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Agent skill
Sealeap Baxia Amazon Weather Driven Selection Scan
Screen a weather- or event-triggered demand spike for Amazon product opportunities by checking local supply-capacity gaps, installation or usage-friction barriers, and one's own supply-chain…
Install Sealeap Baxia Amazon Weather Driven Selection Scan
$ npx skills add xjli360/sealeap-amazon-skills --skill sealeap-baxia-amazon-weather-driven-selection-scan -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install xjli360/sealeap-amazon-skills sealeap-baxia-amazon-weather-driven-selection-scan --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/weixin/baxia/sealeap-baxia-amazon-weather-driven-selection-scan .claude/skills/sealeap-baxia-amazon-weather-driven-selection-scan && 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-baxia-amazon-weather-driven-selection-scan" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/weixin/baxia/sealeap-baxia-amazon-weather-driven-selection-scan into .claude/skills/sealeap-baxia-amazon-weather-driven-selection-scan/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-baxia-amazon-weather-driven-selection-scan", 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/weixin/baxia/sealeap-baxia-amazon-weather-driven-selection-scanType 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-baxia-amazon-weather-driven-selection-scan -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install xjli360/sealeap-amazon-skills sealeap-baxia-amazon-weather-driven-selection-scan --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/weixin/baxia/sealeap-baxia-amazon-weather-driven-selection-scan .agents/skills/sealeap-baxia-amazon-weather-driven-selection-scan && 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-baxia-amazon-weather-driven-selection-scan" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/weixin/baxia/sealeap-baxia-amazon-weather-driven-selection-scan into .agents/skills/sealeap-baxia-amazon-weather-driven-selection-scan/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-baxia-amazon-weather-driven-selection-scan", 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-baxia-amazon-weather-driven-selection-scan -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install xjli360/sealeap-amazon-skills sealeap-baxia-amazon-weather-driven-selection-scan --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/weixin/baxia/sealeap-baxia-amazon-weather-driven-selection-scan .cursor/skills/sealeap-baxia-amazon-weather-driven-selection-scan && 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-baxia-amazon-weather-driven-selection-scan" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/weixin/baxia/sealeap-baxia-amazon-weather-driven-selection-scan into .cursor/skills/sealeap-baxia-amazon-weather-driven-selection-scan/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-baxia-amazon-weather-driven-selection-scan", 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/weixin/baxia/sealeap-baxia-amazon-weather-driven-selection-scan--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-baxia-amazon-weather-driven-selection-scan -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install xjli360/sealeap-amazon-skills sealeap-baxia-amazon-weather-driven-selection-scan --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/weixin/baxia/sealeap-baxia-amazon-weather-driven-selection-scan .gemini/skills/sealeap-baxia-amazon-weather-driven-selection-scan && 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-baxia-amazon-weather-driven-selection-scan" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/weixin/baxia/sealeap-baxia-amazon-weather-driven-selection-scan into .gemini/skills/sealeap-baxia-amazon-weather-driven-selection-scan/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-baxia-amazon-weather-driven-selection-scan", 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-baxia-amazon-weather-driven-selection-scanInstalls 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-baxia-amazon-weather-driven-selection-scan -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/weixin/baxia/sealeap-baxia-amazon-weather-driven-selection-scan .github/skills/sealeap-baxia-amazon-weather-driven-selection-scan && 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-baxia-amazon-weather-driven-selection-scan" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/weixin/baxia/sealeap-baxia-amazon-weather-driven-selection-scan into .github/skills/sealeap-baxia-amazon-weather-driven-selection-scan/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-baxia-amazon-weather-driven-selection-scan", 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-baxia-amazon-weather-driven-selection-scan -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-baxia-amazon-weather-driven-selection-scan --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/weixin/baxia/sealeap-baxia-amazon-weather-driven-selection-scan .opencode/skills/sealeap-baxia-amazon-weather-driven-selection-scan && 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-baxia-amazon-weather-driven-selection-scan" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/weixin/baxia/sealeap-baxia-amazon-weather-driven-selection-scan into .opencode/skills/sealeap-baxia-amazon-weather-driven-selection-scan/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-baxia-amazon-weather-driven-selection-scan", 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-baxia-amazon-weather-driven-selection-scan- GitHub stars
- 251
- Token cost
- ~666 tokens
- SKILL.md length
- 129 words
- Files
- 5 (incl. scripts, references)
- Skills in repo
- 179
- Repo updated
- First seen
- Licence
- MIT
At a glance
Screen a weather- or event-triggered demand spike for Amazon product opportunities by checking local supply-capacity gaps, installation or usage-friction barriers, and one's own supply-chain…
- 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。 → …
- 极端天气或突发事件带来的短期需求判断、季节性选品的供需缺口验证、追热点选品前的产能与合规评估
- 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 Baxia Amazon Weather Driven Selection Scan is an agent skill from xjli360/sealeap-amazon-skills. Screen a weather- or event-triggered demand spike for Amazon product opportunities by checking local supply-capacity gaps, installation or usage-friction barriers, and one's own supply-chain response speed before committing inventory. Use for 极端天气或突发事件带来的短期需求判断、季节性选品的供需缺口验证、追热点选品前的产能与合规评估. Do not use to commit large inventory purchases based solely on a short observation window without a clearance or markdown fallback plan.
Its SKILL.md is about 670 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 Security, covering Supply chain security. 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
- 极端天气或突发事件带来的短期需求判断、季节性选品的供需缺口验证、追热点选品前的产能与合规评估
- Commit large inventory purchases based solely on a short observation window without a clearance
- Markdown fallback plan
Example prompts
- “/sealeap-baxia-amazon-weather-driven-selection-scan”
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 Baxia Amazon Weather Driven Selection Scan loads about 666 tokens when it runs, and up to ~3k if it reads all its reference files. Until then it costs about 120 tokens; SKILL.md has 129 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). 129 words, ~666 tokens.
Download SKILL.mdSave it as
.claude/skills/sealeap-baxia-amazon-weather-driven-selection-scan/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.- name
- sealeap-baxia-amazon-weather-driven-selection-scan
- description
- Screen a weather- or event-triggered demand spike for Amazon product opportunities by checking local supply-capacity gaps, installation or usage-friction barriers, and one's own supply-chain response speed before committing inventory. Use for 极端天气或突发事件带来的短期需求判断、季节性选品的供需缺口验证、追热点选品前的产能与合规评估. Do not use to commit large inventory purchases based solely on a short observation window without a clearance or markdown fallback plan.
Amazon 极端天气选品排查
目标
Screen a weather- or event-triggered demand spike for Amazon product opportunities by checking local supply-capacity gaps, installation or usage-friction barriers, and one's own supply-chain response speed before committing inventory.
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。
- 供给缺口规模、产能数字与出口增速等是来源引用的公开统计口径，具体量级会随时间变化，判断时应查证当下最新数据而非沿用旧口径。
- 短期需求缺口能持续多久、是否会被本土产能追平，属于待验证假设，不能按单一季度的热销直接外推为长期机会。
- 涉及电器类等有安全认证要求的品类，供需缺口大不代表可以跳过认证流程，合规仍是前置条件。
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：
- 观察到极端天气或突发事件带来某类目搜索或销量异动时，先确认异动是否集中在少数相关品类而非全站噪音。
- 核实当地本土产能与市场需求的量级关系，判断是结构性供给缺口还是短期物流延迟，两者的应对方式不同。
- 对比自身候选产品与当地主流产品在安装门槛、使用便利性上的差异，判断是否存在真实的替代优势而不仅是价格更低。
- 评估自身供应链从接单到出货的响应速度，能否在需求窗口关闭前完成备货与上架，响应不上的品类不追。
- 小批量试单验证转化与复购，同时准备好需求回落后的降价清仓或转品预案，不因短期爆单一次性大量压货。
- 跟进过程中持续检查目的地站点的合规与认证要求，例如涉及电器类目的安全认证，确认满足后再放量。
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：补充目标市场的销量与出口等公开统计数据，用于验证供需缺口是否成立。
- 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
- 需求异动品类清单
- 供需缺口与竞争优势评估
- 供应链响应速度核验记录
- 试单与回退预案
- 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/weixin/baxia/sealeap-baxia-amazon-weather-driven-selection-scan 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 Baxia Amazon Weather Driven Selection Scan 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 Baxia Amazon Weather Driven Selection Scan this skillxjli360/sealeap-amazon-skills | 251 | — | ~666 | Automated safety check: Pass | MIT | |
| Plugin Scanneriflytek/skillhub | 5.2k | 2 repos | ~1.1k | Automated safety check: Notes | Apache-2.0 | |
| Skill InspectorNVIDIA/SkillSpector | 20k | — | ~1.8k | Automated safety check: Pass | Apache-2.0 | |
| Vulners API Python SDKvulnersCom/api | 376 | — | ~2.3k | Automated safety check: Pass | MIT | |
| Hol Guard Protectionhashgraph-online/hol-guard | 845 | — | ~605 | Automated safety check: Pass | Apache-2.0 | |
| Bumblebeesickn33/agentic-awesome-skills | 47k | 1 repos | ~2.5k | Automated safety check: Notes | MIT |
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Questions about Sealeap Baxia Amazon Weather Driven Selection Scan
What does Sealeap Baxia Amazon Weather Driven Selection Scan do?
Screen a weather- or event-triggered demand spike for Amazon product opportunities by checking local supply-capacity gaps, installation or usage-friction barriers, and one's own supply-chain…. Sealeap Baxia Amazon Weather Driven Selection Scan is an agent skill from xjli360/sealeap-amazon-skills. Screen a weather- or event-triggered demand spike for Amazon product opportunities by checking local supply-capacity gaps, installation or usage-friction barriers, and one's own supply-chain response speed before committing inventory.
When should I use Sealeap Baxia Amazon Weather Driven Selection Scan?
Sealeap Baxia Amazon Weather Driven Selection Scan fits situations like: 极端天气或突发事件带来的短期需求判断、季节性选品的供需缺口验证、追热点选品前的产能与合规评估; commit large inventory purchases based solely on a short observation window without a clearance; markdown fallback plan.
How do I install Sealeap Baxia Amazon Weather Driven Selection Scan in Claude Code?
Run `npx skills add xjli360/sealeap-amazon-skills --skill sealeap-baxia-amazon-weather-driven-selection-scan -a claude-code`. Or copy the skill folder (amazon-skills/weixin/baxia/sealeap-baxia-amazon-weather-driven-selection-scan in xjli360/sealeap-amazon-skills) into .claude/skills/sealeap-baxia-amazon-weather-driven-selection-scan in your project. Claude Code loads it when a task matches its description.
How do I install Sealeap Baxia Amazon Weather Driven Selection Scan in Codex?
Run `npx skills add xjli360/sealeap-amazon-skills --skill sealeap-baxia-amazon-weather-driven-selection-scan -a codex`. Or copy the skill folder (amazon-skills/weixin/baxia/sealeap-baxia-amazon-weather-driven-selection-scan in xjli360/sealeap-amazon-skills) into .agents/skills/sealeap-baxia-amazon-weather-driven-selection-scan in your project. Codex loads it when a task matches its description.
Can I use Sealeap Baxia Amazon Weather Driven Selection Scan 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-baxia-amazon-weather-driven-selection-scan -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-baxia-amazon-weather-driven-selection-scan, .gemini/skills/sealeap-baxia-amazon-weather-driven-selection-scan, .github/skills/sealeap-baxia-amazon-weather-driven-selection-scan and .opencode/skills/sealeap-baxia-amazon-weather-driven-selection-scan in your project.
What does Sealeap Baxia Amazon Weather Driven Selection Scan need to run?
Going by SKILL.md and its folder, Sealeap Baxia Amazon Weather Driven Selection Scan needs Python for the scripts in its folder. Our summary lists: Python 3.
Does Sealeap Baxia Amazon Weather Driven Selection Scan 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 Baxia Amazon Weather Driven Selection Scan 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 Baxia Amazon Weather Driven Selection Scan use?
Sealeap Baxia Amazon Weather Driven Selection Scan 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 Baxia Amazon Weather Driven Selection Scan use?
About 666 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 Baxia Amazon Weather Driven Selection Scan?
Skills that share tags, products or a category with Sealeap Baxia Amazon Weather Driven Selection Scan: Plugin Scanner (iflytek/skillhub, 5.2k stars), Skill Inspector (NVIDIA/SkillSpector, 20k stars), Vulners API Python SDK (vulnersCom/api, 376 stars) and Hol Guard Protection (hashgraph-online/hol-guard, 845 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Sealeap Baxia Amazon Weather Driven Selection Scan?
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.