Ab Test Analyzer
irinabuht12-oss/marketing-skills
Statistical significance calculator for A/B test results with sample size requirements, segment breakdowns, and hypothesis generation.
Agent skill
Sealeap Dijiang Amazon Review Mined Image Iteration
Mine competitor reviews with AI assistance to surface recurring pain points and desired improvements, benchmark the main image against top competitors, validate candidate images with quick…
Install Sealeap Dijiang Amazon Review Mined Image Iteration
$ npx skills add xjli360/sealeap-amazon-skills --skill sealeap-dijiang-amazon-review-mined-image-iteration -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install xjli360/sealeap-amazon-skills sealeap-dijiang-amazon-review-mined-image-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/dijiang/sealeap-dijiang-amazon-review-mined-image-iteration .claude/skills/sealeap-dijiang-amazon-review-mined-image-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-dijiang-amazon-review-mined-image-iteration" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/youtube/dijiang/sealeap-dijiang-amazon-review-mined-image-iteration into .claude/skills/sealeap-dijiang-amazon-review-mined-image-iteration/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-dijiang-amazon-review-mined-image-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/dijiang/sealeap-dijiang-amazon-review-mined-image-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-dijiang-amazon-review-mined-image-iteration -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install xjli360/sealeap-amazon-skills sealeap-dijiang-amazon-review-mined-image-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/dijiang/sealeap-dijiang-amazon-review-mined-image-iteration .agents/skills/sealeap-dijiang-amazon-review-mined-image-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-dijiang-amazon-review-mined-image-iteration" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/youtube/dijiang/sealeap-dijiang-amazon-review-mined-image-iteration into .agents/skills/sealeap-dijiang-amazon-review-mined-image-iteration/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-dijiang-amazon-review-mined-image-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-dijiang-amazon-review-mined-image-iteration -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install xjli360/sealeap-amazon-skills sealeap-dijiang-amazon-review-mined-image-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/dijiang/sealeap-dijiang-amazon-review-mined-image-iteration .cursor/skills/sealeap-dijiang-amazon-review-mined-image-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-dijiang-amazon-review-mined-image-iteration" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/youtube/dijiang/sealeap-dijiang-amazon-review-mined-image-iteration into .cursor/skills/sealeap-dijiang-amazon-review-mined-image-iteration/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-dijiang-amazon-review-mined-image-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/dijiang/sealeap-dijiang-amazon-review-mined-image-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-dijiang-amazon-review-mined-image-iteration -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install xjli360/sealeap-amazon-skills sealeap-dijiang-amazon-review-mined-image-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/dijiang/sealeap-dijiang-amazon-review-mined-image-iteration .gemini/skills/sealeap-dijiang-amazon-review-mined-image-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-dijiang-amazon-review-mined-image-iteration" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/youtube/dijiang/sealeap-dijiang-amazon-review-mined-image-iteration into .gemini/skills/sealeap-dijiang-amazon-review-mined-image-iteration/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-dijiang-amazon-review-mined-image-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-dijiang-amazon-review-mined-image-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-dijiang-amazon-review-mined-image-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/dijiang/sealeap-dijiang-amazon-review-mined-image-iteration .github/skills/sealeap-dijiang-amazon-review-mined-image-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-dijiang-amazon-review-mined-image-iteration" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/youtube/dijiang/sealeap-dijiang-amazon-review-mined-image-iteration into .github/skills/sealeap-dijiang-amazon-review-mined-image-iteration/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-dijiang-amazon-review-mined-image-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-dijiang-amazon-review-mined-image-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-dijiang-amazon-review-mined-image-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/dijiang/sealeap-dijiang-amazon-review-mined-image-iteration .opencode/skills/sealeap-dijiang-amazon-review-mined-image-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-dijiang-amazon-review-mined-image-iteration" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/youtube/dijiang/sealeap-dijiang-amazon-review-mined-image-iteration into .opencode/skills/sealeap-dijiang-amazon-review-mined-image-iteration/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-dijiang-amazon-review-mined-image-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-dijiang-amazon-review-mined-image-iteration- GitHub stars
- 251
- Token cost
- ~748 tokens
- SKILL.md length
- 142 words
- Files
- 5 (incl. scripts, references)
- Skills in repo
- 179
- Repo updated
- First seen
- Licence
- MIT
At a glance
Mine competitor reviews with AI assistance to surface recurring pain points and desired improvements, benchmark the main image against top competitors, validate candidate images with quick…
- 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。 → …
- 主图怎么迭代、怎么从评论里找卖点、图片该不该做分屏测试、主图要不要参考竞品
- 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 Dijiang Amazon Review Mined Image Iteration is an agent skill from xjli360/sealeap-amazon-skills. Mine competitor reviews with AI assistance to surface recurring pain points and desired improvements, benchmark the main image against top competitors, validate candidate images with quick naive-viewer reaction tests, and let controlled split-test results, not internal intuition, decide which version ships. Use for 主图怎么迭代、怎么从评论里找卖点、图片该不该做分屏测试、主图要不要参考竞品. Do not use to add on-image or on-render claims and text that are not actually true of the shipped product.
Its SKILL.md is about 750 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 A/B testing. 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
- 主图怎么迭代、怎么从评论里找卖点、图片该不该做分屏测试、主图要不要参考竞品
- On-render claims and text that are not actually true of the shipped product
Example prompts
- “/sealeap-dijiang-amazon-review-mined-image-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 Dijiang Amazon Review Mined Image Iteration loads about 748 tokens when it runs, and up to ~3.2k if it reads all its reference files. Until then it costs about 129 tokens; SKILL.md has 142 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). 142 words, ~748 tokens.
Download SKILL.mdSave it as
.claude/skills/sealeap-dijiang-amazon-review-mined-image-iteration/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.- name
- sealeap-dijiang-amazon-review-mined-image-iteration
- description
- Mine competitor reviews with AI assistance to surface recurring pain points and desired improvements, benchmark the main image against top competitors, validate candidate images with quick naive-viewer reaction tests, and let controlled split-test results, not internal intuition, decide which version ships. Use for 主图怎么迭代、怎么从评论里找卖点、图片该不该做分屏测试、主图要不要参考竞品. Do not use to add on-image or on-render claims and text that are not actually true of the shipped product.
Amazon 评论痛点驱动主图迭代
目标
Mine competitor reviews with AI assistance to surface recurring pain points and desired improvements, benchmark the main image against top competitors, validate candidate images with quick naive-viewer reaction tests, and let controlled split-test results, not internal intuition, decide which version ships.
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批量评论摘要存在误判与遗漏，重大素材改版前应人工抽查原始评论确认摘要没有扭曲原意。
- 直觉测试样本量小、参与者背景单一，只能作为方向性参考，不能替代真实流量下的分屏测试结论。
- 主图与效果图必须与实物一致，把产品本身不具备的文字、认证或功能标识加到渲染图上属于不采用的做法，即使短期未被追责也存在合规与售后风险。
- 同等转化率下价格更高的商品更容易被平台优先展示这类因果表述属于未经证实的假设，不作为定价决策的确定依据。
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：
- 用AI辅助工具批量扫描竞品的评论内容，归纳出高频出现的抱怨点与好评点，区分材质结构类可改进项与包装说明类体验项，作为素材迭代的原始输入，而不是凭主观猜测消费者在意什么。
- 把归纳出的真实痛点与自身产品的实际能力对照：产品本身就能解决的痛点在主图与文案里明确呈现；产品目前不具备的能力，不通过渲染图伪造出与实物不符的文字或标识来暗示具备。
- 制作主图前用同类竞品的主图做并排对比，观察头部竞品在留白、卖点呈现方式上的共性，作为设计方向参考，而非直接照搬某一竞品的具体版式。
- 素材定稿前找一批不了解产品背景的人做快速直觉测试：只给几秒钟看图，之后询问能否说出这是什么产品、解决什么问题，如果多数人说不出来，说明图片信息传达不清晰，需要重新设计。
- 上线新素材后用分屏测试收集实际点击与转化数据，测试结果与内部主观判断不一致时以数据结果为准，回到上一步重新分析原因，而不是坚持看起来更好看的版本。
- 把每一轮痛点挖掘、竞品对标、直觉测试与分屏测试结果都留痕，作为下一轮迭代的起点，避免重复测试已经验证过的方向。
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：批量抓取竞品评论用于AI痛点归纳，为主图与文案迭代提供证据来源。
- 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
- 竞品评论痛点/好评归纳清单
- 主图竞品对标分析
- 直觉测试反馈记录
- 分屏测试结果与迭代结论
- 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/dijiang/sealeap-dijiang-amazon-review-mined-image-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 Dijiang Amazon Review Mined Image 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 Dijiang Amazon Review Mined Image Iteration this skillxjli360/sealeap-amazon-skills | 251 | — | ~748 | Automated safety check: Pass | MIT | |
| Ab Test Analyzeririnabuht12-oss/marketing-skills | 4.1k | — | ~1.4k | Automated safety check: Pass | None | |
| Scenario Model Comparisonscenario-labs/skills | 946 | — | ~2.6k | Automated safety check: Pass | MIT | |
| Continue Projectglifxyz/glif-mcp-server | 213 | — | ~734 | Automated safety check: Pass | MIT | |
| Ab Testingcoreyhaines31/marketingskills | 54k | 3 repos | ~3.1k | Automated safety check: Pass | MIT | |
| AnalyticsNexus-JPF/note-companion | 870 | 7 repos | ~2.2k | Automated safety check: Pass | MIT |
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Questions about Sealeap Dijiang Amazon Review Mined Image Iteration
What does Sealeap Dijiang Amazon Review Mined Image Iteration do?
Mine competitor reviews with AI assistance to surface recurring pain points and desired improvements, benchmark the main image against top competitors, validate candidate images with quick…. Sealeap Dijiang Amazon Review Mined Image Iteration is an agent skill from xjli360/sealeap-amazon-skills. Mine competitor reviews with AI assistance to surface recurring pain points and desired improvements, benchmark the main image against top competitors, validate candidate images with quick naive-viewer reaction tests, and let controlled split-test results, not internal intuition, decide which version ships.
When should I use Sealeap Dijiang Amazon Review Mined Image Iteration?
Sealeap Dijiang Amazon Review Mined Image Iteration fits situations like: 主图怎么迭代、怎么从评论里找卖点、图片该不该做分屏测试、主图要不要参考竞品; on-render claims and text that are not actually true of the shipped product.
How do I install Sealeap Dijiang Amazon Review Mined Image Iteration in Claude Code?
Run `npx skills add xjli360/sealeap-amazon-skills --skill sealeap-dijiang-amazon-review-mined-image-iteration -a claude-code`. Or copy the skill folder (amazon-skills/youtube/dijiang/sealeap-dijiang-amazon-review-mined-image-iteration in xjli360/sealeap-amazon-skills) into .claude/skills/sealeap-dijiang-amazon-review-mined-image-iteration in your project. Claude Code loads it when a task matches its description.
How do I install Sealeap Dijiang Amazon Review Mined Image Iteration in Codex?
Run `npx skills add xjli360/sealeap-amazon-skills --skill sealeap-dijiang-amazon-review-mined-image-iteration -a codex`. Or copy the skill folder (amazon-skills/youtube/dijiang/sealeap-dijiang-amazon-review-mined-image-iteration in xjli360/sealeap-amazon-skills) into .agents/skills/sealeap-dijiang-amazon-review-mined-image-iteration in your project. Codex loads it when a task matches its description.
Can I use Sealeap Dijiang Amazon Review Mined Image 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-dijiang-amazon-review-mined-image-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-dijiang-amazon-review-mined-image-iteration, .gemini/skills/sealeap-dijiang-amazon-review-mined-image-iteration, .github/skills/sealeap-dijiang-amazon-review-mined-image-iteration and .opencode/skills/sealeap-dijiang-amazon-review-mined-image-iteration in your project.
What does Sealeap Dijiang Amazon Review Mined Image Iteration need to run?
Going by SKILL.md and its folder, Sealeap Dijiang Amazon Review Mined Image Iteration needs Python for the scripts in its folder. Our summary lists: Python 3.
Does Sealeap Dijiang Amazon Review Mined Image 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 Dijiang Amazon Review Mined Image 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 Dijiang Amazon Review Mined Image Iteration use?
Sealeap Dijiang Amazon Review Mined Image 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 Dijiang Amazon Review Mined Image Iteration use?
About 748 tokens (SKILL.md is roughly 3k 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 2.4k tokens, read only when the agent opens those files.
What are the alternatives to Sealeap Dijiang Amazon Review Mined Image Iteration?
Skills that share tags, products or a category with Sealeap Dijiang Amazon Review Mined Image Iteration: Ab Test Analyzer (irinabuht12-oss/marketing-skills, 4.1k stars), Scenario Model Comparison (scenario-labs/skills, 946 stars), Continue Project (glifxyz/glif-mcp-server, 213 stars) and Ab Testing (coreyhaines31/marketingskills, 54k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Sealeap Dijiang Amazon Review Mined Image 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.