Startup Pressure Test
Kappaemme-git/codex-startup-pressure-test-skill
Pressure-tests a startup idea with blunt, compact analysis of problem reality, competition, first customers and MVP, ending in a strong, weak or pivot verdict.
专业深度研究与报告生成技能。支持企业竞争分析、产品竞争分析、行业分析、市场规模/竞争格局、AI大模型厂商、AI工具学习指南等领域。整合17个搜索引擎，三阶段工作流（主题确认→框架生成→报告输出），运用PESTEL、SWOT、波特五力、商业模式画布等经典咨询研究模型，输出精美的深蓝色政务风格HTML与Markdown双格式咨询级报告。
Install Deepsearch Mpro
$ npx skills add LeoYeAI/openclaw-master-skills --skill deepsearch-mpro -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills deepsearch-mpro --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/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/deepsearch-mpro .claude/skills/deepsearch-mpro && 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 "deepsearch-mpro" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/deepsearch-mpro into .claude/skills/deepsearch-mpro/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deepsearch-mpro", 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/LeoYeAI/openclaw-master-skills/tree/main/skills/deepsearch-mproType 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 LeoYeAI/openclaw-master-skills --skill deepsearch-mpro -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills deepsearch-mpro --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/deepsearch-mpro .agents/skills/deepsearch-mpro && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "deepsearch-mpro" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/deepsearch-mpro into .agents/skills/deepsearch-mpro/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deepsearch-mpro", 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 LeoYeAI/openclaw-master-skills --skill deepsearch-mpro -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills deepsearch-mpro --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/deepsearch-mpro .cursor/skills/deepsearch-mpro && 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 "deepsearch-mpro" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/deepsearch-mpro into .cursor/skills/deepsearch-mpro/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deepsearch-mpro", 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/LeoYeAI/openclaw-master-skills.git --path skills/deepsearch-mpro--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 LeoYeAI/openclaw-master-skills --skill deepsearch-mpro -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills deepsearch-mpro --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/deepsearch-mpro .gemini/skills/deepsearch-mpro && 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 "deepsearch-mpro" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/deepsearch-mpro into .gemini/skills/deepsearch-mpro/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deepsearch-mpro", 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 LeoYeAI/openclaw-master-skills deepsearch-mproInstalls 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 LeoYeAI/openclaw-master-skills --skill deepsearch-mpro -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/deepsearch-mpro .github/skills/deepsearch-mpro && 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 "deepsearch-mpro" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/deepsearch-mpro into .github/skills/deepsearch-mpro/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deepsearch-mpro", 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 LeoYeAI/openclaw-master-skills --skill deepsearch-mpro -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills deepsearch-mpro --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/deepsearch-mpro .opencode/skills/deepsearch-mpro && 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 "deepsearch-mpro" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/deepsearch-mpro into .opencode/skills/deepsearch-mpro/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deepsearch-mpro", 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
deepsearch-mpro- GitHub stars
- 2.2k
- Token cost
- ~2.4k tokens
- SKILL.md length
- 553 words
- Files
- 93 (incl. scripts, references, assets)
- Skills in repo
- 1,235
- Repo updated
- First seen
- Licence
- MIT
At a glance
专业深度研究与报告生成技能。支持企业竞争分析、产品竞争分析、行业分析、市场规模/竞争格局、AI大模型厂商、AI工具学习指南等领域。整合17个搜索引擎，三阶段工作流（主题确认→框架生成→报告输出），运用PESTEL、SWOT、波特五力、商业模式画布等经典咨询研究模型，输出精美的深蓝色政务风格HTML与Markdown双格式咨询级报告。
- Works in 2 steps: 解析用户输入 → 推断分析领域
- Tasks that involve Startup and business strategy
- SKILL.md covers 概述, 三阶段工作流, 数据收集策略 and 数据真实性协议, plus 3 more sections
What it does
Deepsearch Mpro is an agent skill from LeoYeAI/openclaw-master-skills. 专业深度研究与报告生成技能。支持企业竞争分析、产品竞争分析、行业分析、市场规模/竞争格局、AI大模型厂商、AI工具学习指南等领域。整合17个搜索引擎，三阶段工作流（主题确认→框架生成→报告输出），运用PESTEL、SWOT、波特五力、商业模式画布等经典咨询研究模型，输出精美的深蓝色政务风格HTML与Markdown双格式咨询级报告。
Its SKILL.md is about 2.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 96 other files, including scripts, reference files and assets (for example `.github/ISSUE_TEMPLATE/bug_report.md`, `.github/ISSUE_TEMPLATE/config.yml` and `.github/ISSUE_TEMPLATE/feature_request.md`).
It sits in Business, Finance & HR, covering Startup and business strategy. The repository describes itself as: 🧠 Curated collection of 1209+ best OpenClaw skills — weekly updated by MyClaw.ai. The licence is MIT.
When your agent uses it
- Tasks that involve Startup and business strategy
Example prompts
- “/deepsearch-mpro”
Workflow steps
2 steps, taken from the first numbered list in SKILL.md.
- 解析用户输入
- 推断分析领域
What it can do on your machine
Read from SKILL.md and the folder at commit e5199b5. 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/, 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
Deepsearch Mpro loads about 2.4k tokens when it runs, and up to ~84k if it reads all its reference files. Until then it costs about 46 tokens; SKILL.md has 553 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 LeoYeAI/openclaw-master-skills at commit e5199b5, republished under its MIT licence (© LeoYeAI). 553 words, ~2,430 tokens.
Download SKILL.mdSave it as
.claude/skills/deepsearch-mpro/SKILL.md (or your agent's skills folder). This skill also uses 92 other files; get the full folder from GitHub.- name
- deepsearch-mpro
- description
- 专业深度研究与报告生成技能。支持企业竞争分析、产品竞争分析、行业分析、市场规模/竞争格局、AI大模型厂商、AI工具学习指南等领域。整合17个搜索引擎，三阶段工作流（主题确认→框架生成→报告输出），运用PESTEL、SWOT、波特五力、商业模式画布等经典咨询研究模型，输出精美的深蓝色政务风格HTML与Markdown双格式咨询级报告。
- version
- 1.0.0
专业研究报告技能
概述
该技能用于生成专业、咨询级别的研究报告，覆盖市场分析、AI 大模型厂商、SaaS 厂商、AI 工具学习指南、竞争情报、行业研究等领域。
核心能力
- ✅ 多源搜索引擎：17个搜索引擎（8个国内 + 9个国际），无需 API 密钥
- ✅ 三阶段工作流：主题理解与确认 + 分析框架生成 + 报告生成
- ✅ 交互式确认：支持用户确认、修改、提问等交互式反馈
- ✅ 双格式输出：Markdown + HTML，深蓝色政务风格
何时使用本技能
- 用户请求市场分析、消费者洞察报告、财务分析、行业研究
- 用户输入具体 AI 工具名称，需要生成学习指南（如"Cursor 学习指南"）
- 用户需要专业的咨询风格研究报告
- 用户提供研究主题，并在数据收集前需要结构化分析框架
整合的技能与能力
本技能整合了以下核心搜索能力，提供多源数据搜索与分析支持：
| 整合来源 | 路径 | 能力说明 | 版权归属 |
|---|---|---|---|
| ddg-web-search | ddg-web-search/ | DuckDuckGo 网络搜索能力，提供全球范围内的实时搜索支持 | 原作者所有 |
| multi-search-engine | multi-search-engine/ | 多搜索引擎聚合能力，整合17个搜索引擎（8个国内 + 9个国际），无需 API 密钥 | 原作者所有 |
能力集成说明：
本技能已将上述搜索能力整合为核心功能
三阶段工作流
阶段 0：主题理解与确认
目的：确保正确理解用户的研究需求，避免方向偏差。
工作流程：
解析用户输入
- 识别研究主题、分析领域、搜索范围、特定角度
推断分析领域
根据主题关键词推断分析领域，加载对应的领域文档：
| 主题类型 | 关键词示例 | 领域文档 |
|---|---|---|
| 行业分析 | "{行业名}行业分析"、"ERP行业趋势"、"AI Agent行业" | references/domains/industry-analysis.md |
| 企业竞争分析 | "{公司名}分析"、"{公司名}竞争分析"、"{公司名}研究"、金蝶、用友、SAP等 | references/domains/company-analysis.md |
| 产品竞争分析 | "{产品名}分析"、"{产品名}研究"、"{产品名}竞争力" | references/domains/product-analysis.md |
| 市场规模/竞争格局 | "市场规模分析"、"竞争格局研究"、"市场机会评估" | references/domains/market-analysis.md |
| AI 大模型厂商 | OpenAI、Anthropic、DeepSeek、智谱AI等 | references/domains/ai-vendor-analysis.md |
| AI 工具学习指南 | "AI工具使用指南"、"Gemini教程" | references/domains/ai-tool-learning-guide-framework.md |
推断逻辑：
行业关键词（行业分析、行业趋势、行业研究）→ 行业分析
企业名称 + 分析/研究 → 企业竞争分析
产品名称 + 分析/研究 → 产品竞争分析
市场规模/竞争格局关键词（市场规模、竞争格局、市场机会）→ 市场规模/竞争格局分析
AI 厂商名（OpenAI、DeepSeek等）→ AI 厂商分析
生成确认提示
向用户展示以下确认项，等待用户确认或修改：
| 确认项 | 说明 | 示例 |
|---|---|---|
| 研究主题 | 解析后的核心研究对象 | "金蝶企业竞争分析" |
| 研究方向 | 分析领域及分析角度 | "企业竞争分析：商业模式 + 竞争格局 + 近期动态" |
| 时间范围 | 数据收集的时间窗口 | "近一周" / "近一月" / "自定义（如2024Q1）" |
时间范围说明：
- 近一周：聚焦最新动态、热点事件、近期发布
- 近一月：平衡时效性与深度，适合大多数分析
- 自定义：特定时间段（如财报季、产品发布周期）
时间参数传递：
- 时间范围确认后，记录为全局参数
search_time_range - 在阶段1数据收集时，自动转换为搜索时间过滤条件
- 示例：
search_time_range = "近一周"→ 搜索引擎添加&before=2026-03-18&after=2026-03-11
- 处理反馈
- 用户确认 → 记录时间参数，进入阶段 1
- 用户修改 → 更新确认项后重新确认
- 用户提问 → 解答后重新确认
详细流程和示例：见 references/workflow/phase0-details.md
阶段 1：分析框架生成
目的：生成完整的分析框架，作为后续数据收集与报告生成的蓝图。
工作流程：
理解研究主题
- 识别核心对象和分析领域
- 加载对应的领域文档（
references/domains/*.md）
选择分析模型
- 根据领域文档推荐，按需加载模型文件（
references/models/*.md） - 每次研究只加载 2-4 个模型，避免堆砌
- 模型索引：
references/models/README.md
- 根据领域文档推荐，按需加载模型文件（
设计章节骨架
- 每章包含：分析目标、分析逻辑、核心假设
- 典型结构：3-5 个主要章节
定义数据需求
- 每条需求包含：指标、类型、来源、搜索方法、优先级
- P0 数据必须收集，P1 重要，P2 补充
定义可视化方案
- 为每章定义图表类型和数据来源
详细流程和示例：见 references/workflow/phase1-details.md
阶段 2：报告生成
目的：将分析框架和数据整合为最终的咨询级报告。
工作流程：
接收并校验输入
- 确认分析框架和数据包齐全
- 检查 P0 数据完整性
映射报告结构
- Abstract → Introduction → Body Chapters → Conclusion → References
撰写报告
- 遵循"视觉锚点 → 数据对比 → 综合分析"流程
- 每个小节以充分的分析段落结尾（≥200字）
撰写摘要和结论
- 摘要：3-5 句话，200-300 字
- 结论：纯客观综合判断，不使用 bullet points
整理参考文献
- 按 GB/T 7714-2015 格式列出
生成输出文件
- Markdown 文件：
assets/report-template.md - HTML 文件：根据分析模型组合模块化模板
- Markdown 文件：
HTML 模板系统：
位于 assets/templates/ 目录，提供 10 个模块化模板：
| 模板文件 | 分析模型 |
|---|---|
product-overview-template.html | 产品概览 |
target-users-template.html | 目标用户分析 |
core-features-template.html | 核心功能 |
business-model-canvas-template.html | 商业模式画布 |
porter-five-forces-template.html | 波特五力分析 |
swot-analysis-template.html | SWOT 分析 |
pestel-analysis-template.html | PESTEL 分析 |
competitor-matrix-template.html | 竞品对比矩阵 |
timeline-template.html | 关键时间线 |
key-metrics-template.html | 关键指标 |
模板组合建议：
| 研究类型 | 推荐模板组合 |
|---|---|
| AI 厂商/产品 | 产品概览 → PESTEL → 目标用户 → 竞品矩阵 → 商业模式画布 → SWOT → 核心功能 |
| 市场竞争 | 产品概览 → PESTEL → 波特五力 → 竞品矩阵 → 关键指标 |
| 商业模式 | 产品概览 → PESTEL → 商业模式画布 → SWOT |
| 行业研究 | 产品概览 → PESTEL → 波特五力 → 关键指标 → 时间线 |
详细流程和示例：见 references/workflow/phase2-details.md
数据收集策略
多层搜索方案（优先级顺序）
第一层：Agent 内置 web_search（优先使用）
- 快速获取基础信息
- 参数：query、max_results（建议 3-10）
- 适用场景：初步数据获取、验证数据是否存在
第二层：web_fetch 深度搜索
- 直接访问搜索引擎获取详细结果
- 中文市场：百度、微信、头条
- 国际市场：Google、Bing
- 政府数据：site:gov.cn
- 行业报告：filetype:pdf
- 详细使用方法：见
references/technical/search-engines.md
第三层：multi-search-engine 备用方案（如前两层无法获取数据）
- 整合 17 个搜索引擎（8个国内 + 9个国际）
- 无需 API 密钥
- 支持高级搜索操作符和时间过滤
- 适用场景：当 web_search 和 web_fetch 都无法获取数据时
第四层：ddg-web-search 最终备选
- DuckDuckGo Lite 搜索
- 零依赖，仅需 web_fetch 工具
- 适用场景：当所有搜索引擎都无法访问时的最终备选
搜索策略执行逻辑
对于每个数据需求（P0/P1/P2）：
1. 先使用 web_search 快速获取
- 成功：进入数据提取
- 失败：进入第2层
2. 使用 web_fetch 深度搜索
- 成功：进入数据提取
- 失败：进入第3层
3. 使用 multi-search-engine 备用方案
- 根据数据类型选择合适引擎：
* 中文市场 → 百度、微信、头条
* 国际市场 → Google、Bing
* 政府数据 → site:gov.cn
* 行业报告 → filetype:pdf
- 成功：进入数据提取
- 失败：进入第4层
4. 使用 ddg-web-search 最终备选
- 成功：进入数据提取
- 失败：标注"数据暂不可得"
数据提取：
- 从搜索结果中提取关键数据
- 多源交叉验证（至少2个来源）
- 标注数据可信度（high/medium/low）Show full SKILL.md (230 more words)Show less
内置搜索能力说明
本技能已内置整合以下搜索能力，用户无需额外安装任何子技能：
| 搜索层 | 能力来源 | 说明 |
|---|---|---|
| 第1层 | Agent 内置 web_search | 默认优先使用 |
| 第2层 | Agent 内置 web_fetch | 深度搜索 |
| 第3层 | 已整合 multi-search-engine | 17个搜索引擎，无需安装 |
| 第4层 | 已整合 ddg-web-search | DuckDuckGo Lite，无需安装 |
子技能已整合：
./multi-search-engine/和./ddg-web-search/的能力已完全整合进本技能- 所有搜索引擎的 URL 模板和使用方法见
references/technical/search-engines.md - 四层搜索策略的详细流程见
references/technical/multi-layer-search-strategy.md
数据真实性协议
严格遵循规则：报告中呈现的所有数据，必须直接来源于提供的 Data Summary 或 External Search Findings。
- 禁止幻觉：不得编造、估算或模拟数据
- 可追溯来源：每个重要结论和图表都必须能够追溯到输入的数据包
- 数据缺失处理：如数据缺失，明确写出"数据暂不可得"
输出格式
Markdown 文件
- 文件名：
{主题关键词}-report-{日期}.md - 模板：
assets/report-template.md
HTML 文件
- 文件名：
{主题关键词}-report-{日期}.html - 模板：
assets/html-template.html - 风格：深蓝色政务/企业内报风格
- 布局：左侧树状导航（固定）+ 右侧内容展示
格式与语气标准
- 语气：麦肯锡 / BCG 风格 —— 权威、客观、专业
- 数字格式：千位分隔使用英文逗号（
1,000） - 数据强调：重要观点和关键数字需加粗
- 标题编号：使用标准编号（
1.、1.1） - 参考文献：必须严格遵循 GB/T 7714-2015
参考文档体系
领域分析框架
| 文档 | 说明 |
|---|---|
references/domains/industry-analysis.md | 行业分析框架（PESTEL + 波特五力 + 价值链） |
references/domains/company-analysis.md | 企业竞争分析框架（含 SaaS 厂商分析，商业模式画布 + SWOT + 竞品矩阵） |
references/domains/product-analysis.md | 产品竞争分析框架（目标用户 + 竞品矩阵 + 核心功能） |
references/domains/market-analysis.md | 市场规模/竞争格局分析框架（TAM-SAM-SOM + 波特五力） |
references/domains/ai-vendor-analysis.md | AI 大模型厂商分析框架 |
references/domains/ai-tool-learning-guide-framework.md | AI 工具学习指南生成框架 |
references/domains/hotspot-analysis.md | 热点分析公共模块（企业/产品/AI/SaaS/全球AI热点） |
工作流程详细文档
| 文档 | 说明 |
|---|---|
references/workflow/phase0-details.md | 阶段0 详细流程和示例 |
references/workflow/phase1-details.md | 阶段1 详细流程和示例 |
references/workflow/phase2-details.md | 阶段2 详细流程和示例 |
references/workflow/examples-complete.md | 完整示例集（5个场景） |
技术指南
| 文档 | 说明 |
|---|---|
references/technical/multi-layer-search-strategy.md | 四层搜索策略详细指南 |
references/technical/search-engines.md | 多源搜索引擎使用指南 |
references/technical/data-quality-guidelines.md | 数据质量控制标准 |
references/technical/format-conversion.md | 格式转换指南 |
HTML 模板
| 文档 | 说明 |
|---|---|
assets/templates/README.md | 模板使用说明 |
assets/templates/*.html | 10 个模块化 HTML 模板 |
领域分析框架（重复）
| 文档 | 说明 |
|---|---|
references/domains/industry-analysis.md | 行业分析框架（PESTEL + 波特五力 + 价值链） |
references/domains/company-analysis.md | 企业竞争分析框架（商业模式画布 + SWOT + 竞品矩阵） |
references/domains/product-analysis.md | 产品竞争分析框架（目标用户 + 竞品矩阵 + 核心功能） |
references/domains/ai-vendor-analysis.md | AI 大模型厂商分析框架 |
references/domains/ai-tool-learning-guide-framework.md | AI 工具学习指南框架 |
references/domains/saas-vendor-analysis.md | SaaS 厂商分析框架 |
references/domains/market-analysis.md | 市场与竞争分析框架 |
分析模型库
| 文档 | 说明 |
|---|---|
references/models/README.md | 模型索引和使用说明 |
references/models/strategic/*.md | 战略模型（SWOT、PESTEL、波特五力、VRIO） |
references/models/market/*.md | 市场模型（STP、BCG、TAM-SAM-SOM） |
references/models/competitive/*.md | 竞争模型（商业模式画布、竞品矩阵、价值链） |
references/models/consumer/*.md | 消费者模型（决策旅程、AARRR、RFM） |
references/models/financial/*.md | 财务模型（杜邦分析、DCF、可比公司） |
方法论
| 文档 | 说明 |
|---|---|
references/methodology/deep-research-methodology.md | 深度研究方法论 |
references/methodology/report-writing-guide.md | 报告撰写指南 |
质量检查清单
阶段 0（主题理解与确认）
- 研究主题已明确
- 分析领域已识别
- 搜索范围已确定
- 用户已确认
阶段 1（分析框架）
- 框架覆盖了该领域自然应有的全部分析维度
- 每章均有明确的分析目标、分析逻辑与核心假设
- 数据需求具体、可衡量，并指定了推荐搜索方法
- 每章至少有一个可视化方案
阶段 1→2（数据收集）
- 所有 P0 数据任务已完成
- 关键数据至少从 2 个来源验证
- 数据来源已记录
- 数据可信度已标注
阶段 2（报告生成）
- Markdown 报告零幻觉
- 每个小节均遵循"数据对比 → 综合分析"
- 参考文献格式正确（GB/T 7714-2015）
配置项
output_locale = zh_CN # zh/en
default_search_engines = ["baidu", "bing", "google"]
data_validation_required = true # P0 数据必须验证
interactive_confirmation = true # 交互式确认，支持用户修改研究参数版本历史
- v1.0.0: 正式版
- 三阶段工作流（主题确认→框架生成→报告输出）
- 整合 17 个搜索引擎（8个国内 + 9个国际）
- 支持市场分析、行业分析、AI/SaaS厂商分析、竞争情报、竞争分析等领域
- 交互式主题确认，支持用户修改研究参数
- 双格式输出（Markdown + HTML）
- 四层搜索策略（web_search → web_fetch → multi-search-engine → ddg-web-search）
© LeoYeAI, 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 92 other files (scripts, references, assets) in skills/deepsearch-mpro of LeoYeAI/openclaw-master-skills.
- SKILL.md
- .github/ISSUE_TEMPLATE/bug_report.md
- .github/ISSUE_TEMPLATE/config.yml
- .github/ISSUE_TEMPLATE/feature_request.md
- .github/pull_request_template.md
- CHANGELOG.md
- CONTRIBUTING.md
- README.md
- _meta.json
- assets/analysis-framework-template.md
- assets/html-generation-guide.md
- assets/html-template.html
- assets/report-template.md
- assets/templates/README.md
- assets/templates/business-model-canvas-template.html
- assets/templates/competitor-matrix-template.html
- assets/templates/core-features-template.html
- … and 76 more
Open the folder on GitHubat commit e5199b5
Compare with similar skills
Deepsearch Mpro 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 |
|---|---|---|---|---|---|---|
| Deepsearch Mpro this skillLeoYeAI/openclaw-master-skills | 2.2k | — | ~2.4k | Automated safety check: Pass | MIT | |
| Startup Pressure TestKappaemme-git/codex-startup-pressure-test-skill | 990 | — | ~1.2k | Automated safety check: Pass | MIT | |
| Zhang Yiming Perspectivealchaincyf/zhang-yiming-skill | 175 | 1 repos | ~3.2k | Automated safety check: Pass | MIT | |
| Korean Government Grant Searchdjfksjd/ir-search | 391 | — | ~3.5k | Automated safety check: Notes | MIT | |
| Mao Zedong Thinking Partnerzhangtianruiwork-droid/Maoxuan-Changzheng | 443 | — | ~2.9k | Automated safety check: Pass | None | |
| Constraint Enginelijigang/ljg-skills | 7.5k | — | ~2.1k | Automated safety check: Pass | MIT |
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Related
Categories
Questions about Deepsearch Mpro
What does Deepsearch Mpro do?
专业深度研究与报告生成技能。支持企业竞争分析、产品竞争分析、行业分析、市场规模/竞争格局、AI大模型厂商、AI工具学习指南等领域。整合17个搜索引擎，三阶段工作流（主题确认→框架生成→报告输出），运用PESTEL、SWOT、波特五力、商业模式画布等经典咨询研究模型，输出精美的深蓝色政务风格HTML与Markdown双格式咨询级报告。. Deepsearch Mpro is an agent skill from LeoYeAI/openclaw-master-skills.
When should I use Deepsearch Mpro?
Deepsearch Mpro fits situations like: tasks that involve Startup and business strategy.
How do I install Deepsearch Mpro in Claude Code?
Run `npx skills add LeoYeAI/openclaw-master-skills --skill deepsearch-mpro -a claude-code`. Or copy the skill folder (skills/deepsearch-mpro in LeoYeAI/openclaw-master-skills) into .claude/skills/deepsearch-mpro in your project. Claude Code loads it when a task matches its description.
How do I install Deepsearch Mpro in Codex?
Run `npx skills add LeoYeAI/openclaw-master-skills --skill deepsearch-mpro -a codex`. Or copy the skill folder (skills/deepsearch-mpro in LeoYeAI/openclaw-master-skills) into .agents/skills/deepsearch-mpro in your project. Codex loads it when a task matches its description.
Can I use Deepsearch Mpro 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 LeoYeAI/openclaw-master-skills --skill deepsearch-mpro -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/deepsearch-mpro, .gemini/skills/deepsearch-mpro, .github/skills/deepsearch-mpro and .opencode/skills/deepsearch-mpro in your project.
What does Deepsearch Mpro need to run?
SKILL.md names no scripts, command-line tools or credentials: Deepsearch Mpro is instructions for the agent only.
Does Deepsearch Mpro 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 Deepsearch Mpro 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 Deepsearch Mpro use?
Deepsearch Mpro 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 Deepsearch Mpro use?
About 2.4k tokens (SKILL.md is roughly 9.7k 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 82k tokens, read only when the agent opens those files.
What are the alternatives to Deepsearch Mpro?
Skills that share tags, products or a category with Deepsearch Mpro: Startup Pressure Test (Kappaemme-git/codex-startup-pressure-test-skill, 990 stars), Zhang Yiming Perspective (alchaincyf/zhang-yiming-skill, 175 stars), Korean Government Grant Search (djfksjd/ir-search, 391 stars) and Mao Zedong Thinking Partner (zhangtianruiwork-droid/Maoxuan-Changzheng, 443 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Deepsearch Mpro?
LeoYeAI (a GitHub user) maintains it in LeoYeAI/openclaw-master-skills, which has 2,161 GitHub stars. The repository holds 1,235 skills in this directory. The repository was last updated on July 20, 2026.
Source: LeoYeAI/openclaw-master-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.