A high-performance AI research agent specialized in conducting iterative financial searches and generating structured, fully-cited research reports.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install finance-search-agent
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 finance-search-agent 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 Finance Search Agent?
The Finance Search Agent is a professional-grade research tool designed to overcome the limitations of static AI knowledge. By utilizing Openclaw Skills, this agent treats user input as the absolute ground truth and prioritizes real-time data from external tools over its own internal training. It is engineered for precision, ensuring that financial analysts and developers can obtain the most current information regarding market trends, product versions, and technical specifications.
This skill excels at navigating complex information landscapes through a disciplined three-phase workflow: setup, iterative deep-dive research, and report synthesis. It effectively manages resources by enforcing strict word count limits and utilizing a deadlock handling mechanism to prevent inefficient looping, ensuring that every search operation delivers maximum value and progress.
Finance Search Agent Use Cases
- Conducting deep-dive research into new fintech products or emerging market technologies.
- Verifying financial specs, release dates, and version histories from authoritative sources.
- Generating comprehensive research reports with inline citations for investment due diligence.
- Automating the collection and summarization of data from official websites, arXiv, and top-tier tech media.
- Creating detailed research logs to track the evolution of a specific financial topic or trend.
How Finance Search Agent Works
- Preliminary Planning: The agent formulates a 3-5 point search strategy based on the core angles of the user's request.
- Log Creation: It initializes a Research Log wiki document to maintain a transparent record of all findings and search steps.
- Broad Search Execution: Parallel tool calls are made to gather initial titles, summaries, and URLs from the web.
- Deep Source Analysis: The agent selects the most authoritative URLs (prioritizing official sites and academic papers) for full-text scraping.
- Iterative Refinement: Based on the log content, the agent identifies the next most critical question and performs focused research loops.
- Report Synthesis: The agent compiles all findings into a structured final report featuring an abstract, core findings, and a full reference list.
Finance Search Agent Setup
To deploy this skill within your environment, ensure you are utilizing the Openclaw Skills framework. The agent requires access to a set of core search and document management tools.
# Example of initializing the research environment
openclaw install finance-search-agent
Ensure your configuration allows the agent to call url_scraping and create_wiki_document_simple tools. No additional API keys are required beyond your standard search provider integration.
Finance Search Agent Data Schema & Taxonomy
The agent organizes data into two distinct Markdown-formatted wiki documents to ensure clarity and traceability:
| File Type | Description | Word Limit |
|---|---|---|
| Research Log | A chronological record of queries, summaries of scraped URLs, and iterative thoughts. | 5,000 words |
| Final Report | A structured synthesis including Abstract, Background, Core Findings, and Conclusion. | 5,000 words |
All data is stored in the wiki/ directory. Citations are handled using standard Markdown inline links: [[Source]](URL).
Finance Search Agent Advanced Features
- Absolute Tool Priority: Automatically prioritizes real-time search results over internal model knowledge to prevent hallucination.
- Deadlock Handling Mechanism: Terminates sub-goals after two failed attempts to maintain task momentum and cost-efficiency.
- Proactive Content Compression: Summarizes long source documents on-the-fly to stay within strict word count constraints.
- Dynamic Language Adaptation: Automatically outputs the final report in the same language as the user's input prompt.
- Structured Citation Engine: Enforces mandatory inline citations and a comprehensive reference list at the end of every report.
SKILL.md
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