A sophisticated research framework that combines longitudinal history with cross-sectional competitive analysis to generate exhaustive PDF reports.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install hv-analysis
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 hv-analysis 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 HV-Analysis Deep Research Agent?
The HV-Analysis (Horizontal-Vertical Analysis) skill is a comprehensive research methodology proposed by Digital Life Khazix. It integrates diachronic analysis from linguistics, longitudinal-cross-sectional design from social sciences, and competitive strategy from business schools. This tool allows an agent to systematically investigate a product, company, concept, or person by tracing its full lifecycle while simultaneously comparing it against current market competitors.
By leveraging the power of Openclaw Skills, this agent transcends simple summaries to produce narrative-driven, professional-grade research. It avoids common AI tropes, focusing instead on decision logic, historical origins, and strategic positioning to deliver high-value insights that help users understand the past, present, and potential future of any given subject.
HV-Analysis Deep Research Agent Use Cases
- Investigating a product timeline to understand its version iterations and technical pivots.
- Performing competitive landscape analysis for a company to identify market share and unique value propositions.
- Deconstructing complex technical or philosophical concepts by tracing their academic and practical origins.
- Conducting biographical deep-dives into industry leaders to map their career trajectories and key decision-making moments.
How HV-Analysis Deep Research Agent Works
- Information Gathering: The agent deploys multiple sub-agents to perform parallel web searches, focusing on longitudinal history and horizontal competitive data.
- Longitudinal Analysis: It reconstructs the subject's entire development timeline, from birth to the present, focusing on origin stories and evolutionary milestones.
- Horizontal Analysis: The agent creates a cross-sectional slice of the current market, identifying 3-5 key competitors and comparing core differences in technology, business models, and user experience.
- Synthesis & Insight: It intersects the historical trajectory with the competitive landscape to produce strategic judgments and future scenarios.
- PDF Generation: The final narrative (ranging from 10,000 to 30,000 words) is processed through a specialized script to output a formatted PDF report.
HV-Analysis Deep Research Agent Setup
To integrate this capability into your environment, ensure you have the necessary dependencies for PDF rendering. This skill utilizes Openclaw Skills for orchestration and WeasyPrint for document generation.
# Clone the skill repository
git clone https://github.com/KKKKhazix/khazix-skills
# Install required dependencies for PDF generation
pip install weasyprint
# Configure your agent to point to the hv-analysis skill path
HV-Analysis Deep Research Agent Data Schema & Taxonomy
The skill organizes research into a structured Markdown format before converting it to a final document. The internal data taxonomy follows this structure:
| Section | Content Type | Target Word Count |
|---|---|---|
| Longitudinal | Origins, Milestones, Decision Logic | 6,000 - 15,000 |
| Horizontal | Competitors, Market Position, UX | 3,000 - 10,000 |
| Synthesis | Historical Roots of Current Position | 1,500 - 3,000 |
| Final Output | Professional PDF (A4, Cover Page, CSS Styled) | 10,000 - 30,000 |
The output utilizes a dedicated CSS stylesheet for typography, table zebra-striping, and multilingual support (Chinese/English).
HV-Analysis Deep Research Agent Advanced Features
- Parallel Sub-Agent Execution: Simultaneously handles historical research and competitive scanning to reduce latency.
- Custom PDF Styling: Built-in
md_to_pdf.pyscript with comprehensive CSS for professional report formatting including headers, footers, and cover pages. - Dynamic Adaptation: Automatically adjusts analysis dimensions based on whether the subject is a product, company, concept, or person.
- Narrative-Driven Logic: Designed to produce cohesive storytelling rather than fragmented bullet points, enhancing the readability of long-form reports within Openclaw Skills.
SKILL.md
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