxies, not first-party Amazon data. Use for 配置AI选品工作流、用MCP连接器批量拉取选品数据、让Agent做可溯源的选品分析. Do not use its output as a final selection decision without cross-checking an independent source.
Amazon AI选品数据代理协同
目标
Configure an AI coding/agent tool with a third-party Amazon-data MCP connector to retrieve category, ASIN sales-structure, keyword, review, and price-band evidence, and drive a structured, source-traceable product-selection analysis. Estimates from the connector are directional third-party proxies, not first-party Amazon data.
不可妥协的边界
- 当前 Amazon 官方政策、账户资格、站点字段和一方数据优先于本 Skill 的经验框架。
- 第三方数据一律标为估算或前台观测，不得写成 Amazon 一方事实。
- 默认只读诊断和草案；任何广告、Listing、库存、促销或外部系统写操作都需逐项展示并取得明确批准。
- 一次实验只改变一个主要变量，并记录基线、样本、成功、停止和回退条件。
- 不得复制来源材料或竞品表达；输出必须按当前任务重新组织并可由现有证据支撑。
- 第三方数据MCP连接器返回的销量、搜索量等均为平台外估算值，与亚马逊官方后台口径可能存在差异，只能作为方向性参考，不能替代自身店铺一方数据。
- AI Agent的分析步骤与结论组织方式依赖其推理过程，同样的数据在不同次调用中可能产出不同表述，关键结论需人工复核而非直接采纳。
- 数据连接器的可用字段、鉴权方式与购买渠道由服务商决定且会变化，接入前以服务商当前文档为准。
先判断任务模式
- 诊断：读取现状、证据和缺口，不生成线上写入动作。
- 方案草案：输出可审核的结构、参数范围、实验和回退值。
- 执行准备：只生成待批准变更表或 API/控制台操作草案。
- 已批准执行：仅对用户在当前会话明确批准的对象和字段执行，并立即回读核验。
用户未指定时采用“诊断”。
开始前要拿到
- 目标 marketplace、产品事实、品牌语气和当前政策约束
- 已授权的 Listing、关键词、评论/VOC、图片和竞品证据
- 每项数据的来源、时间、站点、样本和限制
- 人工审核人、发布边界和不可生成的声明或视觉特征
缺失项必须标为 NEEDS_EVIDENCE；不得猜数字、补属性或把不同站点、ASIN、变体、币种和时间窗混在一起。
工作流
先读取 references/playbook.md，确认该方法适用于当前对象。按以下顺序执行：
- 在AI编程/agent工具中新增一个第三方亚马逊数据MCP连接器，按数据服务商当前的接入文档完成鉴权（通常需在服务商后台单独购买或开通该接口权限），并用一次最小化查询验证连通与返回字段。
- 明确本次选品要回答的具体问题（如某类目容量、某关键词竞争格局、某价格带机会），把问题拆解成需要拉取的数据字段清单，避免让Agent无目标地全量抓取。
- 让Agent按拆解好的步骤依次调用数据连接器获取类目大盘、ASIN销量结构、关键词搜索量、竞品评论与价格带分布，每一步先检查返回数据的时间窗口、样本量与口径是否与上一步一致。
- 对Agent给出的中间结论（如某类目机会、某价格带空档）逐项要求列出支撑数据来源与计算方式，凡是无法追溯到具体返回字段的结论标记为待验证，不直接采信。
- 把Agent产出的候选清单与至少一个独立信源（如另一数据连接器或人工核对店铺后台）做交叉验证，出现明显冲突的候选先搁置。
- 保留本次查询的字段范围、时间窗口与Agent推理过程记录，作为后续复查或调整分析问题的依据。
最后做数据充分性检查，并把结论分成 FACT / ESTIMATE / HYPOTHESIS / UNKNOWN。若关键证据不足，状态写 HOLD。
第三方 MCP 数据
仅在自有数据不足且当前任务确实需要外部证据时，读取 references/mcp-data-plan.md，再使用 scripts/mcp_research.py。本 Skill 的外部取数目的：通过第三方亚马逊数据MCP连接器获取类目大盘、ASIN销量结构、关键词搜索量、竞品评论与价格带分布，供AI Agent做可溯源的选品分析与交叉验证。
- 先
doctor，再search-tools和describe；工具名及参数以实时tools/list与inputSchema为准。 - Token 只从环境变量读取。不得写入命令参数、URL、Skill、报告、日志或 Git。
tools/call或 Actor 可能计费；先展示 Provider、工具、无密钥业务参数、预计成本与输出位置，核对已有授权覆盖后才加--allow-cost；该标志不是费用上限。
必须交付的结果
- MCP连接器接入与验证记录
- 选品问题拆解与字段需求清单
- 候选品交叉验证结果表
- Agent推理过程留痕记录
- 数据范围、来源、采集时间、样本与限制。
- 关键假设、待补证据、风险和不可确定项。
- 若有动作：对象、旧值、新值、预期、停止条件、回退值与审批状态。
方案状态使用 READY FOR REVIEW / DRAFT / HOLD / STOP；如已执行，另行记录实际结果及回读证据。未得到明确批准时，不得声称已修改线上对象。
© xjli360, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 4 other files (scripts, references) in amazon-skills/weixin/tianlu/sealeap-tianlu-amazon-ai-agent-mcp-product-research of xjli360/sealeap-amazon-skills.
- SKILL.md
- agents/openai.yaml
- references/mcp-data-plan.md
- references/playbook.md
- scripts/mcp_research.py
Open the folder on GitHubat commit 497d4b8
Compare with similar skills
Sealeap Tianlu Amazon AI Agent MCP Product Research 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 |
|---|---|---|---|---|---|---|
| Sealeap Tianlu Amazon AI Agent MCP Product Research this skillxjli360/sealeap-amazon-skills | 251 | — | ~756 | Automated safety check: Pass | MIT | |
| Slm Web Accessqualixar/superlocalmemory | 230 | — | ~1.3k | Automated safety check: Notes | AGPL-3.0 | |
| ComposioComposioHQ/composio | 30k | 1 repos | ~1.7k | Automated safety check: Pass | MIT | |
| Composio Byodrewnekota/cetus | 146 | — | ~627 | Automated safety check: Pass | MIT | |
| Superset Integration Toolssuperset-sh/superset | 15k | — | ~1.6k | Automated safety check: Warn | Custom licence | |
| Power Platform MCP Connector Suitegithub/awesome-copilot | 40k | 1 repos | ~1.6k | Automated safety check: Pass | MIT |
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Diagnose and draft Amazon Canada apparel advertising plans with lifecycle and seasonal timing, English/French search coverage, account evidence, profitability guardrails, and approval-ready…
Sealeap Amazon Listing Optimizer
xjli360/sealeap-amazon-skills
Audit, diagnose, rewrite, creatively brief, test, and safely prepare updates for Amazon product detail pages using live marketplace and product-type requirements, verified product facts, Brand…
Sealeap Amazon Prime Day Planning
xjli360/sealeap-amazon-skills
Filter, interpret, and turn the authorized 2025 Amazon Prime Day advertising insight records into a qualified event plan without averaging incompatible slices or treating historical benchmarks as…
Sealeap Amazon Product Targeting
xjli360/sealeap-amazon-skills
Research, diagnose, and draft Amazon Ads ASIN and category product-targeting plans that complement keyword targeting, including audience expansion, competitor and category traffic, cross-sell…
Sealeap Amazon Acos Conversion Diagnostics
xjli360/sealeap-amazon-skills
Diagnose high Amazon Ads ACoS by decomposing CPC, conversion rate, price, query mix, placement mix, and sample sufficiency.
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Questions about Sealeap Tianlu Amazon AI Agent MCP Product Research
What does Sealeap Tianlu Amazon AI Agent MCP Product Research do?
Configure an AI coding/agent tool with a third-party Amazon-data MCP connector to retrieve category, ASIN sales-structure, keyword, review, and price-band evidence, and drive a structured…. Sealeap Tianlu Amazon AI Agent MCP Product Research is an agent skill from xjli360/sealeap-amazon-skills. Configure an AI coding/agent tool with a third-party Amazon-data MCP connector to retrieve category, ASIN sales-structure, keyword, review, and price-band evidence, and drive a structured, source-traceable product-selection analysis.
When should I use Sealeap Tianlu Amazon AI Agent MCP Product Research?
Sealeap Tianlu Amazon AI Agent MCP Product Research fits situations like: 配置AI选品工作流、用MCP连接器批量拉取选品数据、让Agent做可溯源的选品分析; tasks that involve App automation through connectors; tasks that involve MCP servers.
How do I install Sealeap Tianlu Amazon AI Agent MCP Product Research in Claude Code?
Run `npx skills add xjli360/sealeap-amazon-skills --skill sealeap-tianlu-amazon-ai-agent-mcp-product-research -a claude-code`. Or copy the skill folder (amazon-skills/weixin/tianlu/sealeap-tianlu-amazon-ai-agent-mcp-product-research in xjli360/sealeap-amazon-skills) into .claude/skills/sealeap-tianlu-amazon-ai-agent-mcp-product-research in your project. Claude Code loads it when a task matches its description.
How do I install Sealeap Tianlu Amazon AI Agent MCP Product Research in Codex?
Run `npx skills add xjli360/sealeap-amazon-skills --skill sealeap-tianlu-amazon-ai-agent-mcp-product-research -a codex`. Or copy the skill folder (amazon-skills/weixin/tianlu/sealeap-tianlu-amazon-ai-agent-mcp-product-research in xjli360/sealeap-amazon-skills) into .agents/skills/sealeap-tianlu-amazon-ai-agent-mcp-product-research in your project. Codex loads it when a task matches its description.
Can I use Sealeap Tianlu Amazon AI Agent MCP Product Research in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add xjli360/sealeap-amazon-skills --skill sealeap-tianlu-amazon-ai-agent-mcp-product-research -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/sealeap-tianlu-amazon-ai-agent-mcp-product-research, .gemini/skills/sealeap-tianlu-amazon-ai-agent-mcp-product-research, .github/skills/sealeap-tianlu-amazon-ai-agent-mcp-product-research and .opencode/skills/sealeap-tianlu-amazon-ai-agent-mcp-product-research in your project.
What does Sealeap Tianlu Amazon AI Agent MCP Product Research need to run?
Going by SKILL.md and its folder, Sealeap Tianlu Amazon AI Agent MCP Product Research needs Python for the scripts in its folder. Our summary lists: Python 3.
Does Sealeap Tianlu Amazon AI Agent MCP Product Research access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Sealeap Tianlu Amazon AI Agent MCP Product Research safe to install?
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.
What licence does Sealeap Tianlu Amazon AI Agent MCP Product Research use?
Sealeap Tianlu Amazon AI Agent MCP Product Research is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Sealeap Tianlu Amazon AI Agent MCP Product Research use?
About 756 tokens (SKILL.md is roughly 3k 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 2.4k tokens, read only when the agent opens those files.
What are the alternatives to Sealeap Tianlu Amazon AI Agent MCP Product Research?
Skills that share tags, products or a category with Sealeap Tianlu Amazon AI Agent MCP Product Research: Slm Web Access (qualixar/superlocalmemory, 230 stars), Composio (ComposioHQ/composio, 30k stars), Composio Byo (drewnekota/cetus, 146 stars) and Superset Integration Tools (superset-sh/superset, 15k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Sealeap Tianlu Amazon AI Agent MCP Product Research?
xjli360 (a GitHub user) maintains it in xjli360/sealeap-amazon-skills, which has 251 GitHub stars. The repository holds 179 skills in this directory. The repository was last updated on September 28, 2026.
Source: xjli360/sealeap-amazon-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.