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 codebasegit 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/codebase)<a href="https://agentmods.dev/skills/redhat-community-ai-tools/unifai/codebase"><img src="https://agentmods.dev/badge/skills/redhat-community-ai-tools/unifai/codebase/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/codebase"><img src="https://agentmods.dev/badge/skills/redhat-community-ai-tools/unifai/codebase.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 | $0.00042 | $0.00870 |
| Opus 5.5 | $0.00017 | $0.00348 |
| Sonnet 5.5 | $0.00008 | $0.00174 |
| Haiku 4.5 | $0.00004 | $0.00087 |
Grade A, and why
codebase-overview 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 — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.
UnifAI Codebase Overview
UnifAI is a multi-service platform for building and running AI agent workflows. The monorepo contains backend services (Python/Flask), a frontend (React/TypeScript), shared libraries, and infrastructure tooling.
Domain Routing Table
| Path prefix | Domain | Skill |
|---|---|---|
multi-agent/ |
Multi-Agent System (MAS) | domains/multi-agent/SKILL.md |
rag/ |
RAG Pipeline | domains/rag/SKILL.md |
shared-resources/identity/ |
Identity & Auth | domains/identity/SKILL.md |
ui/client/src/ |
Frontend UI | domains/ui/SKILL.md |
global_utils/ |
Global Utilities | domains/global-utils/SKILL.md |
backend/ |
Platform Backend | domains/backend/SKILL.md |
rag/infrastructure/celery/ |
Celery Workers | domains/celery/SKILL.md |
temporal-worker/ |
Temporal Workers | domains/temporal-worker/SKILL.md |
helm/ |
Helm / Deploy Charts | domains/helm/SKILL.md |
Domain skills are auto-surfaced via paths when editing files in their scope.
Service Architecture Summary
| Service | Role | Tech Stack | Port |
|---|---|---|---|
| multi-agent | AI agent orchestration engine | Python, Flask, MongoDB, Redis, LangGraph | 5001 |
| rag | Document ingestion & retrieval | Python, Flask, MongoDB, Qdrant, Celery | 5002 |
| identity | Auth, teams, SSO | Python, Flask, Keycloak, MongoDB | 5003 |
| backend (platform) | Admin config, platform API | Python, Flask, MongoDB | 5000 |
| ui | Web frontend | React, TypeScript, Vite, TailwindCSS | 3000 |
| global_utils | Shared Python library | Python (package) | — |
| celery | Async task workers for RAG | Python, Celery, Redis | — |
| temporal-worker | Distributed workflow execution | Python, Temporal | — |
Cross-Cutting Concerns
| Concern | Where it lives |
|---|---|
| Authentication | shared-resources/identity/ + global_utils/identity.py |
| Configuration | Each service's config/ + global_utils/config/ |
| MongoDB access | Each service's adapters/ or infrastructure/ |
| Redis caching | global_utils/redis/ + per-service usage |
| Message queues | RabbitMQ via Celery (global_utils/celery_app/) |
| Workflow orchestration | Temporal (multi-agent/adapters/outbound/temporal/) |
What ships with it
27 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.
- domains/backend/SKILL.md 6.4 KB
- domains/celery/SKILL.md 4.6 KB
- domains/global-utils/SKILL.md 6.6 KB
- domains/helm/recipes/add-shared-pvc.md 2.5 KB
- domains/helm/references/shared-storage.md 1.5 KB
- domains/helm/SKILL.md 4.4 KB
- domains/identity/SKILL.md 7.2 KB
- domains/multi-agent/recipes/add-new-llm.md 9.7 KB
- domains/multi-agent/recipes/add-new-node.md 11 KB
- domains/multi-agent/recipes/add-new-provider.md 8.7 KB
- domains/multi-agent/recipes/add-new-tool.md 8.1 KB
- domains/multi-agent/references/adapters.md 10 KB
- domains/multi-agent/references/bootstrap.md 4.0 KB
- domains/multi-agent/references/core.md 8.1 KB
- domains/multi-agent/references/elements.md 12 KB
- domains/multi-agent/references/engine-graph.md 6.1 KB
- domains/multi-agent/references/resources.md 30 KB
- domains/multi-agent/references/session.md 6.0 KB
- domains/multi-agent/SKILL.md 13 KB
- domains/rag/references/bootstrap.md 4.9 KB
- domains/rag/references/data-sources.md 5.2 KB
- domains/rag/references/infrastructure.md 6.4 KB
- domains/rag/references/pipeline.md 4.7 KB
- domains/rag/references/vector-retrieval.md 4.6 KB
- domains/rag/SKILL.md 10 KB
- domains/temporal-worker/SKILL.md 6.6 KB
- domains/ui/SKILL.md 11 KB
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 · 67 lines · 42 tokens per session scan A 77e4d4d01064
codebase-overview is a skill published in the GitHub repository redhat-community-ai-tools/UnifAI (45 stars, last pushed yesterday), licensed Apache-2.0. It adds 42 tokens to every session and 870 once invoked, about $0.0002 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
mem0-integration
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rag-hybrid-search
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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.
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.
rag-chunking-strategy
Document chunking with multiple strategies including semantic, recursive, and fixed-size chunking.
vector-memory
HNSW vector search for pattern similarity retrieval and knowledge graph maintenance with PageRank scoring, community detection, and 3-tier memory management.