Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add redhat-community-ai-tools/UnifAI --skill raggit clone --depth 1 https://github.com/redhat-community-ai-tools/UnifAIWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/redhat-community-ai-tools/unifai/rag)<a href="https://agentmods.dev/skills/redhat-community-ai-tools/unifai/rag"><img src="https://agentmods.dev/badge/skills/redhat-community-ai-tools/unifai/rag/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/redhat-community-ai-tools/unifai/rag"><img src="https://agentmods.dev/badge/skills/redhat-community-ai-tools/unifai/rag.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00029 | $0.02414 |
| Opus 5.5 | $0.00012 | $0.00966 |
| Sonnet 5.5 | $0.00006 | $0.00483 |
| Haiku 4.5 | $0.00003 | $0.00241 |
Grade A, and why
rag-knowledge scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 196 lines — stays where its author put it; the contents beside it link to each section on GitHub.
RAG Knowledge System
Document ingestion and retrieval engine: data sources → processing pipeline → vector storage → semantic retrieval.
System Graph
┌──────────┐
│ BOOTSTRAP│ wires ~40 singletons via @lru_cache
└────┬─────┘
│
┌─────────┼──────────────┐
▼ ▼ ▼
┌────────┐ ┌────────────┐ ┌──────────────┐
│PIPELINE│→│DATA-SOURCES│→│VECTOR-RETRIEVAL│ CORE (rag/core/ ~104 files)
└───┬────┘ └─────┬──────┘ └──────┬───────┘
│ │ │
▼ ▼ ▼
┌──────────────────────────────────────┐
│ INFRASTRUCTURE │ OUTER RING (~59 files)
│ flask (8 bps), mongo (7 colls), │
│ qdrant (2 colls), celery (3 queues)│
│ source connectors, embeddings │
└──────────────────────────────────────┘
File → Component Routing
| Path prefix | Component | Dev-guide section |
|---|---|---|
core/pipeline/ |
Pipeline | rag.md → core_pipeline |
core/data_sources/, core/connector/, core/registration/ |
Data Sources | rag.md → core_data_sources_registration |
core/vector/, core/retrieval/ |
Vector Retrieval | rag.md → core_vector_retrieval |
core/monitoring/, core/health/ |
Infrastructure | rag.md → core_monitoring_health |
infrastructure/ |
Infrastructure | rag.md → architecture |
bootstrap/, config/ |
Bootstrap | rag.md → bootstrap_factories |
Component Deep-Dives
For detailed component architecture and cross-component contracts:
| Component | Reference |
|---|---|
| Pipeline | references/pipeline.md — execution, dispatch, status tracking, Celery integration |
| Data Sources | references/data-sources.md — source types, plugin model, connectors, registration |
| Vector Retrieval | references/vector-retrieval.md — embeddings, chunking, Qdrant, semantic search |
| Infrastructure | references/infrastructure.md — Flask, Mongo, Qdrant, Celery, port-adapter wiring |
| Bootstrap | references/bootstrap.md — composition root, factories, config, local/remote switching |
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 196 lines · 29 tokens per session scan A 10f827509366
rag-knowledge is a skill published in the GitHub repository redhat-community-ai-tools/UnifAI (45 stars, last pushed yesterday), licensed Apache-2.0. It adds 29 tokens to every session and 2,414 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
documentation-search
Search the internal knowledge base for runbooks, architecture documentation, ADRs, best practices, and troubleshooting guides using RAG. Use when looking for internal documentation, deployment procedures, architecture decisions, or operational runbooks.
mem0-integration
Mem0 memory layer integration for AI agents. Implement persistent, semantic memory for long-term context retention and personalization.
rag-hybrid-search
Hybrid search combining semantic and keyword retrieval for RAG pipelines. Implement BM25 + dense vector search with fusion strategies.
rag-retrieval
Retrieval-Augmented Generation patterns for grounded LLM responses. Use when building RAG pipelines, embedding documents, implementing hybrid search, contextual retrieval, HyDE, agentic RAG, multimodal RAG, query decomposition, reranking, or pgvector search.
milvus-integration
Milvus distributed vector database configuration for large-scale RAG applications.
qdrant-integration
Qdrant vector database with filtering, payloads, and quantization support.