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Competitors Analysis

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seaworld008
competitors-analysis

Compare competitor repositories using verified code and source evidence when technical competitive analysis or repository profiling is requested.

Overview

Publisherseaworld008
RepositoryCommonly-used-high-value-skills
Skill namecompetitors-analysis
Stars
70
Forks
11
Bundled files
3
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by seaworld008 on GitHub. Read the source before you install it.

Installation

Install the Competitors Analysis AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/seaworld008/Commonly-used-high-value-skills.git /tmp/Commonly-used-high-value-skills
mkdir -p .claude/skills
cp -r /tmp/Commonly-used-high-value-skills/openclaw-skills/competitors-analysis .claude/skills/competitors-analysis
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Competitors Analysis in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Competitors Analysis on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Competitors Analysis is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Competitors Analysis

Evidence-based competitor tracking and analysis. All analysis must be based on actual code, never assumptions.

When to Use

Use this skill when the user wants to:

  • analyze a competitor repository with evidence
  • create or update a competitor profile
  • compare a competing product's technical stack or architecture
  • track competitor changes over time using cloned code

Usage

Recommended flow:

text
clone repo locally
-> inspect source files and metadata
-> cite every factual claim
-> write profile from evidence only
-> run post-analysis verification

CRITICAL: Evidence-Based Analysis Only

在开始分析之前,必须完成以下检查:

Pre-Analysis Checklist

  • 仓库已克隆到本地 ~/Workspace/competitors/{product}/
  • 可以 ls 查看目录结构
  • 可以 cat package.json (或等效配置文件) 读取版本信息
  • 可以 git log -1 确认代码是最新的

如果以上任何一项未完成,停止分析,先完成克隆操作。

Forbidden Patterns (禁止的表述)

禁止原因
"推测..."、"可能..."、"应该..."没有证据支持
"架构图(推测版)"必须基于实际代码
"未公开"、"未披露"如果不知道就不要写
不带来源的技术细节无法验证

Required Patterns (必须的表述)

正确格式示例
技术细节 + (来源: 文件:行号)"使用 better-sqlite3 (来源: package.json:88)"
直接引用 + 来源> "description text" (README.md:3)
版本号 + 来源"版本 1.3.3 (package.json:2)"

Analysis Workflow

Step 1: Clone Repository (必须)

bash
# 创建产品竞品目录
mkdir -p ~/Workspace/competitors/{product-name}

# 克隆竞品仓库 (SSH,失败则重试)
cd ~/Workspace/competitors/{product-name}
git clone git@github.com:org/repo.git

网络问题处理: 中国网络环境可能需要多次重试。

Step 2: Gather Facts (收集事实)

按顺序读取以下文件,记录关键信息:

2.1 项目元数据

bash
# Node.js 项目
cat package.json | head -20      # name, version, description
cat package.json | grep -A50 dependencies

# Python 项目
cat pyproject.toml               # 或 setup.py, requirements.txt

# Rust 项目
cat Cargo.toml

2.2 项目结构

bash
ls -la                           # 根目录结构
ls src/                          # 源码目录
find . -name "*.md" -maxdepth 2  # 文档文件

2.3 核心模块

bash
# 找到入口文件
cat main.js | head -50           # 或 index.js, app.py, main.rs
# 找到核心 helpers/utils
ls src/helpers/ 2>/dev/null || ls src/utils/ 2>/dev/null

2.4 README 和文档

bash
cat README.md | head -100        # 官方描述
cat CHANGELOG.md | head -50      # 版本历史

Step 3: Deep Dive (深入分析)

针对关键技术点,读取具体实现文件:

bash
# 示例:分析 ASR 实现
cat src/helpers/whisper.js       # 读取完整文件
grep -n "class.*Manager" src/helpers/*.js  # 找到核心类

记录格式:

| 文件 | 行号 | 发现 |
|------|------|------|
| whisper.js | 33-35 | 使用 WhisperServerManager |

Step 4: Write Profile (撰写分析)

使用 references/profile_template.md 模板,确保每个技术细节都有来源标注。

Step 5: Post-Analysis Verification (分析后验证)

自检清单:

  • 所有版本号都有来源标注?
  • 所有技术栈都来自 package.json/Cargo.toml?
  • 架构描述基于实际代码结构?
  • 没有"推测"、"可能"等词汇?
  • 对比表中的竞品数据都有来源?

Directory Structure

~/Workspace/competitors/
├── flowzero/              # Flowzero 的竞品
│   ├── openwhispr/        # git clone 的仓库
│   └── ...
└── {product-name}/        # 其他产品

{project}/docs/competitors/
├── README.md              # 索引(标注分析状态)
├── profiles/
│   └── {competitor}.md    # 基于代码的分析
├── landscape/
├── insights/
└── updates/2026/

Templates and Checklists

文档用途
references/profile_template.md竞品分析报告模板
references/analysis_checklist.md分析前/中/后检查清单

关键要求:

  1. 顶部必须标注数据来源路径和 commit hash
  2. 每个技术细节必须有 (来源: 文件:行号)
  3. 引用 README 内容必须标注行号
  4. 无法验证的标记为"待验证"并说明原因
  5. 分析完成后运行检查清单中的验证命令

Tech Stack Analysis Guide

Node.js / JavaScript

信息来源文件关键字段
版本package.jsonversion
依赖package.jsondependencies, devDependencies
入口package.jsonmain, scripts.start
框架package.jsonelectron, react, vite 等

Python

信息来源文件关键字段
版本pyproject.toml[project].version
依赖pyproject.toml / requirements.txtdependencies
入口pyproject.toml[project.scripts]

Rust

信息来源文件关键字段
版本Cargo.toml[package].version
依赖Cargo.toml[dependencies]

Common Mistakes to Avoid

1. 跳过克隆直接分析

❌ 错误: 从 GitHub 网页或 WebFetch 获取信息后直接写分析 ✅ 正确: 必须 git clone 到本地,用 Read 工具读取文件

2. 混合事实和推测

❌ 错误:

markdown
## 技术栈
- Electron (推测基于桌面应用特征)
- 可能使用了 React

✅ 正确:

markdown
## 技术栈 (来源: package.json)
| 依赖 | 版本 | 来源 |
|------|------|------|
| electron | 36.9.5 | package.json:68 |
| react | 19.1.0 | package.json:96 |

3. 使用过时信息

❌ 错误: 分析时不检查 git log,使用过时的代码 ✅ 正确: 分析前运行 git pull,记录分析时的 commit hash

4. 对比表中竞品数据无来源

❌ 错误:

markdown
| 维度 | 竞品 | 我们 |
|------|------|------|
| 支持语言 | 25种 | 58种 |

✅ 正确:

markdown
| 维度 | 竞品 | 来源 | 我们 |
|------|------|------|------|
| 支持语言 | 25种 | modelRegistryData.json:9-35 | 58种 (FunASR 官方文档) |

Scripts

See scripts/update-competitors.sh for repository management.

bash
./scripts/update-competitors.sh clone   # 克隆所有竞品
./scripts/update-competitors.sh pull    # 更新所有竞品
./scripts/update-competitors.sh status  # 检查状态

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Competitors Analysis AI skill do?

Compare competitor repositories using verified code and source evidence when technical competitive analysis or repository profiling is requested.

Why use Competitors Analysis on TypingMind?

Because you install it once and use it with any model. Competitors Analysis is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Competitors Analysis in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/seaworld008/Commonly-used-high-value-skills/tree/main/openclaw-skills/competitors-analysis. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Competitors Analysis?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Competitors Analysis?

As many as you like. As long as a model supports skills, you can use Competitors Analysis with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Competitors Analysis AI skill free?

Yes. It is published on GitHub by seaworld008 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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