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 Fenghuang Amazon Product Research Methods
Generate product candidates for the US marketplace through several discovery paths—keyword-gap search, filtered product databases, reverse-ASIN keyword pulls, and own-problem ideas—then push every…
Install Sealeap Fenghuang Amazon Product Research Methods
$ npx skills add xjli360/sealeap-amazon-skills --skill sealeap-fenghuang-amazon-product-research-methods -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install xjli360/sealeap-amazon-skills sealeap-fenghuang-amazon-product-research-methods --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-research-methods .claude/skills/sealeap-fenghuang-amazon-product-research-methods && 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-research-methods" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/youtube/fenghuang/sealeap-fenghuang-amazon-product-research-methods into .claude/skills/sealeap-fenghuang-amazon-product-research-methods/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-fenghuang-amazon-product-research-methods", 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-research-methodsType 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-research-methods -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install xjli360/sealeap-amazon-skills sealeap-fenghuang-amazon-product-research-methods --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-research-methods .agents/skills/sealeap-fenghuang-amazon-product-research-methods && 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-research-methods" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/youtube/fenghuang/sealeap-fenghuang-amazon-product-research-methods into .agents/skills/sealeap-fenghuang-amazon-product-research-methods/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-fenghuang-amazon-product-research-methods", 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-research-methods -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install xjli360/sealeap-amazon-skills sealeap-fenghuang-amazon-product-research-methods --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-research-methods .cursor/skills/sealeap-fenghuang-amazon-product-research-methods && 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-research-methods" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/youtube/fenghuang/sealeap-fenghuang-amazon-product-research-methods into .cursor/skills/sealeap-fenghuang-amazon-product-research-methods/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-fenghuang-amazon-product-research-methods", 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-research-methods--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-research-methods -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install xjli360/sealeap-amazon-skills sealeap-fenghuang-amazon-product-research-methods --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-research-methods .gemini/skills/sealeap-fenghuang-amazon-product-research-methods && 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-research-methods" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/youtube/fenghuang/sealeap-fenghuang-amazon-product-research-methods into .gemini/skills/sealeap-fenghuang-amazon-product-research-methods/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-fenghuang-amazon-product-research-methods", 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-research-methodsInstalls 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-research-methods -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-research-methods .github/skills/sealeap-fenghuang-amazon-product-research-methods && 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-research-methods" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/youtube/fenghuang/sealeap-fenghuang-amazon-product-research-methods into .github/skills/sealeap-fenghuang-amazon-product-research-methods/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-fenghuang-amazon-product-research-methods", 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-research-methods -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-research-methods --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-research-methods .opencode/skills/sealeap-fenghuang-amazon-product-research-methods && 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-research-methods" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/youtube/fenghuang/sealeap-fenghuang-amazon-product-research-methods into .opencode/skills/sealeap-fenghuang-amazon-product-research-methods/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-fenghuang-amazon-product-research-methods", 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-research-methods- GitHub stars
- 251
- Token cost
- ~725 tokens
- SKILL.md length
- 159 words
- Files
- 5 (incl. scripts, references)
- Skills in repo
- 179
- Repo updated
- First seen
- Licence
- MIT
At a glance
Generate product candidates for the US marketplace through several discovery paths—keyword-gap search, filtered product databases, reverse-ASIN keyword pulls, and own-problem ideas—then push every…
- 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。 → …
- 怎么找产品、选品方法有哪些、关键词找品、反查竞品关键词、自己想做的产品有没有市场、竞品月销怎么估
- 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 Fenghuang Amazon Product Research Methods is an agent skill from xjli360/sealeap-amazon-skills. Generate product candidates for the US marketplace through several discovery paths—keyword-gap search, filtered product databases, reverse-ASIN keyword pulls, and own-problem ideas—then push every candidate through the same demand, competition, and revenue-estimate validation before shortlisting. Use for 怎么找产品、选品方法有哪些、关键词找品、反查竞品关键词、自己想做的产品有没有市场、竞品月销怎么估. Do not use to output a final GO decision without unit economics and compliance checks.
Its SKILL.md is about 730 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 Keyword research, Financial modeling and Regulatory compliance. 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
- 怎么找产品、选品方法有哪些、关键词找品、反查竞品关键词、自己想做的产品有没有市场、竞品月销怎么估
- Output a final GO decision without unit economics and compliance checks
Example prompts
- “/sealeap-fenghuang-amazon-product-research-methods”
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 Research Methods loads about 725 tokens when it runs, and up to ~3.1k if it reads all its reference files. Until then it costs about 123 tokens; SKILL.md has 159 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). 159 words, ~725 tokens.
Download SKILL.mdSave it as
.claude/skills/sealeap-fenghuang-amazon-product-research-methods/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-research-methods
- description
- Generate product candidates for the US marketplace through several discovery paths—keyword-gap search, filtered product databases, reverse-ASIN keyword pulls, and own-problem ideas—then push every candidate through the same demand, competition, and revenue-estimate validation before shortlisting. Use for 怎么找产品、选品方法有哪些、关键词找品、反查竞品关键词、自己想做的产品有没有市场、竞品月销怎么估. Do not use to output a final GO decision without unit economics and compliance checks.
Amazon 多路径选品发现与需求验证
目标
Generate product candidates for the US marketplace through several discovery paths—keyword-gap search, filtered product databases, reverse-ASIN keyword pulls, and own-problem ideas—then push every candidate through the same demand, competition, and revenue-estimate validation before shortlisting.
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。
- 「搜索结果越靠后销量越少」是来源的经验分布，实际曲线随类目与设备不同，用当前数据观察。
- 所有月销与营收数字都是第三方估算，标为 ESTIMATE；不同工具口径差异大，不得写成事实。
- 来源强调「先动手别拖延」，可作决策节奏建议，但不等于省略验证步骤。
- 来源中的选品工具与课程推荐不采纳；本 Skill 只描述工具类别。
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：
- 明确目标：找「有稳定搜索需求 + 头部竞争可撼动」的品，而不是「便宜好做」的品；先定 marketplace（默认 US）与可投入资金上限。
- 路径 A 关键词缺口：用关键词工具类别列出搜索量可观的短语，到前台搜索看结果是否缺少精准匹配产品或匹配品质量差；把缺口写成可证伪假设。
- 路径 B 数据库筛选：按目标月营收、评论数上限、价格带等条件筛产品库，产出候选池；筛选条件以自有资金和类目校准。
- 路径 C 反查竞品：对候选品头部 ASIN 做关键词反查，看其流量词是否集中、是否存在本品能覆盖而对手未覆盖的词。
- 路径 D 自身痛点或兴趣：把自己遇到的未被满足需求写成产品定义，但同样走路径 B、C 验证，不因「热爱」跳过数据。
- 统一估算：用 BSR 与销量估算工具类别推算头部竞品月销与营收（销量 × 售价），标注估算口径；多家工具口径不一致时并列保留，不取平均。
- 竞争判断：看首页产品的评论数、评分、价格与品牌集中度，判断新品能否以差异化进入；输出候选短名单与各自的缺口证据。
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：获取关键词搜索量、竞品反查词、产品库筛选与销量估算的第三方代理证据。
- 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
- 候选品发现记录
- 关键词缺口假设表
- 竞品反查词表
- 销量与营收估算表
- 候选短名单与竞争判断
- 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-research-methods 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 Research Methods 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 Research Methods this skillxjli360/sealeap-amazon-skills | 251 | — | ~725 | Automated safety check: Pass | MIT | |
| Creating Financial ModelsChen-zexi/open-ptc-agent | 729 | 3 repos | ~1.3k | 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 | |
| Find Law Firmjeremylongshore/tons-of-skills-marketplace | 2.8k | — | ~3.6k | Automated safety check: Notes | MIT | |
| Historical Financial RatingsOctagonAI/skills | 127 | — | ~1k | Automated safety check: Pass | MIT |
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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 Fenghuang Amazon Product Research Methods
What does Sealeap Fenghuang Amazon Product Research Methods do?
Generate product candidates for the US marketplace through several discovery paths—keyword-gap search, filtered product databases, reverse-ASIN keyword pulls, and own-problem ideas—then push every…. Sealeap Fenghuang Amazon Product Research Methods is an agent skill from xjli360/sealeap-amazon-skills. Generate product candidates for the US marketplace through several discovery paths—keyword-gap search, filtered product databases, reverse-ASIN keyword pulls, and own-problem ideas—then push every candidate through the same demand, competition, and revenue-estimate validation before shortlisting.
When should I use Sealeap Fenghuang Amazon Product Research Methods?
Sealeap Fenghuang Amazon Product Research Methods fits situations like: 怎么找产品、选品方法有哪些、关键词找品、反查竞品关键词、自己想做的产品有没有市场、竞品月销怎么估; output a final GO decision without unit economics and compliance checks.
How do I install Sealeap Fenghuang Amazon Product Research Methods in Claude Code?
Run `npx skills add xjli360/sealeap-amazon-skills --skill sealeap-fenghuang-amazon-product-research-methods -a claude-code`. Or copy the skill folder (amazon-skills/youtube/fenghuang/sealeap-fenghuang-amazon-product-research-methods in xjli360/sealeap-amazon-skills) into .claude/skills/sealeap-fenghuang-amazon-product-research-methods in your project. Claude Code loads it when a task matches its description.
How do I install Sealeap Fenghuang Amazon Product Research Methods in Codex?
Run `npx skills add xjli360/sealeap-amazon-skills --skill sealeap-fenghuang-amazon-product-research-methods -a codex`. Or copy the skill folder (amazon-skills/youtube/fenghuang/sealeap-fenghuang-amazon-product-research-methods in xjli360/sealeap-amazon-skills) into .agents/skills/sealeap-fenghuang-amazon-product-research-methods in your project. Codex loads it when a task matches its description.
Can I use Sealeap Fenghuang Amazon Product Research Methods 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-research-methods -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-research-methods, .gemini/skills/sealeap-fenghuang-amazon-product-research-methods, .github/skills/sealeap-fenghuang-amazon-product-research-methods and .opencode/skills/sealeap-fenghuang-amazon-product-research-methods in your project.
What does Sealeap Fenghuang Amazon Product Research Methods need to run?
Going by SKILL.md and its folder, Sealeap Fenghuang Amazon Product Research Methods needs Python for the scripts in its folder. Our summary lists: Python 3.
Does Sealeap Fenghuang Amazon Product Research Methods 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 Research Methods 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 Research Methods use?
Sealeap Fenghuang Amazon Product Research Methods 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 Research Methods use?
About 725 tokens (SKILL.md is roughly 2.9k 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 Fenghuang Amazon Product Research Methods?
Skills that share tags, products or a category with Sealeap Fenghuang Amazon Product Research Methods: Creating Financial Models (Chen-zexi/open-ptc-agent, 729 stars), Dcf Model (Wind-Alice/AliceMarket, 134 stars), Analyst Estimates (OctagonAI/skills, 127 stars) and Find Law Firm (jeremylongshore/tons-of-skills-marketplace, 2.8k 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 Research Methods?
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.