competitor-docs

What This Skill Does

The competitor-docs skill provides a high-performance, local-first search engine for competitive intelligence. It leverages SQLite FTS5 with BM25 ranking to allow the OpenClaw agent to query large documentation repositories instantaneously. By indexing raw documentation folders and extracting metadata—specifically source URLs—this skill enables the agent to cross-reference feature sets, technical specifications, and configuration guides from multiple vendors without the latency or privacy concerns of external cloud-based vector search services. It is an essential tool for deep-dive research into competitor capabilities.

Installation

To integrate this skill into your OpenClaw environment, execute the following command in your terminal: clawhub install openclaw/skills/skills/carev01/competitor-docs After installation, ensure your documentation repository is prepared with the required file format (articles separated by --- with *Source: headers) and run scripts/docs.py index ./docs to build the search database.

Use Cases

• Competitive Benchmarking: Quickly compare how two different platforms handle specific infrastructure tasks (e.g., "kubernetes backup vs standard snapshots").
• Technical Due Diligence: Extract specific configuration limitations or support requirements from vendor manuals to inform architecture decisions.
• Evidence-Based Reporting: Generate research reports that include verified, clickable source URLs for every claim, ensuring data accuracy.
• Knowledge Gap Analysis: Search across vast archives to see if a specific feature is documented for a competitor, helping identify potential market weaknesses or feature parity gaps.

Example Prompts

1. "Search the indexed documentation for all mentions of data retention policies and compare the maximum storage duration for our top three competitors."
2. "Find the installation requirements for the competitor's API gateway and generate a summary table with source links."
3. "Does the documentation mention specific limitations for multi-region clustering? Please provide the exact source URL for the relevant article."

Tips & Limitations

• Indexing: The skill requires an initial local indexing step. If the underlying documentation files are updated, you must re-run the index command to ensure the search results remain current.
• Formatting: The skill relies on specific delimiters (---) and source tags (*Source:). Documents that do not follow this strict structure may not be indexed correctly or will miss citation metadata.
• Hardware: Since it uses SQLite FTS5, it is extremely resource-efficient. It works well even on local developer machines or edge devices, making it suitable for air-gapped or restricted-network environments.