An AI research pipeline that automates search retrieval through Perplexity and saves results to a SQLite database with full observability.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install research-logger
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 research-logger 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 Research Logger?
Research Logger is a specialized tool designed to transform how developers and researchers interact with AI-driven search engines. By creating a bridge between the Perplexity API and a local SQLite database, it ensures that valuable insights are never lost in transient chat sessions. This skill is a cornerstone of the Openclaw Skills ecosystem, providing a structured way to build a searchable knowledge base while maintaining a detailed audit trail of research activities.
Beyond simple logging, it incorporates enterprise-grade observability through Langfuse tracing. This allows users to monitor the performance and costs of their research pipelines. Whether you are conducting competitive analysis or deep technical dives, Research Logger provides the persistence and organization necessary for professional workflows.
Research Logger Use Cases
- Saving and recalling complex research sessions for long-term projects.
- Building a localized, searchable knowledge base from repeated AI queries.
- Auditing research decisions and tracking the evolution of information gathering.
- Creating a centralized repository for competitive analysis and market research data.
How Research Logger Works
- The user triggers a research query via the CLI using specific commands for quick or deep-dive analysis.
- The skill interfaces with the Perplexity API to retrieve high-fidelity research results.
- Data is automatically parsed and enriched with project-specific metadata and topics.
- The entry is committed to a local SQLite database for permanent storage.
- The entire lifecycle is recorded via Langfuse to provide a full trace of the research session within the Openclaw Skills framework.
Research Logger Setup
To get started with this skill, ensure you have Python 3.10+ installed and a valid Perplexity API key.
# Set your environment variable
export PERPLEXITY_API_KEY='your_perplexity_key_here'
# Run a quick research log
python3 scripts/research_logger.py log quick "what is RAG"
# Run a professional-grade research log with topic tagging
python3 scripts/research_logger.py log pro "vector database comparison" --topic "databases"
Research Logger Data Schema & Taxonomy
Research Logger organizes data using a structured SQLite schema to ensure high performance and easy retrieval.
| Attribute | Description |
|---|---|
| Query | The original prompt or search string |
| Result | The comprehensive response generated by Perplexity |
| Metadata | Includes topic, project tags, and mode (quick/pro) |
| Tracing | Langfuse trace IDs for observability |
| Timestamp | ISO 8601 formatted date and time of the session |
Research Logger Advanced Features
- Support for Pro research modes for more detailed and nuanced data collection.
- Integrated search command to query your local SQLite database without re-running API calls.
- Full Langfuse integration for tracing LLM calls and managing latency/costs.
- Flexible topic and project tagging to keep diverse Openclaw Skills research sessions organized.
SKILL.md
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