skills/deep-research/SKILL.md
Execute autonomous multi-step research using Google Gemini Deep Research Agent. Use for: market analysis, competitive landscaping, literature reviews, technical research, due diligence. Takes 2-10 minutes but produces detailed, cited reports. Costs $2-5 per task.
npx skillsauth add legendaryabhi/agent-skills-hub deep-researchInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Run autonomous research tasks that plan, search, read, and synthesize information into comprehensive reports.
Use this skill when:
pip install -r requirements.txtexport GEMINI_API_KEY=your-api-key-here
Or create a .env file in the skill directory.python3 scripts/research.py --query "Research the history of Kubernetes"
python3 scripts/research.py --query "Compare Python web frameworks" \
--format "1. Executive Summary\n2. Comparison Table\n3. Recommendations"
python3 scripts/research.py --query "Analyze EV battery market" --stream
python3 scripts/research.py --query "Research topic" --no-wait
python3 scripts/research.py --status <interaction_id>
python3 scripts/research.py --wait <interaction_id>
python3 scripts/research.py --query "Elaborate on point 2" --continue <interaction_id>
python3 scripts/research.py --list
--json): Structured data for programmatic use--raw): Unprocessed API response| Metric | Value | |--------|-------| | Time | 2-10 minutes per task | | Cost | $2-5 per task (varies by complexity) | | Token usage | ~250k-900k input, ~60k-80k output |
--query "..."--stream or poll with --status--continue for follow-up questionsdevelopment
Multiagent AI system for scientific research assistance that automates research workflows from data analysis to publication. This skill should be used when generating research ideas from datasets, developing research methodologies, executing computational experiments, performing literature searches, or generating publication-ready papers in LaTeX format. Supports end-to-end research pipelines with customizable agent orchestration.
development
Implement DeFi protocols with production-ready templates for staking, AMMs, governance, and lending systems. Use when building decentralized finance applications or smart contract protocols.
tools
NGS analysis toolkit. BAM to bigWig conversion, QC (correlation, PCA, fingerprints), heatmaps/profiles (TSS, peaks), for ChIP-seq, RNA-seq, ATAC-seq visualization.
testing
Molecular ML with diverse featurizers and pre-built datasets. Use for property prediction (ADMET, toxicity) with traditional ML or GNNs when you want extensive featurization options and MoleculeNet benchmarks. Best for quick experiments with pre-trained models, diverse molecular representations. For graph-first PyTorch workflows use torchdrug; for benchmark datasets use pytdc.