creel

Use a real LLM with creel — wire an injected LLM client, run AI-powered graph extraction, and judge/resolve with a model. Use when the user wants schema-as-extractor extraction, to inject an LLM client (services={'llm': aix_client()} or anthropic_client()), the ('llm', {}) binding, an LLM judge for llm_rubric verification, LLM entity resolution, self-consistency policy, or to test LLM code with a fake client. Triggers on "use a real LLM with creel", "extract with an LLM / GPT / Claude", "schema-as-extractor", "inject an LLM client", "aix_client / anthropic_client", "services={'llm':...}", "the ('llm',{}) binding", "LLM as a judge", "LLM entity resolution", "self-consistency voting", "ExtractionPolicy", "AI-powered graph extraction", "fake LLM client for tests". The LLM is a swappable strategy — no provider SDK lives in creel's core.

Write creel bindings: map each grammar element to an extractor strategy. Use when you need to choose an extractor strategy for a node-type or edge-type, decide pattern vs query vs LLM extraction, write regex_node/regex_edge over prose, or table_map/sql/json_query over structured sources. Covers the binding mental model ({element_id: (strategy, params)} or a bare callable), id templates and casts, endpoint templates for edges, exclude_groups, cluster bindings (cluster_llm), registering a custom extractor strategy, and how unbound elements behave (on_missing_binding). Trigger on: map grammar elements to extractors, route prose to regex and tables to query, bind a funds/measures edge, derive node ids, id_template/id_from, register_extractor a custom strategy, cluster_llm one-pass extraction, on_missing_binding, fix a binding error.

Evaluate / verify a creel extraction — the answer to "is this extraction correct?" is a pluggable Verifier, NOT a hardcoded == . Use when you need to score extraction quality, compare an actual graph to an expected_graph, build a test corpus, or pick the right comparison per attribute. Covers the verifier-kind taxonomy (exact, normalized, numeric_tolerance, set_match, schema_constraint, semantic_similarity, composite), graph_match (decomposable partial credit with a structured mismatch report), and llm_rubric (a verifier defined by a natural-language criterion, graded by an injected LLM judge). Keywords: pluggable Verifier not equality, graph_match partial credit, numeric_tolerance, semantic similarity, natural-language LLM rubric verifier, score extraction quality.

Extract a typed property graph from messy sources with creel. Use when the user wants to turn prose, tables, JSON, or PDFs/documents into a clean, typed graph — i.e. run extract(), build a property graph, go sources->graph, use the creel facade, or get started with creel. Triggers on "extract a graph from documents", "build a knowledge/property graph with creel", "creel quickstart", "how do I call extract()", "sources to graph", "turn this prose/table/JSON into a typed graph", "ingest a folder of files/PDFs into a graph", "the one-call creel workflow". This is the ENTRY/orchestrator skill: it covers the end-to-end extract(sources, graph_spec, extractors) -> graph call, its keyword args (services, on_missing_binding, resolve, cache), and how to read the returned Graph (nodes, edges, evidence, report, validate_graph, to_canonical_json). Hand off to creel-grammar, creel-bindings, creel-ai, creel-evaluation, creel-projections for the deeper pieces.

Use when you need to define a creel grammar (a GraphSpec) — the typed schema for the graph you want to extract. Covers declaring node-types and edge-types with AttrSchema attributes; first-class edges that carry their own typed attributes (a funds edge with amount/currency); attribute options (required, range, enum, minimum/maximum, pattern, multivalued, description); reusable EnumDef value-sets and named ranges; inheritance via is_a/abstract/mixins; validating a grammar (validate_spec) and an instance graph (validate_graph); and optionally authoring in LinkML to generate JSON-Schema or Pydantic. Trigger on "define a creel grammar", "declare node-types and edge-types", "typed attributes on edges", "enums/ranges/inheritance for graph extraction", "schema for graph extraction", "LinkML authoring", or "validate a spec".

Project/transform a creel graph into everything downstream — persist, query, view, export, annotate are all projections of the one graph, never engine features. Use after you have a Graph and want to clean it up or get it OUT. Covers entity resolution (merge duplicate/same-entity nodes via Normalize/Registry/LLM/Cascade resolvers, the resolve= facade arg, or resolve_graph); the reify/unreify toggle (attributed edge ⇄ relation node when it goes n-ary, losslessly); view projections (to_node_edge_records/to_table/to_dot/to_mermaid/to_cytoscape); export adapters (to_jgf/to_graphml/to_cypher/to_turtle — GraphML, JGF, parameterized Cypher for Neo4j, RDF-star Turtle); the annotate/render contract; and trace (per-attribute grounding, TraceIndex reverse lookup, reanchor to source span). Triggers on "merge duplicate nodes", "entity resolution", "reify edge to node", "view as Mermaid/DOT/Cytoscape", "export to GraphML/Cypher/RDF/Turtle", "annotate a graph", "trace a value to its source span", "downstream rendering".

Use when building or extending creel's evaluation/verification subsystem, or when writing tests for extraction. Covers the Verifier Protocol, the verifier-kind taxonomy (exact/normalized/numeric_tolerance/set_match/graph_match/schema_constraint/semantic_similarity/llm_rubric/composite), how an LLM-instruction (G-Eval) verifier is specified and run, the test-corpus layout ({sources, expected_graph, verifier_overrides?}), per-element→per-type→per-graph score roll-up, and the rule that comparisons are pluggable verifiers — NOT hardcoded equality. Trigger on work in creel/verify/, the eval runner, the RBM corpus, or any extraction test.

Use when working anywhere inside the creel package internals — the source-to-graph extraction engine. Covers the architecture map (layers/modules), the 15 design decisions D1–D15, the key Protocol interfaces, the canonical JSON contract, the two-layer (grammar vs bindings) join, and where each choice is justified in the research. Trigger when adding/changing core modules, wiring the extract() facade, the graph model, the spec/grammar layer, or canonical JSON; or when a design question needs the authoritative answer.

Use when adding, changing, or wiring an extraction strategy in creel — the pluggable mechanisms that detect and pull a graph element's value(s) from sources. Covers the Extractor Protocol, the three strategy families (LLM/NL-description, query over structured sources, pattern/function), the registry + entry points, the schema-as-extractor default, the bindings→join→facade flow, and how every extraction attaches an evidence record (provenance + grounding + confidence). Trigger on work in creel/extract/, bindings.py, join.py, facade.py, or evidence.py.

Use when building or changing creel's document ingestion layer — turning raw files (PDF, DOCX, XLSX, HTML, Markdown, images) into Sources the extractors consume. Covers the route-by-format + quality-gate escalation strategy, the structure-preserving local default (Docling), grounding/provenance requirements (page/cell/char-span/bbox), the permissive-license discipline, and when to escalate to OCR/VLM or a multimodal model (Claude native PDF + Citations). Trigger on work in creel/ingest/, file loaders, format handling, OCR, or document parsing. Authoritative source: misc/docs/research/13-document-ingestion.md and decision D-OP7.