startup-competitors

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Chinese

Startup Competitors

初创企业竞品分析

Deep competitive intelligence that goes beyond surface-level profiles. Produces actionable battle cards, pricing landscape analysis, and strategic vulnerability mapping using real web data.

深度竞品情报分析，不止于表面的竞品画像。利用真实网络数据生成可落地的战斗卡片、定价格局分析及战略弱点图谱。

How It Works

工作原理

INTAKE → RESEARCH (3 parallel waves) → SYNTHESIS → BATTLE CARDS

The process is focused: understand the product, research competitors deeply across 3 dimensions, synthesize findings, and produce actionable output. Typical runtime: 15-25 minutes in Claude Code (parallel agents), 30-45 minutes in Claude.ai (sequential).

INTAKE → RESEARCH (3 parallel waves) → SYNTHESIS → BATTLE CARDS

该流程目标明确：先了解产品，从3个维度深度调研竞品，再整合分析结果，最终生成可落地的输出内容。在Claude Code（并行Agent）中运行时长通常为15-25分钟，在Claude.ai（串行模式）中则为30-45分钟。

Language

语言设置

Default output language is English. If the user writes in another language or explicitly requests one, use that language for all outputs instead.

默认输出语言为English。若用户使用其他语言提问或明确指定语言，则所有输出均使用该语言。

Phase 1: Intake

第一阶段：信息收集

Short and focused — 1-2 rounds of questions, not an extended interview. The goal is just enough context to run targeted research.

简短聚焦——仅1-2轮提问，而非冗长访谈。目标是获取足够上下文以开展针对性调研。

Check for Prior startup-design Work

检查是否有前置startup-design工作成果

Before asking questions, check if a

startup-design

session has already been completed for this project. Look for these files in the working directory or subdirectories:

```
01-discovery/competitor-landscape.md
```
— competitor profiles and analysis
```
01-discovery/market-analysis.md
```
— market size, trends, regulatory
```
01-discovery/target-audience.md
```
— customer personas, pain points
```
00-intake/brief.md
```
— product description and context

If these files exist, read them and use the data as a head start:

Extract the product description, target market, and known competitors from the brief
Use the competitor list from
```
competitor-landscape.md
```
as the starting point for deeper analysis (startup-design profiles 5-8 competitors at surface level — this skill goes much deeper on each)
Pull market size and trends from
```
market-analysis.md
```
to contextualize the competitive landscape
Use customer pain points from
```
target-audience.md
```
to focus the sentiment mining on what matters most

Tell the user: "I found data from a previous startup-design session. I'll use it as a starting point and go deeper on the competitive analysis."

Skip the intake interview entirely if the startup-design files provide enough context. Go straight to research.

提问前，先检查当前项目是否已完成过

startup-design

会话。查看工作目录或子目录中是否存在以下文件：

```
01-discovery/competitor-landscape.md
```
——竞品画像与分析
```
01-discovery/market-analysis.md
```
——市场规模、趋势、监管情况
```
01-discovery/target-audience.md
```
——客户画像、痛点
```
00-intake/brief.md
```
——产品描述与背景

若存在这些文件，读取并利用其中数据作为调研起点：

从brief中提取产品描述、目标市场及已知竞品
将
```
competitor-landscape.md
```
中的竞品列表作为深度分析的起点（startup-design仅对5-8个竞品进行表层画像——本技能会对每个竞品进行更深入的分析）
从
```
market-analysis.md
```
中提取市场规模与趋势，为竞争格局分析提供上下文
利用
```
target-audience.md
```
中的客户痛点，聚焦挖掘最关键的反馈信息

告知用户：“我发现了之前startup-design会话的相关数据。我将以此为起点，开展更深入的竞品分析。”

若startup-design文件已提供足够上下文，可完全跳过信息收集访谈，直接进入调研阶段。

What to Ask (if no prior data exists)

需提问的内容（若无前置数据）

Round 1 — The basics:

What's your product/idea? (one sentence is fine)
What problem does it solve and for whom?
What market/category are you in?
Do you know any competitors already? (names, URLs)

Round 2 — Sharpening (only if needed):

What geography/market are you targeting?
What's your pricing model or range?
What do you consider your key differentiator?

Don't over-interview. If the user gives a clear description upfront, skip straight to research. The competitive analysis itself will surface what matters.

第一轮——基础信息：

你的产品/创意是什么？（一句话即可）
它解决了什么问题，面向哪些用户？
你所处的市场/品类是什么？
你是否已知一些竞争对手？（名称、网址）

第二轮——细化信息（仅在需要时）：

你的目标地域/市场是哪里？
你的定价模式或价格范围是什么？
你认为自身的核心差异化优势是什么？

避免过度访谈。若用户已提供清晰描述，直接进入调研阶段。竞品分析本身会挖掘出关键信息。

Output

输出

Save to

{project-name}/intake.md

— a brief summary of the product, market, and known competitors. If built on startup-design data, note the source files used. The project name should be derived from the product/market (kebab-case, e.g.,

ai-email-assistant

Create

{project-name}/PROGRESS.md

with: project name, skill name (

startup-competitors

), start date, language, research mode (Live / Knowledge-Based), and a phase checklist. Update it after each phase completes. If PROGRESS.md already exists from a previous session, resume from the last incomplete phase.

将内容保存至

{project-name}/intake.md

——包含产品、市场及已知竞品的简要总结。若基于startup-design数据构建，需注明所使用的源文件。项目名称应从产品/市场名称衍生而来（采用短横线分隔的小写格式，例如

ai-email-assistant

）。

创建

{project-name}/PROGRESS.md

，包含：项目名称、技能名称（

startup-competitors

）、启动日期、语言、调研模式（实时/基于知识库）及阶段检查清单。完成每个阶段后更新该文件。若PROGRESS.md已存在于之前的会话中，从最后未完成的阶段继续。

Phase 2: Research

第二阶段：调研

Three parallel research waves, each attacking the competitive landscape from a different angle. Together they produce a 360-degree view.

三个并行调研模块，从不同角度切入竞争格局，共同构建全方位视图。

Environment Detection

环境检测

Check if the

Agent

tool is available:

Agent tool available (Claude Code): Spawn all agents within each wave in parallel. This is faster.
Agent tool NOT available (Claude.ai, web): Execute research sequentially, following the same templates. Same depth, just slower.

检查是否可使用

Agent

工具：

Agent工具可用（Claude Code）： 在每个模块中并行启动所有Agent，速度更快。
Agent工具不可用（Claude.ai、网页端）： 按顺序执行调研，遵循相同模板。调研深度一致，仅速度较慢。

Web Search

网络搜索

This skill requires WebSearch for real data. If WebSearch is unavailable or denied, fall back to Knowledge-Based Mode: use training data, mark all findings with [Knowledge-Based — verify independently], and reduce confidence ratings by one level.

Reference: Read
references/research-principles.md
before starting any wave. It defines source quality tiers, cross-referencing rules, and how to handle data gaps.

本技能需要WebSearch工具获取真实数据。若WebSearch不可用或被禁用，切换至基于知识库模式：使用训练数据，所有分析结果标记**[基于知识库——请独立验证]**，并将置信等级降低一级。

参考： 启动任何模块前，请阅读
references/research-principles.md
。该文件定义了来源质量层级、交叉验证规则及数据缺口处理方式。

Wave 1: Competitor Profiles + Pricing Intelligence

模块1：竞品画像 + 定价情报

Reference: Read
references/research-wave-1-profiles-pricing.md
for agent templates.

Two agents (or two sequential blocks):

A1: Competitor Deep-Dives — Identify and profile 5-8 direct competitors plus 2-3 adjacent solutions (broader platforms, manual alternatives, tools from neighboring categories that compete for the same budget). For each: product, features, team size, funding, traction signals, strengths, weaknesses. Go beyond their marketing page — check reviews, job postings, and funding data.

A2: Pricing Intelligence — For each competitor: reverse-engineer the pricing model. Not just "it costs $49/mo" but: what's the value metric (per seat? per usage? flat?), how do tiers differentiate, what pricing psychology do they use (anchoring, decoy, charm pricing), what's the switching cost (technical, contractual, emotional). Build a tier-by-tier comparison.

参考： 执行模块1前，请阅读
references/research-wave-1-profiles-pricing.md
中的Agent模板。

两个Agent（或两个串行模块）：

A1：竞品深度调研——识别并分析5-8个直接竞品，外加2-3个相邻解决方案（更广泛的平台、手动替代方案、来自相邻品类且争夺相同预算的工具）。针对每个竞品，分析：产品、功能、团队规模、融资情况、增长信号、优势、劣势。不要仅停留在营销页面——查看评论、招聘信息及融资数据。

A2：定价情报——针对每个竞品，逆向拆解其定价模式。不仅是“每月49美元”，还要分析：价值度量标准（按席位？按使用量？固定价格？）、不同套餐的差异、采用的定价心理学策略（锚定、诱饵、魅力定价）、转换成本（技术、合同、情感层面）。构建套餐对比表。

Wave 2: Customer Sentiment Mining

模块2：客户反馈挖掘

Reference: Read
references/research-wave-2-sentiment-mining.md
for agent templates.

Two agents (or two sequential blocks):

B1: Review Mining — Mine G2, Capterra, TrustRadius, Product Hunt, and App Store reviews for each competitor. Extract patterns: what do people praise? What do they complain about? What features do they request? Organize by competitor and by pain theme. Include verbatim quotes.

B2: Forum & Community Mining — Mine Reddit, Indie Hackers, Hacker News, Quora, and niche communities. Find: complaints about existing tools, "what do you use for X?" threads, migration stories, workaround discussions. Build a language map — the exact words customers use to describe their problems and desires. Identify churn signals — why people leave each competitor.

参考： 执行模块2前，请阅读
references/research-wave-2-sentiment-mining.md
中的Agent模板。

两个Agent（或两个串行模块）：

B1：评论挖掘——挖掘每个竞品在G2、Capterra、TrustRadius、Product Hunt及应用商店中的评论。提取规律：用户称赞什么？抱怨什么？请求哪些功能？按竞品及痛点主题整理。包含原文引用。

B2：论坛与社区挖掘——挖掘Reddit、Indie Hackers、Hacker News、Quora及垂直社区中的内容。寻找：对现有工具的抱怨、“你用什么工具做X？”的讨论帖、迁移经历、 workaround讨论。构建语言图谱——客户描述问题与需求时使用的具体措辞。识别流失信号——用户离开每个竞品的原因。

Wave 3: GTM & Strategic Signals

模块3：GTM与战略信号

Reference: Read
references/research-wave-3-gtm-signals.md
for agent templates.

Two agents (or two sequential blocks):

C1: Go-to-Market Analysis — For each competitor: primary acquisition channel, sales motion (self-serve vs. sales-led), content strategy (blog frequency, topics, quality), social presence, paid advertising signals, partnership plays. Build a channel opportunity map showing competitor saturation vs. opportunity per channel.

C2: Strategic & Growth Signals — Funding trajectory (rounds, investors, timing), hiring patterns (engineering-heavy = building, sales-heavy = scaling, support-heavy = struggling), content/SEO footprint (what keywords they rank for, where the gaps are), product roadmap signals from changelogs and public statements. Identify content pillars each competitor owns and which topics nobody covers well.

参考： 执行模块3前，请阅读
references/research-wave-3-gtm-signals.md
中的Agent模板。

两个Agent（或两个串行模块）：

C1：GTM策略分析——针对每个竞品，分析：主要获客渠道、销售模式（自助 vs. 销售主导）、内容策略（博客频率、主题、质量）、社交媒体存在感、付费广告信号、合作伙伴策略。构建渠道机会图谱，展示各渠道的竞品饱和度与机会空间。

C2：战略与增长信号——融资轨迹（轮次、投资者、时间节点）、招聘模式（工程师占比高=产品研发阶段，销售占比高=扩张阶段，支持人员占比高=困境阶段）、内容/SEO覆盖（排名关键词、空白领域）、从更新日志及公开声明中提取的产品路线图信号。识别每个竞品占据的内容支柱，以及无人深耕的主题。

Post-Research Checkpoint

调研后检查点

After all three waves complete, before synthesis, briefly present what the research found to the user: how many competitors were profiled, the top customer pain themes, the most notable strategic signals (funding, hiring, GTM patterns). Ask: "Does this align with your expectations? Any competitors to add or remove before I synthesize?"

Keep it to one message — this is a quick alignment check, not a full report.

完成三个模块的调研后，在整合分析前，向用户简要汇报调研成果：分析了多少个竞品、最核心的客户痛点主题、最值得关注的战略信号（融资、招聘、GTM模式）。询问用户：“这与你的预期是否一致？在我整合分析前，是否需要添加或移除某些竞品？”

控制在一条消息内——这只是快速对齐，而非完整报告。

Phase 3: Synthesis

第三阶段：整合分析

Reference: Read
references/research-synthesis.md
for synthesis protocol and battle card template.

After the checkpoint, synthesize raw findings into strategic deliverables. This step creates the real value — it's not reporting, it's pattern-matching across data sources.

参考： 执行整合分析前，请阅读
references/research-synthesis.md
中的分析规范及战斗卡片模板。

完成检查点后，将原始调研结果整合为战略交付物。这一步是核心价值所在——并非简单汇报，而是跨数据源的模式匹配。

How to Synthesize

整合分析方法

Read all raw files before writing anything
Connect findings across waves: pricing gaps + customer complaints + hiring signals = strategic opportunities
Identify contradictions between sources and explain which to trust
Rate confidence for each major claim (High / Medium / Low)
Surface strategic implications — not just facts, but what they mean
Aggregate all data gaps from raw files into a dedicated "Data Gaps & Research Limitations" section in the competitors-report — every analysis has blind spots, and being explicit about them prevents false confidence
Include adjacent solutions (broader platforms, manual alternatives, tools from neighboring categories) — customers don't just choose between direct competitors, they choose between "good enough" options from adjacent spaces

写作前通读所有原始文件
跨模块关联分析结果：定价缺口 + 客户抱怨 + 招聘信号 = 战略机会
识别数据源之间的矛盾，并说明应采信哪一方
为每个主要结论标注置信等级（高/中/低）
提炼战略意义——不仅呈现事实，还要说明其影响
将所有原始文件中的数据缺口汇总到竞品报告的“数据缺口与调研局限性”章节——任何分析都存在盲区，明确指出可避免错误置信
纳入相邻解决方案（更广泛的平台、手动替代方案、来自相邻品类的工具）——客户不仅在直接竞品中做选择，还会考虑相邻领域的“够用”选项

Output Files

输出文件

Every deliverable file must start with a standardized header:

# {Title}: {product}

followed by

*Skill: startup-competitors | Generated: {date}*

. Every deliverable must end with Red Flags, Yellow Flags, and Sources sections.

{project-name}/competitors-report.md
— The main deliverable:

Executive summary (5-sentence competitive landscape overview)
Market concentration assessment (fragmented / consolidating / dominated)
Key findings per research dimension
Strategic opportunities (where to compete)
Strategic risks (where to avoid)
Competitive moat assessment (network effects, switching costs, data moat, brand, scale)
Data gaps & research limitations (mandatory — aggregate from all raw files)
Red flags and yellow flags

{project-name}/competitive-matrix.md
— Feature comparison table:

Features as rows, competitors as columns
Rating: strong / adequate / weak / missing
Highlight gaps where no competitor serves well
Your product included (or placeholder if pre-launch)

{project-name}/pricing-landscape.md
— Dedicated pricing analysis:

Tier-by-tier comparison across all competitors
Value metric analysis (what each charges for and why)
Pricing psychology breakdown (anchoring, decoy, freemium strategies)
Price positioning map (axes: price vs. feature depth)
Pricing whitespace — where there's room to position
Switching cost matrix (per competitor: technical, contractual, emotional)

{project-name}/battle-cards/{competitor-name}.md
— One per competitor:

One-page format: who they are, their strengths, their weaknesses
How to win against them (specific talking points)
When they win over you (be honest)
Customer objections and responses
Key vulnerability to exploit
Churn signals (why their customers leave)

所有交付物文件必须以标准化开头：

# {标题}: {产品名称}

，随后是

*Skill: startup-competitors | 生成日期: {日期}*

。所有交付物必须包含“红色预警”、“黄色预警”及“来源”章节。

{project-name}/competitors-report.md
——核心交付物：

执行摘要（5句话以内的竞争格局概述）
市场集中度评估（碎片化/整合中/垄断）
各调研维度的关键发现
战略机会（可切入的方向）
战略风险（需规避的领域）
竞争护城河评估（网络效应、转换成本、数据护城河、品牌、规模）
数据缺口与调研局限性（必填——汇总所有原始文件中的缺口）
红色预警与黄色预警

{project-name}/competitive-matrix.md
——功能对比表：

行：功能；列：竞品
评级：强/合格/弱/缺失
突出所有竞品都未很好覆盖的功能缺口
包含你的产品（若未上线则用占位符）

{project-name}/pricing-landscape.md
——专项定价分析：

所有竞品的套餐对比
价值度量分析（每个竞品的收费依据及原因）
定价心理学拆解（锚定、诱饵、免费增值策略）
价格定位图谱（坐标轴：价格 vs. 功能深度）
定价空白领域——可切入的定价区间
转换成本矩阵（每个竞品的技术、合同、情感转换成本）

{project-name}/battle-cards/{competitor-name}.md
——每个竞品对应一份：

一页纸格式：竞品概况、优势、劣势
击败竞品的具体话术
竞品的胜出场景（如实呈现）
客户异议及应对方案
可利用的核心弱点
流失信号（用户离开该竞品的原因）

Raw Data

原始数据

Keep raw research files in

{project-name}/raw/

for reference:

```
competitor-profiles.md
```
```
pricing-intelligence.md
```
```
review-mining.md
```
```
forum-mining.md
```
```
gtm-analysis.md
```
```
strategic-signals.md
```

将原始调研文件保存在

{project-name}/raw/

目录中以供参考：

```
competitor-profiles.md
```
```
pricing-intelligence.md
```
```
review-mining.md
```
```
forum-mining.md
```
```
gtm-analysis.md
```
```
strategic-signals.md
```

Honesty Protocol

诚信规范

Reference: Read
references/honesty-protocol.md
for full protocol and anti-pattern details.

Competitive intelligence is only useful if it's honest. Core rules apply (label claims, quantify, declare gaps), plus competitive-intelligence-specific additions:

No cheerleading. If a competitor is objectively better at something, say so. Battle cards that ignore competitor strengths are useless in real sales conversations.
Label claims. Use [Data], [Estimate], [Assumption], [Opinion] tags. Never present guesses as facts.
Quantify. "$12M ARR growing 40% YoY" not "they're growing fast."
Date everything. Flag data older than 12 months.
Declare gaps. "DATA GAP: Could not find reliable data on [X]" is always better than fabrication.
Surface red flags. If the competitive landscape looks brutal, say so directly.
Challenge confirmation bias. When research confirms what the founder already believes, probe deeper. Look for disconfirming evidence.

See

references/honesty-protocol.md

for the full anti-pattern table (6 entries) and detailed protocol.

参考： 请阅读
references/honesty-protocol.md
获取完整规范及反模式细节。

竞品情报只有真实才有用。除了核心规则（标注结论、量化、说明缺口），还需遵循竞品情报专属规范：

不盲目吹捧。若竞品在某方面确实更优秀，如实说明。忽略竞品优势的战斗卡片在实际销售对话中毫无用处。
标注结论来源。使用**[数据]、[估算]、[假设]、[观点]**标签。绝不将猜测伪装成事实。
量化描述。使用“年经常性收入1200万美元，同比增长40%”而非“增长迅速”。
标注数据日期。标记超过12个月的旧数据。
说明数据缺口。“数据缺口：无法找到关于[X]的可靠数据”永远比编造数据要好。
突出红色预警。若竞争格局极其严峻，直接告知用户。
挑战确认偏差。若调研结果验证了创始人已有的想法，需进一步深挖，寻找相反证据。

详见

references/honesty-protocol.md

中的完整反模式表格（6项）及详细规范。

Reference Files

参考文件

Read only what you need for the current phase.

File	When to Read	~Lines	Purpose
`honesty-protocol.md`	Start of session	~72	Full honesty protocol with anti-patterns
`research-principles.md`	Before starting Phase 2	~54	Source quality, cross-referencing, data gaps
`research-wave-1-profiles-pricing.md`	When running Wave 1	~186	Agent templates for profiles + pricing
`research-wave-2-sentiment-mining.md`	When running Wave 2	~189	Agent templates for review + forum mining
`research-wave-3-gtm-signals.md`	When running Wave 3	~192	Agent templates for GTM + strategic signals
`research-synthesis.md`	After all waves complete	~231	How to synthesize + battle card template

仅阅读当前阶段所需的文件。

文件	阅读时机	约行数	用途
`honesty-protocol.md`	会话开始时	~72	完整诚信规范及反模式
`research-principles.md`	第二阶段开始前	~54	来源质量、交叉验证、数据缺口处理
`research-wave-1-profiles-pricing.md`	执行模块1时	~186	竞品画像+定价的Agent模板
`research-wave-2-sentiment-mining.md`	执行模块2时	~189	评论+论坛挖掘的Agent模板
`research-wave-3-gtm-signals.md`	执行模块3时	~192	GTM+战略信号的Agent模板
`research-synthesis.md`	所有模块完成后	~231	整合分析方法+战斗卡片模板