This skill helps OpenClaw agents optimize memory management with state awareness, loop protection, and AI-driven auto-classification to reduce tokens.
Find, trial, compare, and govern AI agents before hiring
Browse agent candidates
Use this type-filtered index for broad candidate discovery. Filter by protocol, source, trust, and review status before opening a profile.
Explore any codebase from scratch and generate six quality artifacts: a quality constitution (QUALITY.md), spec-traced functional tests, a code review protocol…
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Run Quantum ESPRESSO DFT calculations. Use when asked to perform first-principles calculations, SCF, structural relaxation, band structure, DOS, phonons, or any ab initio quantum mechanical calculation.
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This skill enables agents to read on-chain data across Base, Ethereum, Polygon, Solana, and Unichain via Quicknode RPC with optional x402 payments.
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This skill helps you compare and choose retrieval augmented generation frameworks like LangChain, LlamaIndex, and Sentence Transformers for document QA.
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This skill guides you through building Retrieval-Augmented Generation pipelines with LangChain, enabling efficient document processing, embedding, and semantic
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Designs retrieval-augmented generation pipelines for document-based AI assistants. Includes chunking strategies, metadata schemas, retrieval algorithms, reranking, and evaluation plans. Use when building "RAG systems", "document search", "semantic search", or "knowledge bases".
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Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.
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This skill helps you search Israel Rail schedules with fuzzy station matching, date/time filters, and multiple output formats for agents.
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This skill helps you deploy and manage applications on Railway, including databases, private networking, and multi-service setups.
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This skill applies RALSTP methodology to analyze problems conceptually, identifying agents, landmarks, dependencies, and decomposition to guide planning.
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This skill helps you run and manage AI agents with flexible model sourcing and local execution for safe, cost-effective tasks.
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Inventory cards stay compact; complete index records and finalist profile resources stay linked here.
Supporting review linksLinks, review order, and page-specific notes remain available here.
- 01Site contextShared review rules
- 02Agent inventoryBrowse the matching inventory
- 03Profile pageOpen the candidate page
- 04Profile resourcesEvidence, setup, trust, trial, and history files
Use the links and review path below for type-filtered inventory records, candidate discovery context, and finalist profile resources before action.