skills/algolia-search/SKILL.md
Expert patterns for Algolia search implementation, indexing strategies, React InstantSearch, and relevance tuning Use when: adding search to, algolia, instantsearch, search api, search functionality.
npx skillsauth add legendaryabhi/agent-skills-hub algolia-searchInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Modern React InstantSearch setup using hooks for type-ahead search.
Uses react-instantsearch-hooks-web package with algoliasearch client. Widgets are components that can be customized with classnames.
Key hooks:
SSR integration for Next.js with react-instantsearch-nextjs package.
Use <InstantSearchNext> instead of <InstantSearch> for SSR. Supports both Pages Router and App Router (experimental).
Key considerations:
Indexing strategies for keeping Algolia in sync with your data.
Three main approaches:
Best practices:
| Issue | Severity | Solution | |-------|----------|----------| | Issue | critical | See docs | | Issue | high | See docs | | Issue | medium | See docs | | Issue | medium | See docs | | Issue | medium | See docs | | Issue | medium | See docs | | Issue | medium | See docs | | Issue | medium | See docs |
development
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.