A specialized diagnostic tool for decomposing ad traffic composition and identifying performance anomalies across major advertising platforms.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install traffic-structure-analyzer
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install traffic-structure-analyzer using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Traffic Structure Analyzer?
The Traffic Structure Analyzer is a sophisticated tool designed for the Openclaw Skills ecosystem, specifically engineered to help growth teams and media buyers move from raw data to actionable execution plans. Unlike generic reporting tools, this skill specializes in traffic mix decomposition and trend anomaly diagnosis, providing high-signal insights that directly impact revenue, ROAS, and budget efficiency.
By leveraging this capability within Openclaw Skills, developers and marketers can automate the complex process of cross-channel analysis. It accounts for the distinct behaviors of various platforms—prioritizing creative testing for Meta and TikTok while focusing on intent-based demand capture for Google and Amazon Ads—ensuring that every recommendation is contextually relevant and technically sound.
Traffic Structure Analyzer Use Cases
- Scaling budget based on high-performing traffic segments and conversion quality.
- Troubleshooting revenue plateaus when traffic volume remains high but quality declines.
- Comparing attribution windows to resolve disagreements between 1-day and 7-day click data.
- Automating daily 9AM performance summaries for key campaigns including CPA and ROAS trends.
- Diagnosing performance variances between different audience funnels and platform sources.
How Traffic Structure Analyzer Works
- Definition Alignment: The skill begins by disambiguating metric definitions and time windows to ensure a canonical baseline.
- Query Slicing: It partitions data into logical slices based on platform, funnel stage, and audience segment.
- Delta Computation: The system calculates trend deltas and identifies the specific drivers behind variance in the data.
- Summarization: Findings are synthesized into a result summary that includes explicit confidence levels.
- Recommendation: The workflow concludes with concrete, platform-aware next steps and necessary stop-loss conditions.
Traffic Structure Analyzer Setup
To deploy this analyzer within your Openclaw Skills environment, follow these configuration steps:
# Install the skill via the Openclaw CLI
openclaw install traffic-structure-analyzer
# Configure your data source scopes
openclaw configure traffic-structure-analyzer --platforms="meta,google,tiktok,amazon"
Traffic Structure Analyzer Data Schema & Taxonomy
The Traffic Structure Analyzer utilizes a strict input/output contract to ensure data integrity:
| Input Field | Type | Description |
|---|---|---|
| question_or_report_goal | String | The core business objective for the analysis |
| metric_scope | Object | KPI definitions, dimensions, and date ranges |
| data_source_scope | Array | Specific platforms (Meta, Google, etc.) to query |
| attribution_window | String | Optional window for conversion credit analysis |
Output Schema Example:
{
"platform": "Meta",
"spend": 12000,
"revenue": 42000,
"roas": 3.5,
"confidence": "high"
}
Traffic Structure Analyzer Advanced Features
- Multi-Platform Governance: Integrated logic for DSP, programmatic, and social platforms within the Openclaw Skills framework.
- Automated Risk Mitigation: Every spend recommendation includes mandatory rollback or stop-loss conditions.
- Platform-Aware Logic: Automatically adjusts strategy based on the channel (e.g., focusing on creative testing for TikTok versus query intent for Google).
- Confidence Scoring: Explicitly marks results as directional or conclusive based on sample size and attribution variance.
- Escalation Payloads: Generates structured handoff data for billing or tracking issues.
SKILL.md
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