A search-and-save pipeline that logs deep research results to SQLite and Langfuse for long-term knowledge retention.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install research-logger-pro
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-pro 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 sophisticated automation tool designed to transform ephemeral search queries into a persistent knowledge base. By integrating search capabilities with a local SQLite database and Langfuse tracing, this skill ensures that every piece of information retrieved by your agent is stored with context. It is an essential component for developers and researchers using Openclaw Skills who need to maintain an audit trail of their findings.
The skill eliminates the need for manual copy-pasting by automatically capturing topic tags, timestamps, and full research results. This allows for seamless retrieval of past insights, reducing API costs and preventing redundant research efforts while providing deep observability into the research process.
Research Logger Use Cases
- Archiving deep search results for later recall and reference
- Building a structured knowledge base from repeated research queries
- Creating a verifiable audit trail for research-driven project decisions
- Browsing historical research data by topic or keyword without re-running searches
How Research Logger Works
- The user triggers a research request using either quick or pro modes.
- The skill executes the search through the Perplexity API to fetch up-to-date information.
- Results are automatically piped to a local SQLite database for persistent storage.
- Metadata, including topic tags and timestamps, is indexed for fast retrieval.
- The entire execution is traced via Langfuse to provide transparency and debugging capabilities.
- Users can query the local logs anytime to find past research or view recent entries.
Research Logger Setup
To integrate this functionality into your environment, ensure you have Python 3 installed and a valid Perplexity API key. These Openclaw Skills require minimal configuration to start logging.
# Set the required environment variable
export PERPLEXITY_API_KEY="your_key_here"
# Example: Search and log a database comparison
python3 scripts/research_logger.py log pro "compare vector databases" --topic "databases"
# Example: Search your local history
python3 scripts/research_logger.py search "vector databases"
Research Logger Data Schema & Taxonomy
The skill manages research data using a structured SQLite schema and external tracing identifiers.
| Table/Field | Type | Description |
|---|---|---|
| Topic | String | User-defined tag for categorization |
| Query | Text | The search prompt provided to the agent |
| Results | Text | The full content returned by the search engine |
| Timestamp | ISO8601 | The exact time the research was performed |
| Langfuse Trace | UUID | External ID for observability and performance logs |
Research Logger Advanced Features
- Multi-mode search support (Quick vs. Pro) for balancing speed and depth
- Topic-based tagging for organized knowledge management
- Local search filters to retrieve historical data without API calls
- Native Langfuse integration for enterprise-grade LLM tracing and observability
- CLI flags for limiting recent entry views and filtering search results
SKILL.md
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