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 | $0.00015 | $0.01207 |
| Sonnet 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 11d 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.
- 11d 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 (44 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. 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.
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