Zsxq
unnoo/zsxq-skill
知识星球 CLI（zsxq-cli）与底层接口完整操作指南，涵盖星球和内容管理、Skill Pay 微信支付场景。当用户提到知识星球、zsxq、小密圈、星球、登录/认证、发帖、评论、回答、编辑、删除主题、定时发布/定时任务/定时回答、投票、问答主题、markdown 正文、AI…
Agent skill
Sealeap Fenghuang Amazon Product Exclusion Review Mining
Screen product ideas for the US marketplace with an exclusion list—too cheap, trend-driven, seasonal, restricted—then mine competitor listings and low-star reviews for fixable defects, and confirm…
Install Sealeap Fenghuang Amazon Product Exclusion Review Mining
$ npx skills add xjli360/sealeap-amazon-skills --skill sealeap-fenghuang-amazon-product-exclusion-review-mining -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install xjli360/sealeap-amazon-skills sealeap-fenghuang-amazon-product-exclusion-review-mining --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/fenghuang/sealeap-fenghuang-amazon-product-exclusion-review-mining .claude/skills/sealeap-fenghuang-amazon-product-exclusion-review-mining && 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-fenghuang-amazon-product-exclusion-review-mining" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/youtube/fenghuang/sealeap-fenghuang-amazon-product-exclusion-review-mining into .claude/skills/sealeap-fenghuang-amazon-product-exclusion-review-mining/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-fenghuang-amazon-product-exclusion-review-mining", 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/fenghuang/sealeap-fenghuang-amazon-product-exclusion-review-miningType 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-fenghuang-amazon-product-exclusion-review-mining -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install xjli360/sealeap-amazon-skills sealeap-fenghuang-amazon-product-exclusion-review-mining --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/fenghuang/sealeap-fenghuang-amazon-product-exclusion-review-mining .agents/skills/sealeap-fenghuang-amazon-product-exclusion-review-mining && 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-fenghuang-amazon-product-exclusion-review-mining" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/youtube/fenghuang/sealeap-fenghuang-amazon-product-exclusion-review-mining into .agents/skills/sealeap-fenghuang-amazon-product-exclusion-review-mining/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-fenghuang-amazon-product-exclusion-review-mining", 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-fenghuang-amazon-product-exclusion-review-mining -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install xjli360/sealeap-amazon-skills sealeap-fenghuang-amazon-product-exclusion-review-mining --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/fenghuang/sealeap-fenghuang-amazon-product-exclusion-review-mining .cursor/skills/sealeap-fenghuang-amazon-product-exclusion-review-mining && 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-fenghuang-amazon-product-exclusion-review-mining" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/youtube/fenghuang/sealeap-fenghuang-amazon-product-exclusion-review-mining into .cursor/skills/sealeap-fenghuang-amazon-product-exclusion-review-mining/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-fenghuang-amazon-product-exclusion-review-mining", 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/fenghuang/sealeap-fenghuang-amazon-product-exclusion-review-mining--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-fenghuang-amazon-product-exclusion-review-mining -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install xjli360/sealeap-amazon-skills sealeap-fenghuang-amazon-product-exclusion-review-mining --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/fenghuang/sealeap-fenghuang-amazon-product-exclusion-review-mining .gemini/skills/sealeap-fenghuang-amazon-product-exclusion-review-mining && 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-fenghuang-amazon-product-exclusion-review-mining" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/youtube/fenghuang/sealeap-fenghuang-amazon-product-exclusion-review-mining into .gemini/skills/sealeap-fenghuang-amazon-product-exclusion-review-mining/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-fenghuang-amazon-product-exclusion-review-mining", 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-fenghuang-amazon-product-exclusion-review-miningInstalls 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-fenghuang-amazon-product-exclusion-review-mining -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/fenghuang/sealeap-fenghuang-amazon-product-exclusion-review-mining .github/skills/sealeap-fenghuang-amazon-product-exclusion-review-mining && 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-fenghuang-amazon-product-exclusion-review-mining" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/youtube/fenghuang/sealeap-fenghuang-amazon-product-exclusion-review-mining into .github/skills/sealeap-fenghuang-amazon-product-exclusion-review-mining/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-fenghuang-amazon-product-exclusion-review-mining", 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-fenghuang-amazon-product-exclusion-review-mining -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-fenghuang-amazon-product-exclusion-review-mining --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/fenghuang/sealeap-fenghuang-amazon-product-exclusion-review-mining .opencode/skills/sealeap-fenghuang-amazon-product-exclusion-review-mining && 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-fenghuang-amazon-product-exclusion-review-mining" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/youtube/fenghuang/sealeap-fenghuang-amazon-product-exclusion-review-mining into .opencode/skills/sealeap-fenghuang-amazon-product-exclusion-review-mining/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-fenghuang-amazon-product-exclusion-review-mining", 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-fenghuang-amazon-product-exclusion-review-mining- GitHub stars
- 251
- Token cost
- ~711 tokens
- SKILL.md length
- 151 words
- Files
- 5 (incl. scripts, references)
- Skills in repo
- 179
- Repo updated
- First seen
- Licence
- MIT
At a glance
Screen product ideas for the US marketplace with an exclusion list—too cheap, trend-driven, seasonal, restricted—then mine competitor listings and low-star reviews for fixable defects, and confirm…
- 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。 → …
- 什么产品不能做、低价产品能不能卖、季节品要不要做、看差评找改良点、竞品 Listing 有没有机会
- 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 Fenghuang Amazon Product Exclusion Review Mining is an agent skill from xjli360/sealeap-amazon-skills. Screen product ideas for the US marketplace with an exclusion list—too cheap, trend-driven, seasonal, restricted—then mine competitor listings and low-star reviews for fixable defects, and confirm that an existing audience is already buying before committing to a supplier. Use for 什么产品不能做、低价产品能不能卖、季节品要不要做、看差评找改良点、竞品 Listing 有没有机会. Do not use to approve restricted categories or to estimate market size without third-party or account data.
Its SKILL.md is about 710 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 Sales & Support, covering Customer feedback analysis. 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
- 什么产品不能做、低价产品能不能卖、季节品要不要做、看差评找改良点、竞品 Listing 有没有机会
- Approve restricted categories
- Estimate market size without third-party
Example prompts
- “/sealeap-fenghuang-amazon-product-exclusion-review-mining”
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 Fenghuang Amazon Product Exclusion Review Mining loads about 711 tokens when it runs, and up to ~3.1k if it reads all its reference files. Until then it costs about 124 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, ~711 tokens.
Download SKILL.mdSave it as
.claude/skills/sealeap-fenghuang-amazon-product-exclusion-review-mining/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.- name
- sealeap-fenghuang-amazon-product-exclusion-review-mining
- description
- Screen product ideas for the US marketplace with an exclusion list—too cheap, trend-driven, seasonal, restricted—then mine competitor listings and low-star reviews for fixable defects, and confirm that an existing audience is already buying before committing to a supplier. Use for 什么产品不能做、低价产品能不能卖、季节品要不要做、看差评找改良点、竞品 Listing 有没有机会. Do not use to approve restricted categories or to estimate market size without third-party or account data.
Amazon 选品排除清单与差评改良验证
目标
Screen product ideas for the US marketplace with an exclusion list—too cheap, trend-driven, seasonal, restricted—then mine competitor listings and low-star reviews for fixable defects, and confirm that an existing audience is already buying before committing to a supplier.
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。
- 来源的价格带区间是个人经验，不同类目费用结构不同，必须按当前站点重新计算。
- 「差评一多销量就下滑」与「评论多就有市场」都是观察，不作定律；评论抓取受站点、变体与时间影响。
- 因运输破损产生的差评可按平台流程申请处理，结果不保证；不采用任何刷评或操纵评论的做法。
- 需求验证要用当前搜索量、销量估算与前台观测交叉，不依赖单一工具的机会评分。
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、类目、价格带、上架时间与运营模式
- 候选品与同购买意图可比样本的销量、评论、价格和上架时间
- 关键词需求、历史趋势、广告依赖、同款密度和品牌集中度
- 采购、头程、平台费、退货、仓储、交期和合规/IP 基础信息
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：
- 排除低价薄利品：用当前站点的佣金、FBA 费与预期 CPC 算单件利润，若几次点击就吃掉利润则不做；价格带上限以「买家能否冲动决策」和自有资金校准，来源区间仅作参考。
- 排除短期热点与强季节品：核对关键词搜索量的历史曲线，若需求窗口短于「生产 + 头程 + 入仓」周期，或全年只有一段有销量，则记为不做，并把长期仓储费风险写入判定。
- 排除受限与需资质品类：先查当前站点的品类审核与禁售清单，再确认是否需要认证、保险或额外预处理。
- 在通过排除的候选里，按评论数排序看头部竞品：评论多代表样本大、有现成买家；检查其标题是否遗漏核心搜索词、图片与 A+ 是否薄弱。
- 逐条读低星评论并分类：包装破损、口味或材质、尺寸误导、缺件等可改良项，与情绪化无信息评论分开；只保留可通过产品或包装解决的问题。
- 以「已有人在买 + 可改良点明确 + 供应商能落实改良」三条同时成立作为进入打样的条件，缺一则 HOLD。
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：获取竞品 Listing、评论样本与关键词需求趋势的第三方代理证据。
- 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
- 排除清单判定表
- 候选品单件利润测算
- 竞品差评分类表
- 可改良点清单
- 进入打样判定
- 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/fenghuang/sealeap-fenghuang-amazon-product-exclusion-review-mining 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 Fenghuang Amazon Product Exclusion Review Mining 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 Fenghuang Amazon Product Exclusion Review Mining this skillxjli360/sealeap-amazon-skills | 251 | — | ~711 | Automated safety check: Pass | MIT | |
| Zsxqunnoo/zsxq-skill | 304 | — | ~3.8k | Automated safety check: Pass | MIT | |
| Creating SurveysPostHog/posthog | 40k | — | ~1.6k | Automated safety check: Pass | Custom licence | |
| Sales OsromangojiberryAI/gojiberryai-sales-os | 139 | — | ~2k | Automated safety check: Pass | MIT | |
| Zach Sif Cvr Threshold Analyzerzach22-1999/amazon-skills | 209 | 1 repos | ~982 | Automated safety check: Notes | MIT | |
| Call Prepbreakstageaxe61/genspark-claw | 169 | — | ~986 | Automated safety check: Pass | MIT |
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Questions about Sealeap Fenghuang Amazon Product Exclusion Review Mining
What does Sealeap Fenghuang Amazon Product Exclusion Review Mining do?
Screen product ideas for the US marketplace with an exclusion list—too cheap, trend-driven, seasonal, restricted—then mine competitor listings and low-star reviews for fixable defects, and confirm…. Sealeap Fenghuang Amazon Product Exclusion Review Mining is an agent skill from xjli360/sealeap-amazon-skills. Screen product ideas for the US marketplace with an exclusion list—too cheap, trend-driven, seasonal, restricted—then mine competitor listings and low-star reviews for fixable defects, and confirm that an existing audience is already buying before committing to a supplier.
When should I use Sealeap Fenghuang Amazon Product Exclusion Review Mining?
Sealeap Fenghuang Amazon Product Exclusion Review Mining fits situations like: 什么产品不能做、低价产品能不能卖、季节品要不要做、看差评找改良点、竞品 Listing 有没有机会; approve restricted categories; estimate market size without third-party.
How do I install Sealeap Fenghuang Amazon Product Exclusion Review Mining in Claude Code?
Run `npx skills add xjli360/sealeap-amazon-skills --skill sealeap-fenghuang-amazon-product-exclusion-review-mining -a claude-code`. Or copy the skill folder (amazon-skills/youtube/fenghuang/sealeap-fenghuang-amazon-product-exclusion-review-mining in xjli360/sealeap-amazon-skills) into .claude/skills/sealeap-fenghuang-amazon-product-exclusion-review-mining in your project. Claude Code loads it when a task matches its description.
How do I install Sealeap Fenghuang Amazon Product Exclusion Review Mining in Codex?
Run `npx skills add xjli360/sealeap-amazon-skills --skill sealeap-fenghuang-amazon-product-exclusion-review-mining -a codex`. Or copy the skill folder (amazon-skills/youtube/fenghuang/sealeap-fenghuang-amazon-product-exclusion-review-mining in xjli360/sealeap-amazon-skills) into .agents/skills/sealeap-fenghuang-amazon-product-exclusion-review-mining in your project. Codex loads it when a task matches its description.
Can I use Sealeap Fenghuang Amazon Product Exclusion Review Mining 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-fenghuang-amazon-product-exclusion-review-mining -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-fenghuang-amazon-product-exclusion-review-mining, .gemini/skills/sealeap-fenghuang-amazon-product-exclusion-review-mining, .github/skills/sealeap-fenghuang-amazon-product-exclusion-review-mining and .opencode/skills/sealeap-fenghuang-amazon-product-exclusion-review-mining in your project.
What does Sealeap Fenghuang Amazon Product Exclusion Review Mining need to run?
Going by SKILL.md and its folder, Sealeap Fenghuang Amazon Product Exclusion Review Mining needs Python for the scripts in its folder. Our summary lists: Python 3.
Does Sealeap Fenghuang Amazon Product Exclusion Review Mining 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 Fenghuang Amazon Product Exclusion Review Mining 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 Fenghuang Amazon Product Exclusion Review Mining use?
Sealeap Fenghuang Amazon Product Exclusion Review Mining 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 Fenghuang Amazon Product Exclusion Review Mining use?
About 711 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 Fenghuang Amazon Product Exclusion Review Mining?
Skills that share tags, products or a category with Sealeap Fenghuang Amazon Product Exclusion Review Mining: Zsxq (unnoo/zsxq-skill, 304 stars), Creating Surveys (PostHog/posthog, 40k stars), Sales Os (romangojiberryAI/gojiberryai-sales-os, 139 stars) and Zach Sif Cvr Threshold Analyzer (zach22-1999/amazon-skills, 209 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Sealeap Fenghuang Amazon Product Exclusion Review Mining?
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.