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kostja94

traffic-analysis

作者 Kostja Zhang · GitHub ↗ · v1.1.0 · MIT-0
cross-platform ✓ 安全检测通过
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在 OpenClaw 中安装
/install kostja94-traffic-analysis
功能描述
When the user wants to analyze website traffic sources, attribution, or dark traffic. Also use when the user mentions "traffic sources," "dark traffic," "dir...
使用说明 (SKILL.md)

Analytics: Traffic

Guides website traffic analysis across all channels (organic, paid, social, referral, direct). Covers traffic source attribution, dark traffic identification, and multi-channel reporting.

When invoking: On first use, if helpful, open with 1-2 sentences on what this skill covers and why it matters, then provide the main output. On subsequent use or when the user asks to skip, go directly to the main output.

Scope

  • Traffic sources: Organic, paid, social, referral, direct, email
  • Dark traffic: Unattributed visits labeled as "Direct / None"
  • Attribution: UTM tagging, segmenting, reporting accuracy

Branded vs. Non-Branded Traffic (Organic)

Type Characteristics
Branded Higher CTR, conversion, purchase intent; users closer to funnel bottom
Non-branded Touchpoint with future users; most sites get more non-brand traffic; competition fiercer

Brand traffic grows over time as brand awareness increases.

Bot Traffic

A large share of traffic can be bot traffic—RPA, search crawlers, spiders, scrapers. Exclude or segment when evaluating real user behavior; use GA4 filters or segments to isolate human traffic.

Traffic Channels

Channel Typical Sources Attribution
Organic Google, Bing, other search Referrer preserved
Paid (web) Google Ads, Meta Ads, etc. UTM required
Paid (app) App install ads; Google App Campaigns, Apple Search Ads UTM; in-app events
Paid (TV/CTV) Streaming ads; Hulu, Roku, YouTube TV UTM for QR/URL; brand lift
Social Public posts (Facebook, LinkedIn, etc.) Often preserved
Referral External sites, backlinks Referrer preserved
Direct Typed URL, bookmarks No referrer
Email Newsletters, campaigns Often dark without UTM

Dark Traffic

What It Is

Traffic without clear origin--analytics tools default to "Direct" when referrer is missing. Common causes:

  • Private/dark social: WhatsApp, Messenger, Slack, Discord, TikTok shares
  • Email clients: Many strip referrer headers
  • HTTPS->HTTP: Referrer not passed
  • Mobile apps: In-app browsers often omit referrer
  • Ad blockers, privacy tools: Block tracking

Misattribution (Research)

When traffic was sent from known sources, analytics often misattributed:

  • 100% as direct: TikTok, Slack, Discord, WhatsApp, Mastodon
  • 75%: Facebook Messenger
  • 30%: Instagram DMs
  • 14%: LinkedIn public posts
  • 12%: Pinterest

Mitigation

Action Purpose
UTM parameters Tag links in emails, social, campaigns: ?utm_source=X&utm_medium=Y&utm_campaign=Z
Block internal IPs Exclude company visits from reports
Segment direct traffic Split by page type to estimate dark vs. genuine direct

Segmenting Direct Traffic

  1. Expected direct: Homepage, short URLs, brand pages--likely real direct
  2. Unexpected direct: Long URLs, deep pages, product pages--likely dark traffic
  3. Report separately: Use segments in GA4/analytics to avoid overcounting direct

Attribution for Channel Optimization

Ads, growth channels, and medium can be optimized by viewing attribution data. Clean UTM + conversion tracking feeds attribution models; reliable attribution drives budget allocation and channel decisions.

Use Action
Optimize ads Compare paid channels (Google, Meta, LinkedIn) by attributed conversions; reallocate budget to winners
Optimize growth channels Identify which medium (cpc, email, social, referral) drives conversions; scale what works
Multi-touch attribution Requires clean UTM data; inconsistent tagging (e.g., facebook vs Facebook) fragments reports and misattributes

GA4 Default Channel Grouping: Align utm_medium and utm_source with GA4's rules to avoid "Unassigned" traffic. ~30% of campaigns lack proper UTM markup, leading to wasted ad spend; teams standardizing UTM see 29% improvement in attribution accuracy.

Reference: UTM.io – utm_medium, utm_campaign & utm_source Optimization, UTMs for Marketing Attribution

UTM Best Practices

Parameter Use Example
utm_source Origin newsletter, facebook, google
utm_medium Channel type email, cpc, social
utm_campaign Campaign name summer_sale, product_launch
utm_content Variant (optional) banner_a, cta_button
utm_term Paid keyword (optional) running_shoes

GA4 alignment (avoid Unassigned):

Channel utm_medium utm_source
Paid Search cpc google, bing
Paid Social paid-social, cpc facebook, instagram
Email email newsletter, mailchimp
Organic Social social twitter, linkedin
App install cpc, app google, facebook, apple
CTV / Streaming video, ctv hulu, roku, youtube
Display / Banner display, cpc Publisher or network name
Directory ads paid, cpc taaft, shopify, g2, capterra
  • Consistent naming: Lowercase, hyphens; document conventions; never tag internal links (overwrites session attribution)
  • Apply everywhere: Every link in emails, social posts, ads
  • Avoid: Typos, inconsistent values; causes fragmentation

Traffic Diversification

Principle Guideline
Search share Keep organic search below ~75% of total traffic
Health Higher direct + referral share = healthier profile
Brand sites Diversified traffic is common for strong brands
Engagement Content, email, social, free tools drive return visits

See seo-monitoring for full SEO data analysis framework.

Natural Traffic Benchmark

Location: GA4 > Reports > Acquisition > Traffic acquisition

  1. Review organic traffic trend
  2. Record baseline (e.g., monthly total)
  3. Compare periodically to detect growth or decline

Output Format

  • Traffic source breakdown
  • Dark traffic estimate and actions
  • UTM tagging recommendations
  • Segmentation approach for reporting

Related Skills

  • analytics-tracking: Implement UTM, events, conversions; attribution models
  • google-ads, paid-ads-strategy: Paid channels; attribution informs budget allocation
  • ai-traffic-tracking: AI search traffic
  • google-search-console: GSC performance and indexing analysis
  • seo-monitoring: Full SEO data analysis system, benchmark, article database
  • email-marketing: Email strategy; UTM for email links
安全使用建议
This skill appears coherent and low-risk: it only provides instructions for analyzing traffic and improving attribution and does not request keys or install code. Before using, avoid pasting raw, sensitive analytics credentials or private data into the chat; if you want platform-specific help (GA4, Adobe, etc.), provide anonymized examples or metrics. Verify any percentages or external recommendations against your own data or vendor docs, and treat the guidance as advisory—test tagging and segments in a staging property before applying changes to production reporting.
功能分析
Type: OpenClaw Skill Name: kostja94-traffic-analysis Version: 1.1.0 The skill bundle contains purely informational content and instructions for an AI agent to assist with website traffic analysis, UTM tagging, and attribution. There is no executable code, no data exfiltration logic, and no malicious prompt injection; the instructions in SKILL.md are limited to defining the agent's output format and scope for traffic reporting.
能力评估
Purpose & Capability
Name/description align with the SKILL.md content: it provides guidance on traffic sources, dark traffic, UTM best practices and attribution—no unrelated capabilities are requested.
Instruction Scope
The SKILL.md contains only analytic guidance and mitigation steps (UTM tagging, segmentation, GA4 rules). It does not instruct the agent to read local files, fetch credentials, phone-home, or transmit data to unexpected endpoints.
Install Mechanism
No install spec and no code files — the skill is instruction-only, so nothing is written to disk or executed.
Credentials
The skill requests no environment variables, credentials, or config paths; this is proportionate for its stated analytics guidance role.
Persistence & Privilege
always is false and the skill is user-invocable; it does not request elevated or permanent privileges or modify other skills' configs.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install kostja94-traffic-analysis
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /kostja94-traffic-analysis 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.1.0
Automated batch sync
元数据
Slug kostja94-traffic-analysis
版本 1.1.0
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 1
常见问题

traffic-analysis 是什么?

When the user wants to analyze website traffic sources, attribution, or dark traffic. Also use when the user mentions "traffic sources," "dark traffic," "dir... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 199 次。

如何安装 traffic-analysis?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install kostja94-traffic-analysis」即可一键安装,无需额外配置。

traffic-analysis 是免费的吗?

是的,traffic-analysis 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

traffic-analysis 支持哪些平台?

traffic-analysis 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 traffic-analysis?

由 Kostja Zhang(@kostja94)开发并维护,当前版本 v1.1.0。

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