Creating Financial Models
Chen-zexi/open-ptc-agent
This skill provides an advanced financial modeling suite with DCF analysis, sensitivity testing, Monte Carlo simulations, and scenario planning for investment decisions
Agent skill
Sealeap Qiongqi Amazon AI Product Gap Research
Use an agentic AI workflow on raw Amazon keyword exports and customer feedback to locate shopper-intent gaps with demand but weak supply, then produce a product definition, unit economics, and…
Install Sealeap Qiongqi Amazon AI Product Gap Research
$ npx skills add xjli360/sealeap-amazon-skills --skill sealeap-qiongqi-amazon-ai-product-gap-research -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install xjli360/sealeap-amazon-skills sealeap-qiongqi-amazon-ai-product-gap-research --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/xjli360/sealeap-amazon-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/amazon-skills/youtube/qiongqi/sealeap-qiongqi-amazon-ai-product-gap-research .claude/skills/sealeap-qiongqi-amazon-ai-product-gap-research && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "sealeap-qiongqi-amazon-ai-product-gap-research" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/youtube/qiongqi/sealeap-qiongqi-amazon-ai-product-gap-research into .claude/skills/sealeap-qiongqi-amazon-ai-product-gap-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-qiongqi-amazon-ai-product-gap-research", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/youtube/qiongqi/sealeap-qiongqi-amazon-ai-product-gap-researchType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add xjli360/sealeap-amazon-skills --skill sealeap-qiongqi-amazon-ai-product-gap-research -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install xjli360/sealeap-amazon-skills sealeap-qiongqi-amazon-ai-product-gap-research --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/xjli360/sealeap-amazon-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/amazon-skills/youtube/qiongqi/sealeap-qiongqi-amazon-ai-product-gap-research .agents/skills/sealeap-qiongqi-amazon-ai-product-gap-research && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "sealeap-qiongqi-amazon-ai-product-gap-research" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/youtube/qiongqi/sealeap-qiongqi-amazon-ai-product-gap-research into .agents/skills/sealeap-qiongqi-amazon-ai-product-gap-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-qiongqi-amazon-ai-product-gap-research", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add xjli360/sealeap-amazon-skills --skill sealeap-qiongqi-amazon-ai-product-gap-research -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install xjli360/sealeap-amazon-skills sealeap-qiongqi-amazon-ai-product-gap-research --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/xjli360/sealeap-amazon-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/amazon-skills/youtube/qiongqi/sealeap-qiongqi-amazon-ai-product-gap-research .cursor/skills/sealeap-qiongqi-amazon-ai-product-gap-research && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "sealeap-qiongqi-amazon-ai-product-gap-research" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/youtube/qiongqi/sealeap-qiongqi-amazon-ai-product-gap-research into .cursor/skills/sealeap-qiongqi-amazon-ai-product-gap-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-qiongqi-amazon-ai-product-gap-research", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/xjli360/sealeap-amazon-skills.git --path amazon-skills/youtube/qiongqi/sealeap-qiongqi-amazon-ai-product-gap-research--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add xjli360/sealeap-amazon-skills --skill sealeap-qiongqi-amazon-ai-product-gap-research -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install xjli360/sealeap-amazon-skills sealeap-qiongqi-amazon-ai-product-gap-research --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/xjli360/sealeap-amazon-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/amazon-skills/youtube/qiongqi/sealeap-qiongqi-amazon-ai-product-gap-research .gemini/skills/sealeap-qiongqi-amazon-ai-product-gap-research && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "sealeap-qiongqi-amazon-ai-product-gap-research" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/youtube/qiongqi/sealeap-qiongqi-amazon-ai-product-gap-research into .gemini/skills/sealeap-qiongqi-amazon-ai-product-gap-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-qiongqi-amazon-ai-product-gap-research", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install xjli360/sealeap-amazon-skills sealeap-qiongqi-amazon-ai-product-gap-researchInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add xjli360/sealeap-amazon-skills --skill sealeap-qiongqi-amazon-ai-product-gap-research -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/xjli360/sealeap-amazon-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/amazon-skills/youtube/qiongqi/sealeap-qiongqi-amazon-ai-product-gap-research .github/skills/sealeap-qiongqi-amazon-ai-product-gap-research && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "sealeap-qiongqi-amazon-ai-product-gap-research" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/youtube/qiongqi/sealeap-qiongqi-amazon-ai-product-gap-research into .github/skills/sealeap-qiongqi-amazon-ai-product-gap-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-qiongqi-amazon-ai-product-gap-research", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add xjli360/sealeap-amazon-skills --skill sealeap-qiongqi-amazon-ai-product-gap-research -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install xjli360/sealeap-amazon-skills sealeap-qiongqi-amazon-ai-product-gap-research --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/xjli360/sealeap-amazon-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/amazon-skills/youtube/qiongqi/sealeap-qiongqi-amazon-ai-product-gap-research .opencode/skills/sealeap-qiongqi-amazon-ai-product-gap-research && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "sealeap-qiongqi-amazon-ai-product-gap-research" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/youtube/qiongqi/sealeap-qiongqi-amazon-ai-product-gap-research into .opencode/skills/sealeap-qiongqi-amazon-ai-product-gap-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-qiongqi-amazon-ai-product-gap-research", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
sealeap-qiongqi-amazon-ai-product-gap-research- GitHub stars
- 251
- Token cost
- ~699 tokens
- SKILL.md length
- 154 words
- Files
- 5 (incl. scripts, references)
- Skills in repo
- 179
- Repo updated
- First seen
- Licence
- MIT
At a glance
Use an agentic AI workflow on raw Amazon keyword exports and customer feedback to locate shopper-intent gaps with demand but weak supply, then produce a product definition, unit economics, and…
- Works in 4 steps: 诊断：读取现状、证据和缺口，不生成线上写入动作。 → 方案草案：输出可审核的结构、参数范围、实验和回退值。 → 执行准备：只生成待批准变更表或 API/控制台操作草案。 → …
- AI 选品、找市场缺口、有需求没供给的细分、产品开发方向、单位经济测算、供应商询价整理
- SKILL.md covers 目标, 不可妥协的边界, 先判断任务模式 and 开始前要拿到, plus 3 more sections
- Runs Python scripts from its folder
What it does
Sealeap Qiongqi Amazon AI Product Gap Research is an agent skill from xjli360/sealeap-amazon-skills. Use an agentic AI workflow on raw Amazon keyword exports and customer feedback to locate shopper-intent gaps with demand but weak supply, then produce a product definition, unit economics, and supplier quote requests that are verified by hand before any sourcing decision. Use for AI 选品、找市场缺口、有需求没供给的细分、产品开发方向、单位经济测算、供应商询价整理. Do not use AI projections as final numbers or to skip compliance and IP checks.
Its SKILL.md is about 700 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 Business, Finance & HR, covering Customer feedback analysis and Financial modeling. It works with Model Context Protocol. The repository describes itself as: Reusable Agent Skills for Amazon product research, listings, advertising, inventory, and operations. The licence is MIT.
When your agent uses it
- AI 选品、找市场缺口、有需求没供给的细分、产品开发方向、单位经济测算、供应商询价整理
- Tasks that involve Customer feedback analysis
- Tasks that involve Financial modeling
Example prompts
- “/sealeap-qiongqi-amazon-ai-product-gap-research”
Requirements
- Python 3
Workflow steps
4 steps, taken from the first numbered list in SKILL.md.
- 诊断：读取现状、证据和缺口，不生成线上写入动作。
- 方案草案：输出可审核的结构、参数范围、实验和回退值。
- 执行准备：只生成待批准变更表或 API/控制台操作草案。
- 已批准执行：仅对用户在当前会话明确批准的对象和字段执行，并立即回读核验。
What it can do on your machine
Read from SKILL.md and the folder at commit 497d4b8. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 1 file in scripts/ (Python), which the agent can run.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Sealeap Qiongqi Amazon AI Product Gap Research loads about 699 tokens when it runs, and up to ~3k if it reads all its reference files. Until then it costs about 113 tokens; SKILL.md has 154 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). 154 words, ~699 tokens.
Download SKILL.mdSave it as
.claude/skills/sealeap-qiongqi-amazon-ai-product-gap-research/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.- name
- sealeap-qiongqi-amazon-ai-product-gap-research
- description
- Use an agentic AI workflow on raw Amazon keyword exports and customer feedback to locate shopper-intent gaps with demand but weak supply, then produce a product definition, unit economics, and supplier quote requests that are verified by hand before any sourcing decision. Use for AI 选品、找市场缺口、有需求没供给的细分、产品开发方向、单位经济测算、供应商询价整理. Do not use AI projections as final numbers or to skip compliance and IP checks.
Amazon AI 辅助选品缺口与单位经济
目标
Use an agentic AI workflow on raw Amazon keyword exports and customer feedback to locate shopper-intent gaps with demand but weak supply, then produce a product definition, unit economics, and supplier quote requests that are verified by hand before any sourcing decision.
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。
- 『几分钟产出等同顾问级报告』是来源观点；AI 的财务预测与供应商报价可能失真或幻觉，只作草稿。
- 『需求高供给弱』依赖第三方搜索量估算，季节性与词义歧义会夸大需求，需交叉验证。
- 智能体自动操作浏览器与外部网站时要控制授权范围与数据泄露风险，不要把账户凭证交给它。
- 选品决策还受合规、IP、库存资金等约束，本流程只覆盖需求与经济性初筛。
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：
- 先定上下文：目标细分、现有产品事实、约束（预算、MOQ、目标毛利、站点）；给智能体的任务要像交给一名员工，附资料而非只给一句话。
- 从关键词工具导出该细分主词的完整未过滤关键词列表，连同评论或反馈样本一起提供；不要预处理，让模型做聚合与分组。
- 要求输出：按购买意图聚类的关键词及搜索量合计、每个意图簇下现有供给（在售数量、评论数、评分、价格）与需求的对照，标出『需求高、供给弱』的缺口。
- 对每个缺口人工验证：回到导出数据核对该簇搜索量是否真实、在站内搜索确认供给确实稀少或质量差、查看评论确认痛点存在。
- 让 AI 基于反馈做产品定义（必备特性、差异点）与单位经济（售价、成本、物流、费用、目标 ACOS 下的盈亏平衡），并整理供应商询价；所有价格与报价当作草稿，逐条向供应商与物流核实。
- 补做合规与 IP 初筛后再决定是否立项；把该意图簇的关键词表直接带入后续 PPC 活动规划。
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：获取关键词需求、竞品供给（在售数量、评论、价格）与评论痛点的第三方代理数据。
- 先
doctor，再search-tools和describe；工具名及参数以实时tools/list与inputSchema为准。 - Token 只从环境变量读取。不得写入命令参数、URL、Skill、报告、日志或 Git。
tools/call或 Actor 可能计费；先展示 Provider、工具、无密钥业务参数、预计成本与输出位置，核对已有授权覆盖后才加--allow-cost；该标志不是费用上限。
必须交付的结果
- 上下文与资料包
- 意图缺口分析表
- 缺口人工验证记录
- 产品定义与单位经济草案
- 供应商询价清单
- 数据范围、来源、采集时间、样本与限制。
- 关键假设、待补证据、风险和不可确定项。
- 若有动作：对象、旧值、新值、预期、停止条件、回退值与审批状态。
方案状态使用 READY FOR REVIEW / DRAFT / HOLD / STOP；如已执行，另行记录实际结果及回读证据。未得到明确批准时，不得声称已修改线上对象。
© xjli360, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 4 other files (scripts, references) in amazon-skills/youtube/qiongqi/sealeap-qiongqi-amazon-ai-product-gap-research of xjli360/sealeap-amazon-skills.
- SKILL.md
- agents/openai.yaml
- references/mcp-data-plan.md
- references/playbook.md
- scripts/mcp_research.py
Open the folder on GitHubat commit 497d4b8
Compare with similar skills
Sealeap Qiongqi Amazon AI Product Gap Research next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Sealeap Qiongqi Amazon AI Product Gap Research this skillxjli360/sealeap-amazon-skills | 251 | — | ~699 | Automated safety check: Pass | MIT | |
| Creating Financial ModelsChen-zexi/open-ptc-agent | 729 | 3 repos | ~1.3k | Automated safety check: Pass | MIT | |
| Okx Sentiment Trackerdex-original/okx-agent-trade-kit | 110 | 1 repos | ~3.8k | Automated safety check: Pass | MIT | |
| Dcf ModelWind-Alice/AliceMarket | 134 | 2 repos | ~12k | Automated safety check: Pass | None | |
| Analyst EstimatesOctagonAI/skills | 127 | — | ~1.1k | Automated safety check: Pass | MIT | |
| Historical Financial RatingsOctagonAI/skills | 127 | — | ~1k | Automated safety check: Pass | MIT |
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xjli360/sealeap-amazon-skills
Research, diagnose, and draft Amazon Ads ASIN and category product-targeting plans that complement keyword targeting, including audience expansion, competitor and category traffic, cross-sell…
Sealeap Amazon Acos Conversion Diagnostics
xjli360/sealeap-amazon-skills
Diagnose high Amazon Ads ACoS by decomposing CPC, conversion rate, price, query mix, placement mix, and sample sufficiency.
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Questions about Sealeap Qiongqi Amazon AI Product Gap Research
What does Sealeap Qiongqi Amazon AI Product Gap Research do?
Use an agentic AI workflow on raw Amazon keyword exports and customer feedback to locate shopper-intent gaps with demand but weak supply, then produce a product definition, unit economics, and…. Sealeap Qiongqi Amazon AI Product Gap Research is an agent skill from xjli360/sealeap-amazon-skills. Use an agentic AI workflow on raw Amazon keyword exports and customer feedback to locate shopper-intent gaps with demand but weak supply, then produce a product definition, unit economics, and supplier quote requests that are verified by hand before any sourcing decision.
When should I use Sealeap Qiongqi Amazon AI Product Gap Research?
Sealeap Qiongqi Amazon AI Product Gap Research fits situations like: AI 选品、找市场缺口、有需求没供给的细分、产品开发方向、单位经济测算、供应商询价整理; tasks that involve Customer feedback analysis; tasks that involve Financial modeling.
How do I install Sealeap Qiongqi Amazon AI Product Gap Research in Claude Code?
Run `npx skills add xjli360/sealeap-amazon-skills --skill sealeap-qiongqi-amazon-ai-product-gap-research -a claude-code`. Or copy the skill folder (amazon-skills/youtube/qiongqi/sealeap-qiongqi-amazon-ai-product-gap-research in xjli360/sealeap-amazon-skills) into .claude/skills/sealeap-qiongqi-amazon-ai-product-gap-research in your project. Claude Code loads it when a task matches its description.
How do I install Sealeap Qiongqi Amazon AI Product Gap Research in Codex?
Run `npx skills add xjli360/sealeap-amazon-skills --skill sealeap-qiongqi-amazon-ai-product-gap-research -a codex`. Or copy the skill folder (amazon-skills/youtube/qiongqi/sealeap-qiongqi-amazon-ai-product-gap-research in xjli360/sealeap-amazon-skills) into .agents/skills/sealeap-qiongqi-amazon-ai-product-gap-research in your project. Codex loads it when a task matches its description.
Can I use Sealeap Qiongqi Amazon AI Product Gap Research in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add xjli360/sealeap-amazon-skills --skill sealeap-qiongqi-amazon-ai-product-gap-research -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/sealeap-qiongqi-amazon-ai-product-gap-research, .gemini/skills/sealeap-qiongqi-amazon-ai-product-gap-research, .github/skills/sealeap-qiongqi-amazon-ai-product-gap-research and .opencode/skills/sealeap-qiongqi-amazon-ai-product-gap-research in your project.
What does Sealeap Qiongqi Amazon AI Product Gap Research need to run?
Going by SKILL.md and its folder, Sealeap Qiongqi Amazon AI Product Gap Research needs Python for the scripts in its folder. Our summary lists: Python 3.
Does Sealeap Qiongqi Amazon AI Product Gap Research access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Sealeap Qiongqi Amazon AI Product Gap Research safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Sealeap Qiongqi Amazon AI Product Gap Research use?
Sealeap Qiongqi Amazon AI Product Gap Research is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Sealeap Qiongqi Amazon AI Product Gap Research use?
About 699 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 Qiongqi Amazon AI Product Gap Research?
Skills that share tags, products or a category with Sealeap Qiongqi Amazon AI Product Gap Research: Creating Financial Models (Chen-zexi/open-ptc-agent, 729 stars), Okx Sentiment Tracker (dex-original/okx-agent-trade-kit, 110 stars), Dcf Model (Wind-Alice/AliceMarket, 134 stars) and Analyst Estimates (OctagonAI/skills, 127 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Sealeap Qiongqi Amazon AI Product Gap Research?
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.