A sophisticated strategic analysis engine that synthesizes cross-platform advertising data into actionable, hypothesis-driven growth plans.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install deep-marketing-analyst
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 deep-marketing-analyst 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 Deep Ads Analyst?
The Deep Ads Analyst is a specialized tool within the Openclaw Skills ecosystem designed for performance marketers and growth strategists who require deep-dive analysis over generic advice. It excels at hypothesis testing, strategic synthesis, and evidence mapping across major advertising platforms including Meta, Google Ads, TikTok, YouTube, and Amazon Ads. This skill moves beyond basic reporting by evaluating the strength of cross-channel evidence to provide a clear, risk-weighted path forward for scaling or optimizing spend.
By implementing this skill, teams can ensure their advertising workflows are anchored in measurable outcomes. The analyst is built to handle complex multi-channel environments, maintaining platform-specific awareness—such as prioritizing creative cadence for social channels while focusing on demand capture for search platforms. It serves as a bridge between raw data and executive-level decision-making, ensuring that every strategic shift is backed by a rigorous evaluation of available evidence.
Deep Ads Analyst Use Cases
- Conducting deep competitor studies to identify creative and offer shifts across Meta and TikTok Ads over specific timeframes.
- Stress-testing marketing hypotheses, such as comparing the performance of broad targeting versus niche audience segments using existing evidence.
- Developing quarterly board-level strategic briefs that require risk-weighted recommendations for channel budget allocation.
- Synthesizing cross-platform data to determine the most efficient path for improving ROAS or reducing CPA.
How Deep Ads Analyst Works
- Decompose the user's primary research question into a series of testable, data-driven hypotheses.
- Define a source-specific evidence collection plan that targets platforms like Google Ads, Amazon Ads, or DSPs.
- Evaluate the strength and recency of collected evidence, identifying and ranking any conflicting data points.
- Synthesize the findings into a cohesive channel-aware strategy, ensuring platform-specific nuances are respected.
- Output a structured decision set including a research plan, evidence table, and actionable next experiments with stop-loss conditions.
Deep Ads Analyst Setup
To integrate this capability into your workflow, ensure your environment is configured for Openclaw Skills. You can initialize the analyst by defining the input requirements in your agent configuration:
# Example: Initialize the Deep Ads Analyst skill
openclaw install deep-marketing-analyst
Ensure you provide the mandatory fields—research_question, hypothesis_set, and decision_deadline—to trigger the full analytical workflow.
Deep Ads Analyst Data Schema & Taxonomy
The Deep Ads Analyst organizes its findings using a structured schema to maintain consistency across the Openclaw Skills platform.
| Component | Format | Description |
|---|---|---|
| Research Plan | YAML/Markdown | Detailed breakdown of hypotheses and data sources |
| Evidence Table | Table | A ranked list of findings with associated confidence levels |
| Strategic Conclusion | Markdown | The synthesized recommendation based on evidence strength |
| Action Plan | List | Specific follow-up experiments with measurable triggers |
Deep Ads Analyst Advanced Features
- Cross-platform intelligence that avoids generic plans by maintaining specific strategies for social, search, and programmatic channels.
- Confidence-weighted evaluation that explicitly flags weak evidence to prevent making hard claims on low-signal data.
- Risk-mitigation guardrails that include mandatory rollback conditions and stop-loss triggers for any proposed spend increases.
- Hypothesis decomposition that breaks down high-level business goals into granular, testable units of work.
SKILL.md
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