Results for “chaining”

Add plugin tools, connect MCP servers, or chain existing tools in one script within the LaRuche agent environment.

Builds a hyperpersonalization packet for each lead by chaining company-researcher, cro-auditor, and person-researcher. The packet feeds into message-generator for Tier 3 personalized outreach.

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

Converts existing frontend code into a Stitch Design by chaining static HTML extraction, design system extraction, and file upload.

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

Build clean async pipelines with fp-ts TaskEither, replacing nested try/catch with composable error handling, chaining, and parallel execution.

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

Documents MariaDB-specific CREATE USER syntax and behavior, including authentication chaining, TLS options, resource limits, password expiry, and account locking, to help write accurate statements.

End-to-end source processing -- seed, reduce, process all claims through reflect/reweave/verify, archive. The full pipeline in one command. Triggers on "/pipeline", "/pipeline [file]", "process this end to end", "full pipeline".

Retrieval-Augmented Generation pipelines — ingestion, chunking, embedding, vector stores, retrieval, evaluation. Use when building a RAG pipeline, choosing chunking strategies or embedding models, debugging retrieval quality or hallucinations, evaluating an existing RAG system, or scaling/migrating vector stores.

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.

Layer alternative channels / scrapers / data sources with explicit fall-through, where each rung covers a different failure mode. Use when interacting with the world (sending a notification, fetching from a third party, detecting a state change, authenticating) and the single canonical path could fail in known ways. Distinct from retrying — retries handle transient failures; fallback chains handle modal ones (channel down, API gone, cookie expired). Three layers tops; the terminal rung is loud.

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.

Use this skill for transporting steel coils. Covers § 393.120 specific rules, chain requirements, dunnage placement, and inspection procedures.

Identify command-and-control beaconing patterns in network traffic by applying statistical frequency analysis, jitter calculation, and coefficient of variation scoring to detect periodic callbacks from compromised endpoints.

Manage Binding entities — scoped, temporal, many-to-many relationships between v2 entity surfaces. Use when a relationship needs scope, time, or its own attributes — e.g. 'Alice is the tech lead for project X from Jan to June' or 'the security gate applies to this release WorkItem only on the main branch'.

Detects DNS tunneling by computing Shannon entropy of DNS query names, analyzing query length distributions, inspecting TXT record payloads, and identifying high subdomain cardinality using scapy for packet capture analysis.

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.

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.

Turn a pile of accounts into a stack-ranked priority list with a reason on every row. A layered score (gates first, then an evidence-weighted base rank over the signals you actually have, then bounded boosts for product usage and buyer intent) that stays fair across channels and never scores a blank field as a zero. Built for B2B GTM teams, customizable to your signals and your ICP. Trigger on "score these accounts", "rank by fit", "composite ICP score", "stack-rank my list", "who should I work first", "prioritize these leads", or any multi-signal account qualification.

Reduce voluntary and involuntary churn through cancel flow design, save offers, exit surveys, and dunning sequences.

Full pipeline from signal detection to scored lead in CRM. Chains signal-scanner, lead-scorer, and crm-writer into one flow.

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

Conduct passive OSINT reconnaissance on target organizations using a MITRE ATT&CK framework. Discovers employees, email patterns, subdomains, infrastructure, leaked credentials, and cloud assets with confidence-scored findings.

Optional low-priority poller that inventories Worklink tool pins from <home>/worklink.yaml and files/reuses Chainlink bump issues when upstream versions drift. Opt-in: copy this directory into <home>/skills/worklink-tool-pins/ and configure tool_pins in worklink.yaml.

Read and query onchain data using events, The Graph, Dune, Ponder, Multicall3, and WebSocket subscriptions, with guidance on block iteration limitations.

Use to design VIP or tiered support programmes and to test whether higher tiers actually deliver better outcomes. Trigger for "VIP support programme", "service tiering", queue priority for premium customers, entitlement vs priority, tier inflation, or "does our enterprise tier get faster support".