726 skills · sorted by relevance
Build advanced AgentDB workflows with database synchronization, multiple databases, and search that combines meaning with attribute filters. Covers similarity measures, diverse search results, and operational patterns for distributed AI applications.
Build document search with AgentDB that finds related content by meaning. Store documents as numerical representations, combine similarity search with metadata filters, and retrieve relevant passages for systems that generate answers using source material.
Design knowledge systems that preserve document structure and connections across sources. Choose retrieval methods, organize conversation memory, and plan evidence-based updates, review workflows, and access controls for information that scattered text searches cannot adequately cover.
Retrieve facts from named scientific, regulatory, economic, and other databases through documented APIs. Get results with recorded filters, retrieval dates, and record counts so another person can repeat the lookup and assess its completeness.
Build a lasting Markdown knowledge base for project decisions, architecture, and session discoveries. Search pages by keywords and tags, use results to answer with citations, and check for outdated content, broken links, and structural problems.
Coordinate a worker agent in a tmux-based OMX Team through startup acknowledgements, task claims, status transitions, and mailbox checks. Keep task ownership, completion evidence, and idle status visible to the team.
Explore an unfamiliar codebase using a graph of code relationships. Inspect its structure, find functions and classes, trace calls and imports, follow execution paths, and locate large functions for closer investigation.
Manage Lark knowledge spaces, their members, and the hierarchy of document entries. Create spaces and entries, browse their structure, and move or copy entries to organize where team documents and shortcuts are located.
Assess the storage and retrieval layers of RAG systems, which search documents to support AI answers. Test persistent document poisoning, cross-tenant access, source-text leaks, and manipulated search rankings using independently verifiable evidence.
Organize paper notes in an Obsidian project vault and synthesize them into lasting literature summaries. It standardizes review sections, tracks collection coverage, connects papers to project knowledge, and refreshes a visual literature map through filesystem operations.
Build advanced AgentDB workflows with database synchronization, multiple databases, and search that combines meaning with attribute filters. Covers similarity measures, diverse search results, and operational patterns for distributed AI applications.
Build document search with AgentDB that finds related content by meaning. Store documents as numerical representations, combine similarity search with metadata filters, and retrieve relevant passages for systems that generate answers using source material.
Routes any task involving AWS databases — choosing, comparing, recommending, getting started with, or operating a database — to the correct service-specific skill. Supersedes general training-data knowledge with post-training service updates, corrected limitations, and decision procedures for relational (Aurora, DSQL, RDS), key-value (DynamoDB), wide-column (Keyspaces), document (DocumentDB), graph (Neptune), time-series (Timestream), and in-memory/caching (ElastiCache, MemoryDB) workloads. Activates when a user describes building an application on AWS that will store, retrieve, or manage data, even if they do not mention 'database' explicitly.
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Builds generative AI applications on Amazon Bedrock. Covers model invocation (Converse API, InvokeModel), RAG with Knowledge Bases, Bedrock Agents, Guardrails, and AgentCore (including the Harness managed agent loop). Applies when invoking models, setting up Knowledge Bases, creating agents, applying guardrails, deploying to AgentCore, migrating/porting/converting a Bedrock Agent (including inline agents) to an AgentCore Harness, troubleshooting Bedrock errors (ThrottlingException, AccessDeniedException), or choosing models (Claude, Llama, Nova, Titan). Also for prompt caching, quota and throttling diagnosis, cost tracking, migrating between Claude model generations (4.5 to 4.6 to 4.7), chunking strategies, API selection (Converse vs InvokeModel), guardrail capabilities, and model selection. Also covers AgentCore Payments (x402, microtransactions, Payment Manager, Connector, Instrument, Coinbase CDP, Stripe Privy, paid endpoints, agent payments). NOT for custom model training, Rekognition, or Comprehend.
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Start, structure, and grow a persistent research wiki indexed in pro-workflow's SQLite knowledge base. Each wiki is a folder of markdown pages with provenance, plus a shadow FTS5 index so any session can recall it. Use when the user says "start a wiki", "add to wiki", "compile a page", "wiki on X", or wants a long-lived knowledge base on a topic, paper, product, person, project, or codebase.
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Design any process, idea, problem, or body of knowledge into an ICM (Interpretable Context Methodology) workspace — folder structure as agent architecture — or restructure an existing folder, repo, or vault into one. Use when the user wants to (1) turn a recurring workflow into an agent-runnable folder pipeline, (2) organize scattered notes, files, or knowledge into a library one AI agent can walk, (3) map a team or company as connected context ("context map", "second brain", "team brain", "knowledge base for AI"), (4) audit a codebase or mixed folder into a walkable edit map (objects, processes, change-impact) so later agents can change it without slurping the tree, (5) audit or restructure an existing workspace to ICM conventions, or (6) says "make this an ICM", "ICM this", "map this repo", "audit this folder", "what would a change hit", "build me a workspace", or "structure this for agents".
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Extract facts from meetings and update your knowledge base — person profiles, chronological log, and index. Use when the user asks "ingest my meetings", "update my knowledge base", "extract facts from meetings", "sync meetings to wiki", "backfill knowledge", or wants their PARA/Obsidian/wiki profiles updated from conversation data.
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Audit and repair an internal knowledge base — staleness, ownership gaps, orphans, duplication, and findability. Use when the wiki is untrusted, docs are out of date, nobody owns pages, or search returns the wrong answer.
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Codebase'i knowledge graph olarak analiz et. Dependency, call graph, hotspot analizi.
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Monarch Initiative knowledge graph REST API for disease-gene-phenotype associations and cross-species orthology. MONDO disease-to-gene/phenotype, HP phenotype profiles, cross-species comparisons. Use for rare disease gene prioritization and phenotype-based candidate ranking. For GWAS use gwas-database; for clinical pathogenicity use clinvar-database.
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Retrieve mouse phenotype data from the Jackson Laboratory Mouse Phenome Database (MPD) via its REST API. Browse 520+ projects, look up per-project measure metadata, pull strain-level means (raw or LS-mean adjusted) and per-animal values, find measures by MP/VT ontology terms, and resolve strain nomenclature or gene coordinates. Use for QTL support, cross-strain comparison, mouse model selection, and ontology-driven phenotype discovery. Use monarch-database for disease-gene-phenotype knowledge graphs; ensembl-database for mouse genome annotations.
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Write clear, searchable help center articles and FAQ entries based on support data, product documentation, and common customer questions. Use when the user requests knowledge base article writing or provides relevant inputs for this workflow.
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Add persistent memory to AI coding agents — file-based, vector, and semantic search memory systems that survive between sessions. Use when a user asks to "remember this", "add memory to my agent", "persist context between sessions", "build a knowledge base for my agent", "set up agent memory", or "make my AI remember things". Covers file-based memory (MEMORY.md), SQLite with embeddings, vector databases (ChromaDB, Pinecone), semantic search, memory consolidation, and automatic context injection.
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Knowledge graph specialist for entity and causal relationship modelingUse when "knowledge graph, graph database, falkordb, neo4j, cypher query, entity resolution, causal relationships, graph traversal, graph-database, knowledge-graph, falkordb, neo4j, cypher, entity-resolution, causal-graph, ml-memory" mentioned.
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