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 Bifang Amazon Converting Term Discovery
Discover and tier keywords for an Amazon product by combining long-tail expansion, search-suggestion checks, related-term tools and Brand Analytics based converting-term lookups on comparable ASINs…
MITAuto-check passed
Install Sealeap Bifang Amazon Converting Term Discovery
$ npx skills add xjli360/sealeap-amazon-skills --skill sealeap-bifang-amazon-converting-term-discovery -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install xjli360/sealeap-amazon-skills sealeap-bifang-amazon-converting-term-discovery --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/bilibili/bifang/sealeap-bifang-amazon-converting-term-discovery .claude/skills/sealeap-bifang-amazon-converting-term-discovery && 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-bifang-amazon-converting-term-discovery" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/bilibili/bifang/sealeap-bifang-amazon-converting-term-discovery into .claude/skills/sealeap-bifang-amazon-converting-term-discovery/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-bifang-amazon-converting-term-discovery", 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/bilibili/bifang/sealeap-bifang-amazon-converting-term-discoveryType 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-bifang-amazon-converting-term-discovery -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install xjli360/sealeap-amazon-skills sealeap-bifang-amazon-converting-term-discovery --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/bilibili/bifang/sealeap-bifang-amazon-converting-term-discovery .agents/skills/sealeap-bifang-amazon-converting-term-discovery && 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-bifang-amazon-converting-term-discovery" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/bilibili/bifang/sealeap-bifang-amazon-converting-term-discovery into .agents/skills/sealeap-bifang-amazon-converting-term-discovery/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-bifang-amazon-converting-term-discovery", 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-bifang-amazon-converting-term-discovery -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install xjli360/sealeap-amazon-skills sealeap-bifang-amazon-converting-term-discovery --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/bilibili/bifang/sealeap-bifang-amazon-converting-term-discovery .cursor/skills/sealeap-bifang-amazon-converting-term-discovery && 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-bifang-amazon-converting-term-discovery" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/bilibili/bifang/sealeap-bifang-amazon-converting-term-discovery into .cursor/skills/sealeap-bifang-amazon-converting-term-discovery/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-bifang-amazon-converting-term-discovery", 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/bilibili/bifang/sealeap-bifang-amazon-converting-term-discovery--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-bifang-amazon-converting-term-discovery -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install xjli360/sealeap-amazon-skills sealeap-bifang-amazon-converting-term-discovery --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/bilibili/bifang/sealeap-bifang-amazon-converting-term-discovery .gemini/skills/sealeap-bifang-amazon-converting-term-discovery && 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-bifang-amazon-converting-term-discovery" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/bilibili/bifang/sealeap-bifang-amazon-converting-term-discovery into .gemini/skills/sealeap-bifang-amazon-converting-term-discovery/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-bifang-amazon-converting-term-discovery", 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-bifang-amazon-converting-term-discoveryInstalls 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-bifang-amazon-converting-term-discovery -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/bilibili/bifang/sealeap-bifang-amazon-converting-term-discovery .github/skills/sealeap-bifang-amazon-converting-term-discovery && 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-bifang-amazon-converting-term-discovery" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/bilibili/bifang/sealeap-bifang-amazon-converting-term-discovery into .github/skills/sealeap-bifang-amazon-converting-term-discovery/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-bifang-amazon-converting-term-discovery", 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-bifang-amazon-converting-term-discovery -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-bifang-amazon-converting-term-discovery --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/bilibili/bifang/sealeap-bifang-amazon-converting-term-discovery .opencode/skills/sealeap-bifang-amazon-converting-term-discovery && 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-bifang-amazon-converting-term-discovery" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/bilibili/bifang/sealeap-bifang-amazon-converting-term-discovery into .opencode/skills/sealeap-bifang-amazon-converting-term-discovery/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-bifang-amazon-converting-term-discovery", 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-bifang-amazon-converting-term-discovery- GitHub stars
- 251
- Token cost
- ~810 tokens
- SKILL.md length
- 167 words
- Files
- 5 (incl. scripts, references)
- Skills in repo
- 179
- Repo updated
- First seen
- Licence
- MIT
At a glance
Discover and tier keywords for an Amazon product by combining long-tail expansion, search-suggestion checks, related-term tools and Brand Analytics based converting-term lookups on comparable ASINs…
- 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。 → …
- 出单词怎么找、流量词和关键词的区别、下拉框找词、相关词工具、点击份额转化份额怎么看、关键词分组投放
- 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 Bifang Amazon Converting Term Discovery is an agent skill from xjli360/sealeap-amazon-skills. Discover and tier keywords for an Amazon product by combining long-tail expansion, search-suggestion checks, related-term tools and Brand Analytics based converting-term lookups on comparable ASINs, then separate static identity keywords from time-varying traffic terms, group them into manual campaigns and re-check them on a fixed reporting cadence. Use for 出单词怎么找、流量词和关键词的区别、下拉框找词、相关词工具、点击份额转化份额怎么看、关键词分组投放. Do not use to create or edit live campaigns without approval, or to treat third-party share estimates as…
Its SKILL.md is about 810 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
- 出单词怎么找、流量词和关键词的区别、下拉框找词、相关词工具、点击份额转化份额怎么看、关键词分组投放
- Edit live campaigns without approval
- Treat third-party share estimates as Amazon first-party data
Example prompts
- “/sealeap-bifang-amazon-converting-term-discovery”
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 Bifang Amazon Converting Term Discovery loads about 810 tokens when it runs, and up to ~3.3k if it reads all its reference files. Until then it costs about 147 tokens; SKILL.md has 167 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). 167 words, ~810 tokens.
Download SKILL.mdSave it as
.claude/skills/sealeap-bifang-amazon-converting-term-discovery/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.- name
- sealeap-bifang-amazon-converting-term-discovery
- description
- Discover and tier keywords for an Amazon product by combining long-tail expansion, search-suggestion checks, related-term tools and Brand Analytics based converting-term lookups on comparable ASINs, then separate static identity keywords from time-varying traffic terms, group them into manual campaigns and re-check them on a fixed reporting cadence. Use for 出单词怎么找、流量词和关键词的区别、下拉框找词、相关词工具、点击份额转化份额怎么看、关键词分组投放. Do not use to create or edit live campaigns without approval, or to treat third-party share estimates as Amazon first-party data.
Amazon 出单词反查与关键词分层
目标
Discover and tier keywords for an Amazon product by combining long-tail expansion, search-suggestion checks, related-term tools and Brand Analytics based converting-term lookups on comparable ASINs, then separate static identity keywords from time-varying traffic terms, group them into manual campaigns and re-check them on a fixed reporting cadence.
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。
- 来源给出的观察周期（数周级初评、更长周期确认）是经验值，以当前广告归因窗与点击样本量校准。
- “点击份额/转化份额过高即红海应避开”是来源对中小卖家的建议；资金与目标不同的卖家结论不同，需结合自身约束判断。
- 第三方反查的份额、排名与搜索量均为估算或对官方数据的再加工，标为 ESTIMATE；官方 Brand Analytics 数据本身有周度口径与站点限制。
- “出单词随时间变化”需用自身账户的搜索词报告验证，不外推为平台规律。
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、产品事实、ASIN/SKU 与目标购买意图
- 本品和可比竞品的关键词、自然位置、广告可见度与采样时间
- 搜索词报告、转化、CPC、订单、利润和 Listing 当前覆盖
- 站点语言、变体、价格、库存与同期促销记录
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：
- 固定产品事实与目标购买意图，先写出主词（产品名称级别的词）；主词不确定时用搜索结果页与可比 ASIN 的标题核对，不凭感觉。
- 四路来源拉词：①以主词做长尾拓展；②在站点搜索框及细分类目下查看下拉推荐词并记录出现频次；③用相关词/同义词工具把功能诉求换一种说法（用途词、场景词）；④对若干可比 ASIN 做出单词反查（基于 Brand Analytics 的周度数据），只保留有点击与转化记录的词。
- 读份额指标判断竞争：对每个出单词看点击份额、转化份额与前几名 ASIN 的合计占比；占比过高的词意味着被少数 ASIN 垄断，小卖家优先选择份额分散的词，垄断判定的具体比例按当前类目分布校准。
- 清洗与分层：去掉无意义停用词与他人品牌词，按与产品事实的匹配度打分，再按搜索量层级分成主词、精准长尾、补充词；把“标识产品的静态词”（用于收录与索引）和“带来订单的流量词”（会随时间变化）分别标注。
- 投放结构：按层级建手动广告组，前期用固定且不激进的竞价；累计足够点击后用广告报告看各词点击与转化，表现差的降价或否定，表现好的独立成组提高精准竞价以稳定位置。
- 周期校正：设定固定的报告下载与复盘周期，跟踪流量词是否变化、是否出现新出单词并回填清单；每次调整写入变更记录，只在账户内证据支持时改变分层。
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：获取可比 ASIN 的出单词、点击/转化份额与相关词代理数据。
- 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
- 四路来源合并的候选词表（含来源与频次）
- 出单词份额与垄断度读表
- 分层关键词清单（静态索引词/流量词标注）
- 手动广告组结构草案与起始竞价表
- 周期复盘与变更记录
- 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/bilibili/bifang/sealeap-bifang-amazon-converting-term-discovery 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 Bifang Amazon Converting Term Discovery 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 Bifang Amazon Converting Term Discovery this skillxjli360/sealeap-amazon-skills | 251 | — | ~810 | 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 |
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Builds walkthrough videos from Stitch design projects using Remotion, with transitions, zoom effects and text overlays on each screen.
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A skill your agent uses for Laravel MCP development. An agent skill from coollabsio/coolify.
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Questions about Sealeap Bifang Amazon Converting Term Discovery
What does Sealeap Bifang Amazon Converting Term Discovery do?
Discover and tier keywords for an Amazon product by combining long-tail expansion, search-suggestion checks, related-term tools and Brand Analytics based converting-term lookups on comparable ASINs…. Sealeap Bifang Amazon Converting Term Discovery is an agent skill from xjli360/sealeap-amazon-skills. Discover and tier keywords for an Amazon product by combining long-tail expansion, search-suggestion checks, related-term tools and Brand Analytics based converting-term lookups on comparable ASINs, then separate static identity keywords from time-varying traffic terms, group them into manual campaigns and re-check them on a fixed reporting cadence.
When should I use Sealeap Bifang Amazon Converting Term Discovery?
Sealeap Bifang Amazon Converting Term Discovery fits situations like: 出单词怎么找、流量词和关键词的区别、下拉框找词、相关词工具、点击份额转化份额怎么看、关键词分组投放; edit live campaigns without approval; treat third-party share estimates as Amazon first-party data.
How do I install Sealeap Bifang Amazon Converting Term Discovery in Claude Code?
Run `npx skills add xjli360/sealeap-amazon-skills --skill sealeap-bifang-amazon-converting-term-discovery -a claude-code`. Or copy the skill folder (amazon-skills/bilibili/bifang/sealeap-bifang-amazon-converting-term-discovery in xjli360/sealeap-amazon-skills) into .claude/skills/sealeap-bifang-amazon-converting-term-discovery in your project. Claude Code loads it when a task matches its description.
How do I install Sealeap Bifang Amazon Converting Term Discovery in Codex?
Run `npx skills add xjli360/sealeap-amazon-skills --skill sealeap-bifang-amazon-converting-term-discovery -a codex`. Or copy the skill folder (amazon-skills/bilibili/bifang/sealeap-bifang-amazon-converting-term-discovery in xjli360/sealeap-amazon-skills) into .agents/skills/sealeap-bifang-amazon-converting-term-discovery in your project. Codex loads it when a task matches its description.
Can I use Sealeap Bifang Amazon Converting Term Discovery 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-bifang-amazon-converting-term-discovery -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-bifang-amazon-converting-term-discovery, .gemini/skills/sealeap-bifang-amazon-converting-term-discovery, .github/skills/sealeap-bifang-amazon-converting-term-discovery and .opencode/skills/sealeap-bifang-amazon-converting-term-discovery in your project.
What does Sealeap Bifang Amazon Converting Term Discovery need to run?
Going by SKILL.md and its folder, Sealeap Bifang Amazon Converting Term Discovery needs Python for the scripts in its folder. Our summary lists: Python 3.
Does Sealeap Bifang Amazon Converting Term Discovery 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 Bifang Amazon Converting Term Discovery 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 Bifang Amazon Converting Term Discovery use?
Sealeap Bifang Amazon Converting Term Discovery 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 Bifang Amazon Converting Term Discovery use?
About 810 tokens (SKILL.md is roughly 3.2k 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 2.5k tokens, read only when the agent opens those files.
What are the alternatives to Sealeap Bifang Amazon Converting Term Discovery?
Skills that share tags, products or a category with Sealeap Bifang Amazon Converting Term Discovery: 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 Bifang Amazon Converting Term Discovery?
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.