Results for “chaining”

Design, tune, and evaluate production RAG pipelines with deterministic tools for chunking, pipeline design, and retrieval evaluation.

Train or fine-tune sentence-transformers models for retrieval, similarity, clustering, classification, and reranking, with support for bi-encoders, cross-encoders, and sparse encoders.

Trains, evaluates, exports, and runs inference for person re-identification models using TAO, learning discriminative embeddings for cross-camera matching.

Chunks a book into canonical retrieval units with heading-aware structure splitting, recursive token targets, and contextual prefixes for downstream RAG ingestion.

Composite skill — query, capture, improve, and persist knowledge in one workflow. Chains recall (RAG query) → sync-memories (write durable note) → rag-curate (improve weak retrievals) → handoff (durable snapshot if session-ending). Use when the work involves "what did we decide", "remember this", "save where we are", or any closing checkpoint.

Automates genome-wide association studies from genotype files to publication-ready results, running PLINK2 QC and REGENIE regression with Manhattan and QQ plots.

Manages vector embeddings with Pinecone for semantic search, recommendation, and RAG pipelines.

Build, run, and visualize multi-step AI generation workflows by chaining image, video, and audio nodes into automated pipelines.

Designs and implements production-grade RAG systems by chunking documents, generating embeddings, configuring vector stores, building hybrid search pipelines, applying reranking, and evaluating retrieval quality.

Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications.

Use this skill for search indexing, embeddings, RAG chunking, freshness, retrieval evaluation, source citations. Trigger when the task involves ai engineering work related to Search Indexing RAG, implementation, audits, debugging, strategy, or validation.

Inspect or select a Clade execution connection while keeping agent runtime, inference provider, wire protocol, and model distinct. Use for Claude/Codex runtime selection, Anthropic/OpenAI/MiniMax/Kimi/custom gateways, model routing, or connection troubleshooting.

Routes a research topic through a structured two-phase workflow: generate an extensible outline, then fan out parallel web-search agents to investigate each item into validated JSON, producing a complete markdown report with table of contents.

The two model-touching halves of Worklink, shipped as one opt-in skill. (1) Planner: decompose a parent Chainlink issue into testable worklink:ready leaf subissues (mutates Chainlink only, never executes). (2) Ready-queue poller: discovers worklink:ready leaves and dispatches them by invoking `mimir worklink run` as a detached subprocess, up to the concurrent-claim cap — it never reimplements claim/evidence/transition. Opt-in (mimirbot yes, muninn no): `mimir skills install chainlink-orchestrator`, then set the env below to enable autonomous dispatch.

Process the Refinery merge queue - collect agent work, detect and resolve conflicts, merge in dependency order, and verify integration.

Creates reusable command-line scripts and utilities for the Hugging Face API, enabling chaining, piping, and intermediate data processing.