Agent skill
Assemble a repeatable evidence pipeline covering demand mining, side-by-side competitive teardown, and review-driven pain-point extraction before drafting Amazon listing copy and images meant to be…
$ npx skills add xjli360/sealeap-amazon-skills --skill sealeap-baxia-amazon-ai-shopping-evidence-pipeline -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install xjli360/sealeap-amazon-skills sealeap-baxia-amazon-ai-shopping-evidence-pipeline --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/baxia/sealeap-baxia-amazon-ai-shopping-evidence-pipeline .claude/skills/sealeap-baxia-amazon-ai-shopping-evidence-pipeline && 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-baxia-amazon-ai-shopping-evidence-pipeline" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/weixin/baxia/sealeap-baxia-amazon-ai-shopping-evidence-pipeline into .claude/skills/sealeap-baxia-amazon-ai-shopping-evidence-pipeline/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-baxia-amazon-ai-shopping-evidence-pipeline", 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/baxia/sealeap-baxia-amazon-ai-shopping-evidence-pipelineType 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-baxia-amazon-ai-shopping-evidence-pipeline -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install xjli360/sealeap-amazon-skills sealeap-baxia-amazon-ai-shopping-evidence-pipeline --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/baxia/sealeap-baxia-amazon-ai-shopping-evidence-pipeline .agents/skills/sealeap-baxia-amazon-ai-shopping-evidence-pipeline && 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-baxia-amazon-ai-shopping-evidence-pipeline" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/weixin/baxia/sealeap-baxia-amazon-ai-shopping-evidence-pipeline into .agents/skills/sealeap-baxia-amazon-ai-shopping-evidence-pipeline/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-baxia-amazon-ai-shopping-evidence-pipeline", 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-baxia-amazon-ai-shopping-evidence-pipeline -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install xjli360/sealeap-amazon-skills sealeap-baxia-amazon-ai-shopping-evidence-pipeline --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/baxia/sealeap-baxia-amazon-ai-shopping-evidence-pipeline .cursor/skills/sealeap-baxia-amazon-ai-shopping-evidence-pipeline && 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-baxia-amazon-ai-shopping-evidence-pipeline" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/weixin/baxia/sealeap-baxia-amazon-ai-shopping-evidence-pipeline into .cursor/skills/sealeap-baxia-amazon-ai-shopping-evidence-pipeline/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-baxia-amazon-ai-shopping-evidence-pipeline", 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/baxia/sealeap-baxia-amazon-ai-shopping-evidence-pipeline--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-baxia-amazon-ai-shopping-evidence-pipeline -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install xjli360/sealeap-amazon-skills sealeap-baxia-amazon-ai-shopping-evidence-pipeline --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/baxia/sealeap-baxia-amazon-ai-shopping-evidence-pipeline .gemini/skills/sealeap-baxia-amazon-ai-shopping-evidence-pipeline && 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-baxia-amazon-ai-shopping-evidence-pipeline" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/weixin/baxia/sealeap-baxia-amazon-ai-shopping-evidence-pipeline into .gemini/skills/sealeap-baxia-amazon-ai-shopping-evidence-pipeline/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-baxia-amazon-ai-shopping-evidence-pipeline", 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-baxia-amazon-ai-shopping-evidence-pipelineInstalls 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-baxia-amazon-ai-shopping-evidence-pipeline -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/baxia/sealeap-baxia-amazon-ai-shopping-evidence-pipeline .github/skills/sealeap-baxia-amazon-ai-shopping-evidence-pipeline && 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-baxia-amazon-ai-shopping-evidence-pipeline" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/weixin/baxia/sealeap-baxia-amazon-ai-shopping-evidence-pipeline into .github/skills/sealeap-baxia-amazon-ai-shopping-evidence-pipeline/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-baxia-amazon-ai-shopping-evidence-pipeline", 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-baxia-amazon-ai-shopping-evidence-pipeline -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-baxia-amazon-ai-shopping-evidence-pipeline --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/baxia/sealeap-baxia-amazon-ai-shopping-evidence-pipeline .opencode/skills/sealeap-baxia-amazon-ai-shopping-evidence-pipeline && 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-baxia-amazon-ai-shopping-evidence-pipeline" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/weixin/baxia/sealeap-baxia-amazon-ai-shopping-evidence-pipeline into .opencode/skills/sealeap-baxia-amazon-ai-shopping-evidence-pipeline/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-baxia-amazon-ai-shopping-evidence-pipeline", 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.
sealeap-baxia-amazon-ai-shopping-evidence-pipelineAssemble a repeatable evidence pipeline covering demand mining, side-by-side competitive teardown, and review-driven pain-point extraction before drafting Amazon listing copy and images meant to be…
Sealeap Baxia Amazon AI Shopping Evidence Pipeline is an agent skill from xjli360/sealeap-amazon-skills. Assemble a repeatable evidence pipeline covering demand mining, side-by-side competitive teardown, and review-driven pain-point extraction before drafting Amazon listing copy and images meant to be legible to AI-assisted shopping interfaces. Use for AI购物入口上线后的选品调研、竞品拆解取证、评论痛点提炼、文案与图片产出前的证据准备. Do not use to fabricate competitive data or to publish AI-drafted copy without a human fact and policy check.
Its SKILL.md is about 660 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 Writing & Content, covering Copywriting. 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.
4 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit 497d4b8. 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.
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Sealeap Baxia Amazon AI Shopping Evidence Pipeline loads about 657 tokens when it runs, and up to ~2.9k if it reads all its reference files. Until then it costs about 114 tokens; SKILL.md has 130 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 xjli360/sealeap-amazon-skills at commit 497d4b8, republished under its MIT licence (© xjli360). 130 words, ~657 tokens.
.claude/skills/sealeap-baxia-amazon-ai-shopping-evidence-pipeline/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.Assemble a repeatable evidence pipeline covering demand mining, side-by-side competitive teardown, and review-driven pain-point extraction before drafting Amazon listing copy and images meant to be legible to AI-assisted shopping interfaces.
用户未指定时采用“诊断”。
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。
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：
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。
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：补充关键词、竞品对比与评论痛点等第三方证据，用于支撑文案与图片素材的产出简报。
doctor，再 search-tools 和 describe；工具名及参数以实时 tools/list 与 inputSchema 为准。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。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
SKILL.md and 4 other files (scripts, references) in amazon-skills/weixin/baxia/sealeap-baxia-amazon-ai-shopping-evidence-pipeline of xjli360/sealeap-amazon-skills.
Open the folder on GitHubat commit 497d4b8
Sealeap Baxia Amazon AI Shopping Evidence Pipeline 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 Baxia Amazon AI Shopping Evidence Pipeline this skillxjli360/sealeap-amazon-skills | 251 | — | ~657 | Automated safety check: Pass | MIT | |
| Lemlist Campaign From IcpOthmane-Khadri/YALC-the-GTM-operating-system | 318 | — | ~6.5k | Automated safety check: Pass | MIT | |
| Find PR Agencyjeremylongshore/tons-of-skills-marketplace | 2.8k | — | ~3.7k | Automated safety check: Notes | MIT | |
| Roam Evidence HardeningCranot/roam-code | 517 | — | ~1.5k | Automated safety check: Pass | Apache-2.0 | |
| Writeohad6k/emulo | 294 | 1 repos | ~488 | Automated safety check: Pass | MIT | |
| Roam Release ReadinessCranot/roam-code | 517 | — | ~1.7k | Automated safety check: Pass | Apache-2.0 |
Othmane-Khadri/YALC-the-GTM-operating-system
A skill your agent uses when the user says "create a lemlist campaign for X", "build a lemlist campaign from this ICP", "spin a lemlist campaign for Y", "ICP to lemlist", "natural language to…
jeremylongshore/tons-of-skills-marketplace
A skill your agent uses whenever the user wants to find, shortlist, vet, or enrich US public-relations and communications agencies — media relations, crisis comms, investor relations (IR)…
Cranot/roam-code
Investigate and harden Roam detectors, CLI/MCP result contracts, and evidence consumers when dogfooding or correcting incomplete, misleading, or inconsistent analysis.
ohad6k/emulo
A skill your agent uses for marketing, social, replies, product copy, launch copy, and writing in the user's voice when their Emulo writing profile should guide the task.
Cranot/roam-code
Qualify a Roam commit or accumulated batch for package, website, or server publication; reconcile source, tests, review, CI and deployed identities, and identify the next authorized release step.
danyuchn/asd-ste100-skill
A skill your agent uses when English text must be parsed without a human to resolve ambiguity — tool descriptions, error messages, inter-agent instructions, system prompts, status reports — and…
xjli360/sealeap-amazon-skills
Diagnose Amazon Ads ACOS with reconciled CTR, CPC, CVR, AOV, ROAS, TACOS, placement, search-term, benchmark, attribution, and contribution-margin evidence, then produce a single-variable…
xjli360/sealeap-amazon-skills
Diagnose and draft Amazon Canada apparel advertising plans with lifecycle and seasonal timing, English/French search coverage, account evidence, profitability guardrails, and approval-ready…
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…
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…
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…
xjli360/sealeap-amazon-skills
Diagnose high Amazon Ads ACoS by decomposing CPC, conversion rate, price, query mix, placement mix, and sample sufficiency.
Works with
Categories
Assemble a repeatable evidence pipeline covering demand mining, side-by-side competitive teardown, and review-driven pain-point extraction before drafting Amazon listing copy and images meant to be…. Sealeap Baxia Amazon AI Shopping Evidence Pipeline is an agent skill from xjli360/sealeap-amazon-skills. Assemble a repeatable evidence pipeline covering demand mining, side-by-side competitive teardown, and review-driven pain-point extraction before drafting Amazon listing copy and images meant to be legible to AI-assisted shopping interfaces.
Sealeap Baxia Amazon AI Shopping Evidence Pipeline fits situations like: AI购物入口上线后的选品调研、竞品拆解取证、评论痛点提炼、文案与图片产出前的证据准备; fabricate competitive data; publish AI-drafted copy without a human fact and policy check.
Run `npx skills add xjli360/sealeap-amazon-skills --skill sealeap-baxia-amazon-ai-shopping-evidence-pipeline -a claude-code`. Or copy the skill folder (amazon-skills/weixin/baxia/sealeap-baxia-amazon-ai-shopping-evidence-pipeline in xjli360/sealeap-amazon-skills) into .claude/skills/sealeap-baxia-amazon-ai-shopping-evidence-pipeline in your project. Claude Code loads it when a task matches its description.
Run `npx skills add xjli360/sealeap-amazon-skills --skill sealeap-baxia-amazon-ai-shopping-evidence-pipeline -a codex`. Or copy the skill folder (amazon-skills/weixin/baxia/sealeap-baxia-amazon-ai-shopping-evidence-pipeline in xjli360/sealeap-amazon-skills) into .agents/skills/sealeap-baxia-amazon-ai-shopping-evidence-pipeline 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 xjli360/sealeap-amazon-skills --skill sealeap-baxia-amazon-ai-shopping-evidence-pipeline -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-baxia-amazon-ai-shopping-evidence-pipeline, .gemini/skills/sealeap-baxia-amazon-ai-shopping-evidence-pipeline, .github/skills/sealeap-baxia-amazon-ai-shopping-evidence-pipeline and .opencode/skills/sealeap-baxia-amazon-ai-shopping-evidence-pipeline in your project.
Going by SKILL.md and its folder, Sealeap Baxia Amazon AI Shopping Evidence Pipeline needs Python for the scripts in its folder. Our summary lists: Python 3.
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.
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.
Sealeap Baxia Amazon AI Shopping Evidence Pipeline is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 657 tokens (SKILL.md is roughly 2.6k 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 2.3k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Sealeap Baxia Amazon AI Shopping Evidence Pipeline: Lemlist Campaign From Icp (Othmane-Khadri/YALC-the-GTM-operating-system, 318 stars), Find PR Agency (jeremylongshore/tons-of-skills-marketplace, 2.8k stars), Roam Evidence Hardening (Cranot/roam-code, 517 stars) and Write (ohad6k/emulo, 294 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
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.
