Evaluate Skill
every-app/open-seo
Test a candidate OpenSEO skill end to end by running fresh, isolated Codex sessions against the local backend and scoring the reports they save.
Agent skill
Sealeap Qiongqi Amazon AI Keyword Research Workflow
Run an agentic AI keyword research workflow for an Amazon product: start with the simplest prompt, add raw keyword exports and explicit context, require intent-segregated output against an…
Install Sealeap Qiongqi Amazon AI Keyword Research Workflow
$ npx skills add xjli360/sealeap-amazon-skills --skill sealeap-qiongqi-amazon-ai-keyword-research-workflow -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install xjli360/sealeap-amazon-skills sealeap-qiongqi-amazon-ai-keyword-research-workflow --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-ai-keyword-research-workflow .claude/skills/sealeap-qiongqi-amazon-ai-keyword-research-workflow && 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-ai-keyword-research-workflow" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/youtube/qiongqi/sealeap-qiongqi-amazon-ai-keyword-research-workflow into .claude/skills/sealeap-qiongqi-amazon-ai-keyword-research-workflow/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-qiongqi-amazon-ai-keyword-research-workflow", 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-ai-keyword-research-workflowType 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-ai-keyword-research-workflow -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install xjli360/sealeap-amazon-skills sealeap-qiongqi-amazon-ai-keyword-research-workflow --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-ai-keyword-research-workflow .agents/skills/sealeap-qiongqi-amazon-ai-keyword-research-workflow && 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-ai-keyword-research-workflow" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/youtube/qiongqi/sealeap-qiongqi-amazon-ai-keyword-research-workflow into .agents/skills/sealeap-qiongqi-amazon-ai-keyword-research-workflow/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-qiongqi-amazon-ai-keyword-research-workflow", 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-ai-keyword-research-workflow -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install xjli360/sealeap-amazon-skills sealeap-qiongqi-amazon-ai-keyword-research-workflow --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-ai-keyword-research-workflow .cursor/skills/sealeap-qiongqi-amazon-ai-keyword-research-workflow && 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-ai-keyword-research-workflow" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/youtube/qiongqi/sealeap-qiongqi-amazon-ai-keyword-research-workflow into .cursor/skills/sealeap-qiongqi-amazon-ai-keyword-research-workflow/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-qiongqi-amazon-ai-keyword-research-workflow", 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-ai-keyword-research-workflow--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-ai-keyword-research-workflow -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install xjli360/sealeap-amazon-skills sealeap-qiongqi-amazon-ai-keyword-research-workflow --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-ai-keyword-research-workflow .gemini/skills/sealeap-qiongqi-amazon-ai-keyword-research-workflow && 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-ai-keyword-research-workflow" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/youtube/qiongqi/sealeap-qiongqi-amazon-ai-keyword-research-workflow into .gemini/skills/sealeap-qiongqi-amazon-ai-keyword-research-workflow/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-qiongqi-amazon-ai-keyword-research-workflow", 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-ai-keyword-research-workflowInstalls 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-ai-keyword-research-workflow -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-ai-keyword-research-workflow .github/skills/sealeap-qiongqi-amazon-ai-keyword-research-workflow && 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-ai-keyword-research-workflow" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/youtube/qiongqi/sealeap-qiongqi-amazon-ai-keyword-research-workflow into .github/skills/sealeap-qiongqi-amazon-ai-keyword-research-workflow/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-qiongqi-amazon-ai-keyword-research-workflow", 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-ai-keyword-research-workflow -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-ai-keyword-research-workflow --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-ai-keyword-research-workflow .opencode/skills/sealeap-qiongqi-amazon-ai-keyword-research-workflow && 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-ai-keyword-research-workflow" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/youtube/qiongqi/sealeap-qiongqi-amazon-ai-keyword-research-workflow into .opencode/skills/sealeap-qiongqi-amazon-ai-keyword-research-workflow/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-qiongqi-amazon-ai-keyword-research-workflow", 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-ai-keyword-research-workflow- GitHub stars
- 251
- Token cost
- ~685 tokens
- SKILL.md length
- 151 words
- Files
- 5 (incl. scripts, references)
- Skills in repo
- 179
- Repo updated
- First seen
- Licence
- MIT
At a glance
Run an agentic AI keyword research workflow for an Amazon product: start with the simplest prompt, add raw keyword exports and explicit context, require intent-segregated output against an…
- 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。 → …
- AI 做关键词调研、关键词分组自动化、把关键词导出给 AI 整理、AI 关键词表能不能直接投
- 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 AI Keyword Research Workflow is an agent skill from xjli360/sealeap-amazon-skills. Run an agentic AI keyword research workflow for an Amazon product: start with the simplest prompt, add raw keyword exports and explicit context, require intent-segregated output against an excellence example, then verify relevance before campaign use. Use for AI 做关键词调研、关键词分组自动化、把关键词导出给 AI 整理、AI 关键词表能不能直接投. Do not use AI output for live campaigns without human relevance review.
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 sits in Marketing & SEO, covering Keyword research. 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
- AI 做关键词调研、关键词分组自动化、把关键词导出给 AI 整理、AI 关键词表能不能直接投
- Tasks that involve Keyword research
Example prompts
- “/sealeap-qiongqi-amazon-ai-keyword-research-workflow”
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 AI Keyword Research Workflow loads about 685 tokens when it runs, and up to ~3k if it reads all its reference files. Until then it costs about 108 tokens; SKILL.md has 151 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). 151 words, ~685 tokens.
Download SKILL.mdSave it as
.claude/skills/sealeap-qiongqi-amazon-ai-keyword-research-workflow/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-ai-keyword-research-workflow
- description
- Run an agentic AI keyword research workflow for an Amazon product: start with the simplest prompt, add raw keyword exports and explicit context, require intent-segregated output against an excellence example, then verify relevance before campaign use. Use for AI 做关键词调研、关键词分组自动化、把关键词导出给 AI 整理、AI 关键词表能不能直接投. Do not use AI output for live campaigns without human relevance review.
Amazon AI 辅助关键词调研工作流
目标
Run an agentic AI keyword research workflow for an Amazon product: start with the simplest prompt, add raw keyword exports and explicit context, require intent-segregated output against an excellence example, then verify relevance before campaign use.
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。
- AI 会从公开网页与搜索联想补词，可能引入无搜索量或不相关的词；搜索量只信来自工具导出的数据。
- 『AI 已可完全替代人工关键词调研』是来源观点，属待验证假设；仍需按类目抽检。
- 输出质量高度依赖上下文与示例，模型与工具版本变化会改变结果，每次批量使用前先跑小样本对照。
- 含他人品牌、受保护词或违规声明的词不得直接投放；相关性与合规复核由人负责。
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：
- 先做最简尝试：只给产品链接或产品事实，让 AI 输出关键词调研；追问它的来源与分组方式，记录缺口（覆盖不全、未按意图分组、混入不相关词）。
- 补数据：从关键词工具导出目标主词的完整未过滤关键词列表，连同产品事实一起提供；不要预先手工过滤，把过滤逻辑交给模型。
- 补上下文：明确要求只保留与产品相关的词、指出机会点、按购买意图/语义核心分组，并给出输出格式（分组表、后台搜索词、竞品品牌词、否定词、意图说明）。
- 给一份『优秀输出示例』作为模板，让 AI 对齐结构与质量；把这套提示固化成可复用的 Skill 或 SOP，让它主动追问缺失的上下文。
- 人工复核：抽查每组关键词与产品事实的相关性、搜索量是否来自导出数据而非臆测，剔除幻觉词与不合规词。
- 复核后的分组表交给分组建活动流程；上线后用搜索词报表验证分组是否与实际触发搜索一致，并把纠错回填到示例模板。
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：获取原始关键词列表、搜索量与竞品关键词的第三方代理数据。
- 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
- AI 关键词调研提示与上下文包
- 按意图分组的关键词表
- 人工复核记录
- 优秀输出示例模板
- 可复用 Skill/SOP 草案
- 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-ai-keyword-research-workflow 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 AI Keyword Research Workflow 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 AI Keyword Research Workflow this skillxjli360/sealeap-amazon-skills | 251 | — | ~685 | Automated safety check: Pass | MIT | |
| Evaluate Skillevery-app/open-seo | 23k | — | ~1.8k | Automated safety check: Notes | MIT | |
| SEO DataforseoAgriciDaniel/codex-seo | 799 | 2 repos | ~4.6k | Automated safety check: Pass | MIT | |
| Competitor GapRyze-AI-Adgent/open-seo-mcp-skills | 4.7k | — | ~560 | Automated safety check: Pass | MIT | |
| 90 Day SEO SprintBomx/distribb-skill | 197 | — | ~3.6k | Automated safety check: Pass | None | |
| Keyword ResearchRyze-AI-Adgent/open-seo-mcp-skills | 4.7k | — | ~581 | Automated safety check: Pass | MIT |
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Questions about Sealeap Qiongqi Amazon AI Keyword Research Workflow
What does Sealeap Qiongqi Amazon AI Keyword Research Workflow do?
Run an agentic AI keyword research workflow for an Amazon product: start with the simplest prompt, add raw keyword exports and explicit context, require intent-segregated output against an…. Sealeap Qiongqi Amazon AI Keyword Research Workflow is an agent skill from xjli360/sealeap-amazon-skills. Run an agentic AI keyword research workflow for an Amazon product: start with the simplest prompt, add raw keyword exports and explicit context, require intent-segregated output against an excellence example, then verify relevance before campaign use.
When should I use Sealeap Qiongqi Amazon AI Keyword Research Workflow?
Sealeap Qiongqi Amazon AI Keyword Research Workflow fits situations like: AI 做关键词调研、关键词分组自动化、把关键词导出给 AI 整理、AI 关键词表能不能直接投; tasks that involve Keyword research.
How do I install Sealeap Qiongqi Amazon AI Keyword Research Workflow in Claude Code?
Run `npx skills add xjli360/sealeap-amazon-skills --skill sealeap-qiongqi-amazon-ai-keyword-research-workflow -a claude-code`. Or copy the skill folder (amazon-skills/youtube/qiongqi/sealeap-qiongqi-amazon-ai-keyword-research-workflow in xjli360/sealeap-amazon-skills) into .claude/skills/sealeap-qiongqi-amazon-ai-keyword-research-workflow in your project. Claude Code loads it when a task matches its description.
How do I install Sealeap Qiongqi Amazon AI Keyword Research Workflow in Codex?
Run `npx skills add xjli360/sealeap-amazon-skills --skill sealeap-qiongqi-amazon-ai-keyword-research-workflow -a codex`. Or copy the skill folder (amazon-skills/youtube/qiongqi/sealeap-qiongqi-amazon-ai-keyword-research-workflow in xjli360/sealeap-amazon-skills) into .agents/skills/sealeap-qiongqi-amazon-ai-keyword-research-workflow in your project. Codex loads it when a task matches its description.
Can I use Sealeap Qiongqi Amazon AI Keyword Research Workflow 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-ai-keyword-research-workflow -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-ai-keyword-research-workflow, .gemini/skills/sealeap-qiongqi-amazon-ai-keyword-research-workflow, .github/skills/sealeap-qiongqi-amazon-ai-keyword-research-workflow and .opencode/skills/sealeap-qiongqi-amazon-ai-keyword-research-workflow in your project.
What does Sealeap Qiongqi Amazon AI Keyword Research Workflow need to run?
Going by SKILL.md and its folder, Sealeap Qiongqi Amazon AI Keyword Research Workflow needs Python for the scripts in its folder. Our summary lists: Python 3.
Does Sealeap Qiongqi Amazon AI Keyword Research Workflow 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 AI Keyword Research Workflow 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 AI Keyword Research Workflow use?
Sealeap Qiongqi Amazon AI Keyword Research Workflow 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 AI Keyword Research Workflow use?
About 685 tokens (SKILL.md is roughly 2.7k 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 AI Keyword Research Workflow?
Skills that share tags, products or a category with Sealeap Qiongqi Amazon AI Keyword Research Workflow: Evaluate Skill (every-app/open-seo, 23k stars), SEO Dataforseo (AgriciDaniel/codex-seo, 799 stars), Competitor Gap (Ryze-AI-Adgent/open-seo-mcp-skills, 4.7k stars) and 90 Day SEO Sprint (Bomx/distribb-skill, 197 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 AI Keyword Research Workflow?
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.