recursive-knowledgelisted

# Recursive Knowledge Processing Process arbitrarily large document sets through knowledge graph construction and stateful multi-hop queries. Based on RLM research but with proper state management and termination logic. ## Core Concept Instead of stuffing documents into context (which causes degradation), this skill: 1. Indexes documents into a knowledge graph (entities, relationships) 2. Answers queries by traversing the graph 3. Tracks state to avoid redundant exploration 4. Uses confidence thresholds to know when to stop ## Workflow ### Phase 1: Indexing For a new corpus, run the indexer: ```python python3 scripts/index_corpus.py --input /path/to/documents --output /path/to/graph.json ``` This extracts: - **Entities**: People, organizations, concepts, dates, locations - **Relationships**: References, mentions, contradicts, supports, relates_to - **Metadata**: Source document, position, extraction confidence For details on entity/relationship schema, see [references/graph-schema.md](references/graph-schema.md). ### Phase 2: Querying For user queries against an indexed corpus: ```python python3 scripts/query.py --graph /path/to/graph.json --query "user question here" ``` The query engine: 1. Parses query into target entities/relationships 2. Finds entry points in graph 3. Traverses with state tracking 4. Stops when confidence threshold met 5. Returns answer with provenance ### Phase 3: Incremental Updates Add new documents to existing graph: ```python python3