RAG pipeline, embeddings, LLM interactions, and flow orchestration.
Scanned 2/12/2026
Install via CLI
openskills install majiayu000/claude-skill-registry---
name: context-llm-pipeline
description: RAG pipeline, embeddings, LLM interactions, and flow orchestration.
tags: [backend, ai, rag]
---
# LLM Pipeline Context
## Overview
Core AI logic including RAG flows, LLM service orchestration, and vector retrieval.
## Active Files
### Orchestration (Flows)
- `backend/flows/ingestion_flow.py` - Document ingestion
- `backend/flows/scraping_flow.py` - Scrape orchestration
- `backend/flows/template_review_flow.py` - LLM review flow
### Services
- `backend/services/llm/orchestrator.py` - Main LLM handler
- `backend/services/llm/pipeline.py` - Pipeline logic
- `backend/services/llm/analyzer.py` - Analysis logic
- `backend/services/research/zai.py` - ZAI research integration
- `backend/services/search_pipeline_service.py` - Search pipeline
### Shared Packages
- `packages/llm-common/` - Shared types and utilities (Submodule)
## Usage
Use this skill when working on RAG, prompt engineering, or vector search logic.
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Adversarial multi-agent planning skill. Self-orchestrates 5 hostile category members (unspecified-low, unspecified-high, deep, ultrabrain, artistry) via team-mode for ruthless cross-critique debate, distills only the defensible insights, then MANDATORILY hands the distilled insight bundle to the `plan` agent for executable plan formalization. Use when planning needs maximum rigor and surfacing of weak assumptions, blind spots, and over-engineering. Triggers: 'hyperplan', 'hpp', '/hyperplan', ...