MCP Server Builder
anthropics/skills
Guides the design and implementation of Model Context Protocol servers in TypeScript or Python, from tool naming and error messages to evaluation.
Agent skill
Sealeap Qiongqi Amazon Main Image AI Iteration
Iterate Amazon primary images as a continuous experiment: mine search term and performance data for click-through hypotheses, generate compliant image variants with AI, and validate through Manage…
MITAuto-check passed
Install Sealeap Qiongqi Amazon Main Image AI Iteration
$ npx skills add xjli360/sealeap-amazon-skills --skill sealeap-qiongqi-amazon-main-image-ai-iteration -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install xjli360/sealeap-amazon-skills sealeap-qiongqi-amazon-main-image-ai-iteration --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/youtube/qiongqi/sealeap-qiongqi-amazon-main-image-ai-iteration .claude/skills/sealeap-qiongqi-amazon-main-image-ai-iteration && 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-qiongqi-amazon-main-image-ai-iteration" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/youtube/qiongqi/sealeap-qiongqi-amazon-main-image-ai-iteration into .claude/skills/sealeap-qiongqi-amazon-main-image-ai-iteration/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-qiongqi-amazon-main-image-ai-iteration", 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/youtube/qiongqi/sealeap-qiongqi-amazon-main-image-ai-iterationType 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-qiongqi-amazon-main-image-ai-iteration -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install xjli360/sealeap-amazon-skills sealeap-qiongqi-amazon-main-image-ai-iteration --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/youtube/qiongqi/sealeap-qiongqi-amazon-main-image-ai-iteration .agents/skills/sealeap-qiongqi-amazon-main-image-ai-iteration && 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-qiongqi-amazon-main-image-ai-iteration" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/youtube/qiongqi/sealeap-qiongqi-amazon-main-image-ai-iteration into .agents/skills/sealeap-qiongqi-amazon-main-image-ai-iteration/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-qiongqi-amazon-main-image-ai-iteration", 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-qiongqi-amazon-main-image-ai-iteration -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install xjli360/sealeap-amazon-skills sealeap-qiongqi-amazon-main-image-ai-iteration --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/youtube/qiongqi/sealeap-qiongqi-amazon-main-image-ai-iteration .cursor/skills/sealeap-qiongqi-amazon-main-image-ai-iteration && 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-qiongqi-amazon-main-image-ai-iteration" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/youtube/qiongqi/sealeap-qiongqi-amazon-main-image-ai-iteration into .cursor/skills/sealeap-qiongqi-amazon-main-image-ai-iteration/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-qiongqi-amazon-main-image-ai-iteration", 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/youtube/qiongqi/sealeap-qiongqi-amazon-main-image-ai-iteration--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-qiongqi-amazon-main-image-ai-iteration -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install xjli360/sealeap-amazon-skills sealeap-qiongqi-amazon-main-image-ai-iteration --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/youtube/qiongqi/sealeap-qiongqi-amazon-main-image-ai-iteration .gemini/skills/sealeap-qiongqi-amazon-main-image-ai-iteration && 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-qiongqi-amazon-main-image-ai-iteration" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/youtube/qiongqi/sealeap-qiongqi-amazon-main-image-ai-iteration into .gemini/skills/sealeap-qiongqi-amazon-main-image-ai-iteration/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-qiongqi-amazon-main-image-ai-iteration", 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-qiongqi-amazon-main-image-ai-iterationInstalls 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-qiongqi-amazon-main-image-ai-iteration -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/youtube/qiongqi/sealeap-qiongqi-amazon-main-image-ai-iteration .github/skills/sealeap-qiongqi-amazon-main-image-ai-iteration && 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-qiongqi-amazon-main-image-ai-iteration" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/youtube/qiongqi/sealeap-qiongqi-amazon-main-image-ai-iteration into .github/skills/sealeap-qiongqi-amazon-main-image-ai-iteration/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-qiongqi-amazon-main-image-ai-iteration", 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-qiongqi-amazon-main-image-ai-iteration -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-qiongqi-amazon-main-image-ai-iteration --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/youtube/qiongqi/sealeap-qiongqi-amazon-main-image-ai-iteration .opencode/skills/sealeap-qiongqi-amazon-main-image-ai-iteration && 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-qiongqi-amazon-main-image-ai-iteration" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/youtube/qiongqi/sealeap-qiongqi-amazon-main-image-ai-iteration into .opencode/skills/sealeap-qiongqi-amazon-main-image-ai-iteration/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-qiongqi-amazon-main-image-ai-iteration", 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-qiongqi-amazon-main-image-ai-iteration- GitHub stars
- 251
- Token cost
- ~689 tokens
- SKILL.md length
- 145 words
- Files
- 5 (incl. scripts, references)
- Skills in repo
- 179
- Repo updated
- First seen
- Licence
- MIT
At a glance
Iterate Amazon primary images as a continuous experiment: mine search term and performance data for click-through hypotheses, generate compliant image variants with AI, and validate through Manage…
- 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。 → …
- 主图怎么优化、点击率低换主图、AI 生成主图、主图 A/B 测试、主图假设从哪来
- 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 Qiongqi Amazon Main Image AI Iteration is an agent skill from xjli360/sealeap-amazon-skills. Iterate Amazon primary images as a continuous experiment: mine search term and performance data for click-through hypotheses, generate compliant image variants with AI, and validate through Manage Your Experiments before rollout. Use for 主图怎么优化、点击率低换主图、AI 生成主图、主图 A/B 测试、主图假设从哪来. Do not use to publish images that misrepresent the product or add claims outside the real packaging.
Its SKILL.md is about 690 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 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
- 主图怎么优化、点击率低换主图、AI 生成主图、主图 A/B 测试、主图假设从哪来
- Publish images that misrepresent the product
- Add claims outside the real packaging
Example prompts
- “/sealeap-qiongqi-amazon-main-image-ai-iteration”
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 Qiongqi Amazon Main Image AI Iteration loads about 689 tokens when it runs, and up to ~3k if it reads all its reference files. Until then it costs about 107 tokens; SKILL.md has 145 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). 145 words, ~689 tokens.
Download SKILL.mdSave it as
.claude/skills/sealeap-qiongqi-amazon-main-image-ai-iteration/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.- name
- sealeap-qiongqi-amazon-main-image-ai-iteration
- description
- Iterate Amazon primary images as a continuous experiment: mine search term and performance data for click-through hypotheses, generate compliant image variants with AI, and validate through Manage Your Experiments before rollout. Use for 主图怎么优化、点击率低换主图、AI 生成主图、主图 A/B 测试、主图假设从哪来. Do not use to publish images that misrepresent the product or add claims outside the real packaging.
Amazon 主图数据驱动 AI 迭代测试
目标
Iterate Amazon primary images as a continuous experiment: mine search term and performance data for click-through hypotheses, generate compliant image variants with AI, and validate through Manage Your Experiments before rollout.
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。
- 来源列举的点击率与转化率提升幅度为其案例数据，不可外推；本账户效果只以实验结果为准。
- 『主图也影响转化率是因为吸引了更相关的流量』是来源解释，属待验证假设。
- 生成式图像可能产生与实物不符的细节或违规元素，任何上线前须人工逐项核对图片规则。
- 实验结果受同期价格、促销、库存与广告变化干扰，测试期间保持这些变量不变。
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
- marketplace、ASIN/SKU、产品事实和当前 Listing
- 同购买意图可比竞品、价格、评论、图片和销量
- Search Term、Placement、CTR、CVR、订单、退货和利润
- VOC、Q&A、退货原因与任何页面或广告变更日志
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：
- 拉取报表可用最长窗口的搜索词报表与 Listing 的曝光、点击率、转化率基线，明确当前主图是猜测还是有数据支撑。
- 让 AI 分析搜索词与评论：提炼买家的核心意图、异议与关注点（用途、人群、成分、尺寸、配件），标注哪些已在主图体现、哪些缺失。
- 把缺失点写成可证伪的点击率假设，每条只改一个主要元素：加人物/使用场景、展示成分或配件全家福、在真实包装上呈现主搜索词或指标、突出色彩对比、换角度或渲染风格、展示多颜色变体。
- 按假设生成变体，先做合规审查：产品外观必须与实物一致，指标与声明只能出现在真实包装上，纯白底与主体占比等图片规则照旧；细节偏差用编辑功能修正而不是接受。
- 在 Manage Your Experiments 里配置 A/B（A=现图，B=一个假设），跑满平台要求的时长，看点击率与转化率的实际差异；转化率变化解释为流量相关性变化的假设。
- 赢家上线后继续用新假设挑战，把每轮的假设、结果与结论沉淀成可复用工作流；不赢则回到假设库换方向。
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：获取同一搜索结果页竞品主图与价格的公开页面观测，用于对照点击率假设。
- 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
- 买家意图与异议清单
- 主图点击率假设库
- 合规审查表
- A/B 实验记录
- 主图迭代工作流
- 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/youtube/qiongqi/sealeap-qiongqi-amazon-main-image-ai-iteration 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 Qiongqi Amazon Main Image AI Iteration 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 Qiongqi Amazon Main Image AI Iteration this skillxjli360/sealeap-amazon-skills | 251 | — | ~689 | Automated safety check: Pass | MIT | |
| MCP Server Builderanthropics/skills | 180k | 63 repos | ~2.3k | Automated safety check: Pass | Apache-2.0 | |
| MCP Server BuildershareAI-lab/learn-claude-code | 78k | 4 repos | ~1.2k | Automated safety check: Pass | MIT | |
| MCP Integration for Pluginsanthropics/claude-plugins-official | 38k | 11 repos | ~3.1k | Automated safety check: Pass | Apache-2.0 | |
| Figma use_figma Plugin API Ruleswarpdotdev/warp | 65k | 4 repos | ~4.4k | Automated safety check: Pass | AGPL-3.0 | |
| Stitch to Remotion Walkthrough Videosgoogle-labs-code/stitch-skills | 8.5k | 6 repos | ~3.2k | Automated safety check: Notes | Apache-2.0 |
Similar skills
- Official
MCP Server Builder
shareAI-lab/learn-claude-code
Walks through building MCP servers in Python or TypeScript that expose tools, resources and prompts to Claude, with templates, registration and testing.
- Official
MCP Integration for Plugins
anthropics/claude-plugins-official
Explains how to bundle Model Context Protocol servers in a Claude Code plugin, covering config files, stdio, SSE, HTTP and WebSocket server types, and authentication.
Figma use_figma Plugin API Rules
warpdotdev/warp
Required groundwork before any use_figma call: the rules and reference files for running JavaScript in a Figma file through the Plugin API without common failures.
- Official
Stitch to Remotion Walkthrough Videos
google-labs-code/stitch-skills
Builds walkthrough videos from Stitch design projects using Remotion, with transitions, zoom effects and text overlays on each screen.
MCP Development
coollabsio/coolify
A skill your agent uses for Laravel MCP development. An agent skill from coollabsio/coolify.
More from xjli360/sealeap-amazon-skills
All 179 skills in this repoSealeap Amazon Acos Diagnostics
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…
Sealeap Amazon Ca Apparel Ads
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…
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.
Related
Works with
Questions about Sealeap Qiongqi Amazon Main Image AI Iteration
What does Sealeap Qiongqi Amazon Main Image AI Iteration do?
Iterate Amazon primary images as a continuous experiment: mine search term and performance data for click-through hypotheses, generate compliant image variants with AI, and validate through Manage…. Sealeap Qiongqi Amazon Main Image AI Iteration is an agent skill from xjli360/sealeap-amazon-skills. Iterate Amazon primary images as a continuous experiment: mine search term and performance data for click-through hypotheses, generate compliant image variants with AI, and validate through Manage Your Experiments before rollout.
When should I use Sealeap Qiongqi Amazon Main Image AI Iteration?
Sealeap Qiongqi Amazon Main Image AI Iteration fits situations like: 主图怎么优化、点击率低换主图、AI 生成主图、主图 A/B 测试、主图假设从哪来; publish images that misrepresent the product; add claims outside the real packaging.
How do I install Sealeap Qiongqi Amazon Main Image AI Iteration in Claude Code?
Run `npx skills add xjli360/sealeap-amazon-skills --skill sealeap-qiongqi-amazon-main-image-ai-iteration -a claude-code`. Or copy the skill folder (amazon-skills/youtube/qiongqi/sealeap-qiongqi-amazon-main-image-ai-iteration in xjli360/sealeap-amazon-skills) into .claude/skills/sealeap-qiongqi-amazon-main-image-ai-iteration in your project. Claude Code loads it when a task matches its description.
How do I install Sealeap Qiongqi Amazon Main Image AI Iteration in Codex?
Run `npx skills add xjli360/sealeap-amazon-skills --skill sealeap-qiongqi-amazon-main-image-ai-iteration -a codex`. Or copy the skill folder (amazon-skills/youtube/qiongqi/sealeap-qiongqi-amazon-main-image-ai-iteration in xjli360/sealeap-amazon-skills) into .agents/skills/sealeap-qiongqi-amazon-main-image-ai-iteration in your project. Codex loads it when a task matches its description.
Can I use Sealeap Qiongqi Amazon Main Image AI Iteration 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-qiongqi-amazon-main-image-ai-iteration -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-qiongqi-amazon-main-image-ai-iteration, .gemini/skills/sealeap-qiongqi-amazon-main-image-ai-iteration, .github/skills/sealeap-qiongqi-amazon-main-image-ai-iteration and .opencode/skills/sealeap-qiongqi-amazon-main-image-ai-iteration in your project.
What does Sealeap Qiongqi Amazon Main Image AI Iteration need to run?
Going by SKILL.md and its folder, Sealeap Qiongqi Amazon Main Image AI Iteration needs Python for the scripts in its folder. Our summary lists: Python 3.
Does Sealeap Qiongqi Amazon Main Image AI Iteration 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 Qiongqi Amazon Main Image AI Iteration 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 Qiongqi Amazon Main Image AI Iteration use?
Sealeap Qiongqi Amazon Main Image AI Iteration 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 Qiongqi Amazon Main Image AI Iteration use?
About 689 tokens (SKILL.md is roughly 2.8k 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.
What are the alternatives to Sealeap Qiongqi Amazon Main Image AI Iteration?
Skills that share tags, products or a category with Sealeap Qiongqi Amazon Main Image AI Iteration: MCP Server Builder (anthropics/skills, 180k stars), MCP Server Builder (shareAI-lab/learn-claude-code, 78k stars), MCP Integration for Plugins (anthropics/claude-plugins-official, 38k stars) and Figma use_figma Plugin API Rules (warpdotdev/warp, 65k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Sealeap Qiongqi Amazon Main Image AI Iteration?
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.