Search, filter, and retrieve Claude/Codex history indexed by the automem CLI. Use when the user wants to index history, run lexical/semantic/hybrid search, fetch full transcripts, or produce LLM-friendly JSON output for RAG.
Search previous Claude Code conversations for facts, patterns, decisions, and context using semantic or text search
Maintain skills wiki health - check links, naming, cross-references, and coverage
Generates comprehensive reports on recent company developments using web search. Use when analyzing stocks, researching companies, evaluating investments, or when users mention company names, stock tickers, market analysis, or financial research.
Functional mythological compression for OCTAVE documents. Semantic shorthand for LLM audiences, not prose decoration
Retrieves scientific papers from PubMed and creates plain-language research summaries. Use when users ask about medical research, scientific studies, clinical trials, disease treatments, or want to understand recent scientific literature on any biomedical topic.
Generate pre-call research briefs with company news, stakeholder backgrounds, and custom discovery question sets.
Text embeddings for semantic search and similarity. Use when converting text to vectors, choosing embedding models, implementing chunking strategies, or building document similarity features.
Query decomposition for multi-concept retrieval. Use when handling complex queries spanning multiple topics, implementing multi-hop retrieval, or improving coverage for compound questions.
Reranking patterns for improving search precision. Use when implementing cross-encoder reranking, LLM-based relevance scoring, or improving retrieval quality in RAG pipelines.
Advanced RAG with Self-RAG, Corrective-RAG, and knowledge graphs. Adaptive retrieval, document grading, query rewriting, web fallback. Use when building self-correcting retrieval systems.
LLM output evaluation and quality assessment. Use when implementing LLM-as-judge patterns, quality gates for AI outputs, or automated evaluation pipelines.
LLM evaluation and testing patterns including prompt testing, hallucination detection, benchmark creation, and quality metrics. Use when testing LLM applications, validating prompt quality, implementing systematic evaluation, or measuring LLM performance.
HyDE (Hypothetical Document Embeddings) for improved semantic retrieval. Use when queries don't match document vocabulary, retrieval quality is poor, or implementing advanced RAG patterns.
LLM guardrails with NeMo, Guardrails AI, and OpenAI. Input/output rails, hallucination prevention, fact-checking, toxicity detection, red-teaming patterns.
Retrieval-Augmented Generation patterns for grounded LLM responses. Use when building RAG pipelines, constructing context from retrieved documents, adding citations, or implementing hybrid search.
Extract key concepts from any content and create spaced-repetition flashcards. Multiple formats: Anki-compatible, printable PDFs, interactive web.
Aggregate and analyze customer reviews from G2, Capterra, Trustpilot, App Store, and other platforms. Performs sentiment analysis, identifies pain points, extracts feature feedback, generates marketing claims, and compares competitor reviews. Use when users need review analysis, competitive intelligence, or customer feedback insights.
Systematic extraction of pain points, feature gaps, switching triggers, and opportunities from review sources (B2B review sites, app stores, forums, communities, issue trackers). Includes bias hygiene, taxonomy building, and turning insights into experiments.
Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases.
Use this agent when delegating research tasks that require collecting and verifying beyond surface-level sources. The agent crawls, collates, verifies, and fact-checks information, providing evidence of its verification process.
Expert search specialist mastering advanced information retrieval, query optimization, and knowledge discovery. Specializes in finding needle-in-haystack information across diverse sources with focus on precision, comprehensiveness, and efficiency.
Search previous Claude Code conversations for facts, patterns, decisions, and context using semantic or text search
Web search and content extraction via Brave Search API. Use for searching documentation, facts, or any web content. Lightweight, no browser required.
