apify-easy-competitive-intelligence

Compare original and translation side by side

🇺🇸

Original

English

🇨🇳

Translation

Chinese

Competitive Intelligence

竞争情报

Real-time competitive intelligence powered by live web data via Apify actors. Never answer competitive questions from training knowledge alone. Always gather live data first, then analyze.

通过 Apify actors 获取实时网页数据，提供实时竞争情报。**绝不能仅凭训练知识回答竞争问题。**始终先收集实时数据，再进行分析。

Prerequisites

前置条件

Apify CLI v1.5.0+ (
```
npm install -g apify-cli
```
), or Apify MCP server
Authenticated session (
```
apify login
```
or
```
APIFY_TOKEN
```
env var)

CLI rules: Always pass

--json

--user-agent apify-awesome-skills/apify-easy-competitive-intelligence

, and

2>/dev/null

Run actor:

apify actors call "ACTOR_ID" -i 'INPUT' --user-agent apify-awesome-skills/apify-easy-competitive-intelligence --json 2>/dev/null

→ returns run metadata with

defaultDatasetId

Fetch results:

apify datasets get-items DATASET_ID --user-agent apify-awesome-skills/apify-easy-competitive-intelligence --format json > /tmp/results.json 2>/dev/null

— save locally, parse from file:

Quick extraction:

jq '.[] | "\(.field1) | \(.field2)"' /tmp/results.json

Aggregation:

python3 -c "import json; d=json.load(open('/tmp/results.json')); ..."

Tabular:

--format csv > /tmp/results.csv

python3

with

csv.DictReader

Flags:

--limit N

--offset N

--format json|jsonl|csv|xlsx|xml

Output fields:

apify datasets info DATASET_ID --json | jq .fields

Fetch schema:

apify actors info "ACTOR_ID" --input --user-agent apify-awesome-skills/apify-easy-competitive-intelligence --json 2>/dev/null

If CLI is unavailable and Apify MCP server is connected, use MCP

call-actor

fetch-actor-details

get-actor-output

directly.

Apify CLI v1.5.0+（
```
npm install -g apify-cli
```
），或 Apify MCP server
已认证的会话（
```
apify login
```
或
```
APIFY_TOKEN
```
环境变量）

**CLI 规则：**始终传入

--json

、

--user-agent apify-awesome-skills/apify-easy-competitive-intelligence

和

2>/dev/null

。

运行 actor：

apify actors call "ACTOR_ID" -i 'INPUT' --user-agent apify-awesome-skills/apify-easy-competitive-intelligence --json 2>/dev/null

→ 返回包含

defaultDatasetId

的运行元数据

获取结果：

apify datasets get-items DATASET_ID --user-agent apify-awesome-skills/apify-easy-competitive-intelligence --format json > /tmp/results.json 2>/dev/null

— 保存到本地并从文件解析：

快速提取：

jq '.[] | "\(.field1) | \(.field2)"' /tmp/results.json

聚合：

python3 -c "import json; d=json.load(open('/tmp/results.json')); ..."

表格：

--format csv > /tmp/results.csv

+ 使用

csv.DictReader

的

python3

标志：

--limit N

、

--offset N

、

--format json|jsonl|csv|xlsx|xml

输出字段：

apify datasets info DATASET_ID --json | jq .fields

获取 schema：

apify actors info "ACTOR_ID" --input --user-agent apify-awesome-skills/apify-easy-competitive-intelligence --json 2>/dev/null

如果 CLI 不可用但已连接 Apify MCP server，请直接使用 MCP

call-actor

、

fetch-actor-details

或

get-actor-output

。

Authentication

身份认证

If a CLI command fails with an auth error, authenticate using one of these methods:

OAuth (interactive):
```
apify login
```
(opens browser)
Environment variable:
```
export APIFY_TOKEN=your_token_here
```
From .env file:
```
source .env
```
(if the file contains
```
APIFY_TOKEN=...
```
)

Generate token: https://console.apify.com/settings/integrations

如果 CLI 命令因身份认证错误而失败，请使用以下任一方式进行身份认证：

OAuth（交互式）：
```
apify login
```
（会打开浏览器）
环境变量：
```
export APIFY_TOKEN=your_token_here
```
从 .env 文件读取：
```
source .env
```
（如果文件中包含
```
APIFY_TOKEN=...
```
）

生成 token：https://console.apify.com/settings/integrations

Actor Registry

Actor 注册表

Every actor call follows three steps:

Read — find the actor's section in
```
reference/actor-schemas.md
```
. Use the exact verified input and follow the "How to find" instructions for URLs/slugs.
Discover — verify platform URLs and slugs (e.g. via SERP) as described in the actor's schema section. Do not guess — wrong slugs silently return empty or wrong data.
Run — call the actor with verified input.

Alternatively, fetch the live schema:

apify actors info "ACTOR_ID" --user-agent apify-awesome-skills/apify-easy-competitive-intelligence --input --json 2>/dev/null

Data Need	Actor	Notes
Google SERP	`apify/google-search-scraper`	Supports country/language. SERP snippets contain ratings & review counts
Page scrape	`apify/website-content-crawler`	proxyConfiguration REQUIRED. Returns markdown
RAG browse	`apify/rag-web-browser`	Search + scrape in one call. Good fallback
LinkedIn company	`dev_fusion/Linkedin-Company-Scraper`	Output in KV store, not dataset
LinkedIn jobs	`curious_coder/linkedin-jobs-scraper`	Requires LinkedIn search URL, NOT keywords
Crunchbase	`pratikdani/crunchbase-companies-scraper`	Single company URL per call
Amazon product	`junglee/Amazon-crawler`	Product or category URLs
Amazon reviews	`web_wanderer/amazon-reviews-extractor`	May return 0 for some products
Walmart product	`e-commerce/walmart-product-detail-scraper`	May return empty
Google Maps reviews	`compass/Google-Maps-Reviews-Scraper`	Use full Google Maps place URL
G2 reviews	`automation-lab/g2-scraper`	NPS, ratings, switching data. $0.04/run
Capterra reviews	`zen-studio/capterra-reviews-scraper`	$1.99/1K
Gartner Peer Insights	—	No working actor. Use SERP snippet mining as fallback
Glassdoor	`memo23/glassdoor-scraper-ppr`	Reviews, salaries, culture, ratings
Reddit	`harshmaur/reddit-scraper`	Posts + full comment threads
Google Play reviews	`neatrat/google-play-store-reviews-scraper`	App ID or Play Store URL
App Store	`jdtpnjtp/apple-app-store-scraper`	Requires SHADER proxy — may not be available on all plans
SimilarWeb	`pro100chok/similarweb-scraper`	Minimum 10 domains per call
Google News	`data_xplorer/google-news-scraper-fast`	No boolean operators in keywords
Wayback Machine	`andok/wayback-machine-scraper`	Full URL including path

每次调用 actor 均遵循三个步骤：

读取 — 在
```
reference/actor-schemas.md
```
中找到该 actor 的章节。使用经过验证的准确输入，并遵循有关如何查找 URL/slug 的说明。
发现 — 按照 actor 的 schema 章节所述验证平台 URL 和 slug（例如通过 SERP）。不要猜测——错误的 slug 会静默返回空数据或错误数据。
运行 — 使用经过验证的输入调用 actor。

也可以获取实时 schema：

apify actors info "ACTOR_ID" --user-agent apify-awesome-skills/apify-easy-competitive-intelligence --input --json 2>/dev/null

数据需求	Actor	备注
Google SERP	`apify/google-search-scraper`	支持国家/语言。SERP 摘要包含评分和评论数量
网页抓取	`apify/website-content-crawler`	必须设置 proxyConfiguration。返回 markdown
RAG 浏览	`apify/rag-web-browser`	在一次调用中完成搜索和抓取。适合作为备用方案
LinkedIn 公司	`dev_fusion/Linkedin-Company-Scraper`	输出位于 KV store，而非 dataset
LinkedIn 职位	`curious_coder/linkedin-jobs-scraper`	需要 LinkedIn 搜索 URL，不能使用关键词
Crunchbase	`pratikdani/crunchbase-companies-scraper`	每次调用仅支持一个公司 URL
Amazon 商品	`junglee/Amazon-crawler`	支持商品或分类 URL
Amazon 评论	`web_wanderer/amazon-reviews-extractor`	某些商品可能返回 0 条结果
Walmart 商品	`e-commerce/walmart-product-detail-scraper`	可能返回空结果
Google Maps 评论	`compass/Google-Maps-Reviews-Scraper`	使用完整的 Google Maps 地点 URL
G2 评论	`automation-lab/g2-scraper`	包含 NPS、评分和客户转换数据。$0.04/run
Capterra 评论	`zen-studio/capterra-reviews-scraper`	$1.99/1K
Gartner Peer Insights	—	没有可用的 actor。改用 SERP 摘要提取作为备用方案
Glassdoor	`memo23/glassdoor-scraper-ppr`	包含评论、薪资、文化和评分
Reddit	`harshmaur/reddit-scraper`	包含帖子和完整评论线程
Google Play 评论	`neatrat/google-play-store-reviews-scraper`	需要 App ID 或 Play Store URL
App Store	`jdtpnjtp/apple-app-store-scraper`	需要 SHADER proxy，可能并非所有套餐都可用
SimilarWeb	`pro100chok/similarweb-scraper`	每次调用至少需要 10 个域名
Google News	`data_xplorer/google-news-scraper-fast`	关键词中不能使用布尔运算符
Wayback Machine	`andok/wayback-machine-scraper`	需要包含路径的完整 URL

Core Workflow

核心工作流

Step 0: Understand the User (once, at start)

步骤 0：了解用户（仅在开始时执行一次）

Clarify before gathering data:

Role — Analyzed company, competitor, investor, consultant?
Decision — Entering market, defending position, choosing vendor, building battlecard?
Autonomy — Checkpoints after initial findings, or autopilot?

收集数据前先确认：

角色 — 分析对象是公司、竞争对手、投资者，还是顾问？
决策 — 进入市场、防御定位、选择供应商，还是制作 battlecard？
自主程度 — 在初步发现后设置检查点，还是全自动执行？

Steps 1–7

步骤 1–7

Clarify scope — Identify competitors. Select module(s). Default geography: US.
Read module reference — Load
```
reference/modules/<module>.md
```
for gathering + analysis instructions.
Gather live data — For each actor call, follow the three-step pattern: Read (actor-schemas.md) → Discover (SERP for URLs) → Run (call actor). Use PRIMARILY actors from the Actor Registry above.
Checkpoint (if not autopilot) — Present first findings, confirm direction.
Analyze — Select framework, lead with narrative, support with tables.
Verify — Run pre-delivery verification (
```
reference/verification-checklist.md
```
). Check: every claim has a source URL, every major finding has a confidence label, inferences are labeled as such. Remove any ungrounded claims.
Deliver — End with strategic recommendations framed for the user's role.

明确范围 — 识别竞争对手。选择一个或多个模块。默认地域：美国。
阅读模块参考 — 加载
```
reference/modules/<module>.md
```
，了解数据收集与分析说明。
收集实时数据 — 对于每次 actor 调用，遵循“读取（actor-schemas.md）→ 发现（通过 SERP 查找 URL）→ 运行（调用 actor）”的模式。优先使用上述 Actor 注册表中的 actors。
检查点（如果未启用自动驾驶）— 展示初步发现并确认后续方向。
分析 — 选择合适的框架，以叙述性结论为主，并使用表格提供支持。
验证 — 执行交付前验证（
```
reference/verification-checklist.md
```
）。检查每项主张是否有来源 URL、每项主要发现是否都有置信度标签，以及推断是否已明确标注。删除所有无依据的主张。
交付 — 以符合用户角色的方式给出战略性建议作为结尾。

Framework Selection

框架选择

Situation	Framework
Profile one competitor	SWOT
Market dynamics & forces	Porter's Five Forces
Visual position comparison	Strategy Canvas (Blue Ocean)
Why customers switch	Jobs-to-be-Done
Find white space	Positioning Matrix (2x2)
Predict competitor reaction	Competitive Response Matrix

情境	框架
分析单个竞争对手	SWOT
市场动态与竞争力量	Porter's Five Forces
可视化定位对比	Strategy Canvas (Blue Ocean)
客户为何更换供应商	Jobs-to-be-Done
寻找市场空白	Positioning Matrix (2x2)
预测竞争对手的反应	Competitive Response Matrix

Data Collection Rules

数据收集规则

Prefer structured actors over
```
website-content-crawler
```
when a dedicated actor exists.
Cost budget — 3-8 actor calls per snapshot. Track total, warn at 15+.
Parallelize independent
```
call-actor
```
calls in a single response.
Failures — Report every failure explicitly (actor, input, error). Retry with corrected input if the cause is obvious. If retry fails, try
```
rag-web-browser
```
as fallback. Never silently skip a failed data source.
Cite everything — Include source URLs for every data point.
Async for long runs — Set
```
async: true
```
for actors >30s, poll with
```
get-actor-run
```
.
Protected platforms — Do NOT use
```
website-content-crawler
```
or
```
rag-web-browser
```
for: g2.com, capterra.com, gartner.com, glassdoor.com, reddit.com, linkedin.com. Use dedicated actors.

优先使用结构化 actors — 如果存在专用 actor，应优先于
```
website-content-crawler
```
。
成本预算 — 每次快照执行 3-8 次 actor 调用。跟踪总调用量，达到 15+ 时发出警告。
并行处理 — 在一次响应中并行执行相互独立的
```
call-actor
```
调用。
失败处理 — 明确报告每次失败（actor、输入和错误）。如果原因明显，则使用修正后的输入重试。如果重试失败，改用
```
rag-web-browser
```
作为备用方案。绝不能静默跳过失败的数据源。
引用所有内容 — 每个数据点都必须包含来源 URL。
长时间运行使用异步模式 — 对运行时间超过 30 秒的 actor 设置
```
async: true
```
，并使用
```
get-actor-run
```
轮询状态。
受保护的平台 — 对 g2.com、capterra.com、gartner.com、glassdoor.com、reddit.com 和 linkedin.com，不得使用
```
website-content-crawler
```
或
```
rag-web-browser
```
，应改用专用 actor。

Apify vs. WebSearch

Apify 与 WebSearch

Apify required: review sites (G2, Capterra, Gartner, Glassdoor), LinkedIn, Reddit, Amazon, Walmart, app stores, SimilarWeb, Crunchbase, Wayback Machine, Google Maps reviews, news (Google News actor).

WebSearch/WebFetch sufficient (Claude Code built-in tools): competitor discovery, general company info, blog posts, publicly accessible pricing pages.

必须使用 Apify：评论网站（G2、Capterra、Gartner、Glassdoor）、LinkedIn、Reddit、Amazon、Walmart、应用商店、SimilarWeb、Crunchbase、Wayback Machine、Google Maps 评论和新闻（Google News actor）。

WebSearch/WebFetch 即可满足需求（Claude Code 内置工具）：竞争对手发现、公司基本信息、博客文章和公开定价页面。

Data Validation & Grounding

数据验证与依据约束

Every factual claim needs a source URL. No link = not a fact.
Confidence labels are mandatory. Mark every major finding: High (primary source), Medium (2+ third-party sources), Low (single third-party source). Format:
```
[Confidence | Source]
```
. No report without labels.
Data tiers: Verified (primary source) → Reported (third-party, attribute) → Inferred (label as "this suggests...") → Ungrounded (omit).
Numbers are dangerous — employee counts, revenue, funding change fast. Always cite source and date.
Empty results ARE intelligence — 0 jobs = not hiring, 0 SimilarWeb = small site, 12 reviews = low adoption.
Cross-reference — Single-source claims are unverified. Multi-source (G2 + Capterra + Reddit) = pattern.

**每项事实性主张都需要来源 URL。**没有链接就不能视为事实。
**置信度标签为必填项。**必须标记每项主要发现：高（一手来源）、中（2+ 个第三方来源）、低（单一第三方来源）。格式：
```
[置信度 | 来源]
```
。任何报告都必须包含标签。
数据层级：已验证（一手来源）→ 已报告（第三方归因）→ 推断（标注为“这表明……”）→ 无依据（省略）。
数字容易失效 — 员工人数、收入和融资情况变化迅速。始终注明来源和日期。
空结果也属于情报 — 0 个职位意味着没有招聘，0 条 SimilarWeb 数据意味着网站规模较小，12 条评论意味着采用度较低。
交叉验证 — 单一来源的主张尚未得到验证。多个来源（如 G2 + Capterra + Reddit）一致则可视为模式。

Module Selection

模块选择

User says...	Module	Reference
"Analyze [competitor]", "Tell me about [company]"	Competitor Snapshot	`reference/modules/competitor-snapshot.md`
"Compare pricing", "How much does [X] cost"	Pricing Intelligence	`reference/modules/pricing-intelligence.md`
"Pricing details", "per-use-case costs", "tiers", "add-ons"	Pricing Deep Dive	`reference/modules/pricing-deep-dive.md`
"What do customers think", "Reviews", "Pain points"	Review Intelligence	`reference/modules/review-intelligence.md`
"What are they hiring for", "Job postings"	Hiring Signals	`reference/modules/hiring-signals.md`
"How do they rank", "Content strategy", "SEO"	Content & SEO	`reference/modules/content-seo.md`
"Who are the players", "Market landscape"	Market Landscape	`reference/modules/market-landscape.md`
"Full battlecard", "Deep analysis", "Board prep"	Multi-Module	`reference/multi-module-playbook.md`

用户提出……	模块	参考文件
“分析 [竞争对手]”、“介绍 [公司]”	Competitor Snapshot	`reference/modules/competitor-snapshot.md`
“比较定价”、“[X] 的价格是多少”	Pricing Intelligence	`reference/modules/pricing-intelligence.md`
“定价详情”、“各使用场景成本”、“套餐”、“附加功能”	Pricing Deep Dive	`reference/modules/pricing-deep-dive.md`
“客户如何看待”、“评论”、“痛点”	Review Intelligence	`reference/modules/review-intelligence.md`
“他们在招聘哪些职位”、“职位发布”	Hiring Signals	`reference/modules/hiring-signals.md`
“他们如何排名”、“内容策略”、“SEO”	Content & SEO	`reference/modules/content-seo.md`
“市场参与者有哪些”、“市场格局”	Market Landscape	`reference/modules/market-landscape.md`
“完整 battlecard”、“深入分析”、“董事会准备”	Multi-Module	`reference/multi-module-playbook.md`