scipy-pro

Plugin scipy-pro from marketplace codex-marketplace in Travis-Gilbert/claude-marketplace. Commands read from the repo's manifest — 1 month ago.

Skills (1)

• epistemic-engine

  Use when tasks involve the research_api epistemic engine: evidence intake, claim extraction, contradiction or tension analysis, model comparison, method DSL and execution, promotion pipelines, domain packs, retrieval/embedding/NLI tuning, self-organization loops, or two-mode (Railway vs local/dev) a

Slash commands (4)

• /scipy-pro:encode

  Executable knowledge -- from evidence to methods. DSL design, compilation, provenance, promotion pipeline, method execution.

• /scipy-pro:gather

  Acquisition and training -- from the web to training data. Firecrawl scraping, corpus construction, embedding fine-tuning, evaluation.

• /scipy-pro:graph

  Graph intelligence -- from objects to structure. Community detection, causal inference, self-organization, gap analysis, embeddings.

• /scipy-pro:reason

  Epistemic reasoning -- from raw text to structured claims, tensions, and models. NLP, NLI, knowledge representation, claim analysis.

Subagents (12)

• causal-inference

  Specialist in tracing how ideas influenced each other over time. Handles temporal precedence filtering, influence DAG construction, provenance chains, and lineage tracing. Invoke when working on causal_engine.py, provenance.py, engine.py Pass 7, or any code that tracks the temporal evolution of know

• claim-analysis

  Specialist in propositional reasoning — decomposing text into atomic claims and determining support, contradiction, or neutrality between them. Handles claim decomposition (LLM and rule-based), NLI classification with CrossEncoder, stance detection, and epistemic status tracking. Invoke when working

• graph-theory

  Specialist in discovering structure within relationship data. Handles community detection (Louvain, Leiden), centrality measures (PageRank, betweenness, eigenvector), Personalized PageRank, structural hole analysis, spectral methods, and DAG construction. Invoke when working on graph.py, community.p

• information-retrieval

  Specialist in lexical and semantic search over growing corpora. Handles BM25 scoring, TF-IDF weighting, FAISS approximate nearest-neighbor indexes, re-ranking with cross-encoders, query expansion, and reciprocal rank fusion. Invoke when building or modifying any retrieval path — compose_engine searc

• knowledge-representation

  Specialist in structuring what is known and uncertain. Handles knowledge graph embeddings (PyKEEN RotatE, DE-SimplE), ontology design (ObjectType, ComponentType, promotion pipeline), and epistemic primitives (Claim, Question, Tension, Model, Method, Narrative). Invoke when designing data models, wor

• nlp-pipeline

  Specialist in extracting linguistic structure from natural language text. Handles NER with spaCy, adaptive entity recognition via graph-learned PhraseMatcher, sentence embeddings (SBERT, E5, Nomic), cross-encoder scoring, and tokenization strategy. Invoke when building or modifying any NLP extractio

• probabilistic-reasoning

  Manages uncertainty, confidence decay, and evidence weighting in evolving knowledge graphs. Use when working on edge strength scoring, Bayesian updates, confidence calibration, novelty dial tuning, or any system that must quantify how much it knows vs. how much it guesses. Examples: - <example>User

• program-synthesis

  Turns stored knowledge into executable procedures via a domain-specific language. Use when working on Method definitions, DSL design, rule compilation, method versioning, or any system that bridges captured information and encoded executable knowledge. Examples: - <example>User asks "how do I turn t

• self-organization

  Designs and maintains feedback loops that let the knowledge graph modify its own structure safely. Use when working on auto-classification, community clustering, entity promotion, edge decay, emergent type detection, or any system that reorganizes itself based on its own outputs. Examples: - <exampl

• software-architecture

  Makes epistemic systems run reliably in production across constrained and rich runtimes. Use when working on deployment, the two-mode contract, RQ task queues, caching layers, memory budgets, graceful degradation, or any infrastructure concern that affects how the system operates. Examples: - <examp

• training-pipeline

  Improves retrieval, classification, and knowledge graph models from accumulated user data. Use when working on triplet construction, fine-tuning SBERT, active learning, KGE training, evaluation metrics, or any system that learns from the knowledge graph's own data. Examples: - <example>User asks "ho

• web-acquisition

  Gets evidence into the epistemic system from the web. Use when working on content extraction, URL capture, PDF ingestion, Firecrawl pipelines, source typing, corpus construction, or any system that brings external information into the knowledge graph. Examples: - <example>User asks "how do we scrape

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