Auto Research

Conducts autonomous web research with source credibility scoring, produces structured briefings in Obsidian, and stores vectorized data in Qdrant for semanti...

Auto-Research Skill

An autonomous research agent for OpenClaw that searches, synthesizes, and stores findings with full source tracking.

Overview

This skill enables OpenClaw to conduct autonomous research on any topic, compiling structured briefings with executive summaries, key findings, and actionable recommendations. Research outputs are stored in your Obsidian vault and vectorized in Qdrant for semantic retrieval.

Usage

Natural Language Triggers

• "Research [topic]" - Standard depth research
• "Quick research on [topic]" - Fast scan (5 sources)
• "Deep dive into [topic]" - Comprehensive analysis (10+ sources)

Command Line Usage

bash

# Quick scan - 5 sources, 1-page summary
./clawhub-skills/auto-research/research.sh "AI agents 2026" quick

# Standard research - 7 sources, full briefing
./clawhub-skills/auto-research/research.sh "construction CRM software" standard

# Deep dive - 10+ sources, comprehensive analysis
./clawhub-skills/auto-research/research.sh "quantum computing breakthroughs" deep

Configuration

Environment Variables

Variable	Description	Default
BRAVE_API_KEY	Brave Search API token	Uses built-in key
OBSIDIAN_VAULT	Path to Obsidian vault	~/Documents/Obsidian/YoderVault
QDRANT_URL	Qdrant server URL	http://10.0.0.120:6333
REDIS_URL	Redis cache server	10.0.0.120:6379
RESEARCH_DEPTH	Default depth level	standard

Depth Levels

Level	Sources	Detail Level	Use Case
quick	5	Brief summary	Fast fact-checking, initial exploration
standard	7	Full briefing	General research, decision support
deep	10+	Comprehensive	Strategic analysis, deep domain dives

Outputs

1. Obsidian Vault Briefing

Location: Inbox/Research - {Topic} - {YYYY-MM-DD}.md

Structure:

• Executive Summary - 3-5 sentence overview
• Key Findings - Bullet-point highlights
• Detailed Analysis - Organized by theme/subtopic
• Sources - Full citations with URLs and access dates
• Action Items - Recommendations and next steps
• Confidence Rating - High/Medium/Low based on source quality

2. Vectorized Knowledge (Qdrant)

Collection: web_research

Each research document is chunked into semantic segments and stored with:

• Source URL and title
• Research topic tags
• Publication/access date
• Confidence metadata

Features

Source Quality Assessment

Research automatically evaluates source credibility:

High Confidence:

• Academic institutions (.edu)
• Government sources (.gov)
• Major news organizations
• Industry-leading publications

Medium Confidence:

• Established tech blogs
• Company press releases
• Trade publications

Low Confidence:

• Anonymous forums
• Unverified blogs
• Outdated sources (>3 years)

Smart Caching

Search results are cached for 24 hours to avoid redundant API calls:

• Primary: Redis (if available)
• Fallback: Local file cache in /tmp/research-cache/

Cache key format: research:{topic_hash}:{depth}

Vector Search Integration

Once stored in Qdrant, research becomes discoverable via:

bash

# Search your research corpus
./tools/yoder-kb.sh search "your query" 5 --collection web_research

File Structure

text

clawhub-skills/auto-research/
├── SKILL.md                    # This documentation
├── research.sh                 # Main research orchestrator
├── vectorize.sh               # Qdrant vectorization
├── briefing-template.md       # Output format template
└── search-cache.sh           # Result caching utility

Dependencies

Required (Pre-installed)

• curl - API requests
• jq - JSON processing
• python3 - Vectorization script

Optional

• redis-cli - For distributed caching
• obsidian-cli - Vault integration

Example Output

markdown

# Research: Construction CRM Software 2026

**Date:** 2026-02-07  
**Depth:** Standard  
**Sources:** 7  
**Confidence:** High

## Executive Summary

The construction CRM market in 2026 is dominated by integrated platforms 
combining project management, client relationship tracking, and field 
mobility. Key players include Procore, Buildertrend, and emerging AI-
powered solutions like Attentive.ai. Mobile-first design and AI 
automation are the primary differentiators.

## Key Findings

- **Market Leaders:** Procore maintains ~35% market share with enterprise focus
- **AI Integration:** 60% of new platforms incorporate predictive analytics
- **Mobile Priority:** Field-accessible CRMs show 3x higher adoption rates
- **Pricing Trends:** Average $99-299/seat/month for full-featured platforms

[... detailed analysis continues ...]

Integration with OpenClaw

As a Skill

When placed in clawhub-skills/auto-research/, OpenClaw automatically:

1. Parses natural language research requests
2. Calls research.sh with appropriate parameters
3. Displays progress and final output path
4. Confirms vectorization success

Standalone Usage

Scripts can run independently for automation:

bash

# Daily research digest via cron
0 9 * * 1 /path/to/research.sh "weekly tech trends" quick

Troubleshooting

Issue	Solution
No results returned	Check Brave API key; verify internet connection
Cache not working	Verify Redis is running at configured URL
Vault save fails	Check Obsidian vault path permissions
Vectorization fails	Verify Qdrant is accessible and collection exists

Version

v1.0.0 - Initial release with Brave Search, Qdrant integration, and Obsidian output.

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Part of the ClawHub skill ecosystem for OpenClaw.