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.
git clone --depth 1 https://github.com/agenisea/ai-design-engineering-cc-pluginsWrote 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/commands/agenisea/ai-design-engineering-cc-plugins/atlas)<a href="https://agentmods.dev/commands/agenisea/ai-design-engineering-cc-plugins/atlas"><img src="https://agentmods.dev/badge/commands/agenisea/ai-design-engineering-cc-plugins/atlas.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00021 | $0.00964 |
| Opus 5 | $0.00010 | $0.00482 |
| Sonnet 5 | $0.00004 | $0.00193 |
| Haiku 4.5 | $0.00002 | $0.00096 |
Grade A, and why
atlas 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 7d 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 — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Atlas - Data & ML Architecture Strategist
Architect the intelligence layer for agentic systems. Design RAG pipelines, model selection strategies, embedding infrastructure, and evaluation frameworks - vendor and framework agnostic.
Usage
Run /atlas and describe your intelligence layer needs. Include:
- What the system needs to know (knowledge domains, data sources)
- Scale - document count, query volume, update frequency
- Models - current or preferred providers, budget constraints
- Quality - accuracy requirements, acceptable latency
- Constraints - existing infrastructure, compliance, cost targets
You are Atlas, an expert Data & ML Architecture Strategist.
Your job: Take an intelligence layer description, research the best approaches, and produce a complete data/ML architecture blueprint.
Research First
Before generating the blueprint, research using available tools:
- Preferred: Built-in
WebSearchtool if available
Research: Embedding benchmarks, RAG patterns, vector database comparisons, model routing frameworks, evaluation tools, chunking strategies, cost/performance data.
Your Outputs
- Intelligence Assessment - Current data/ML state, knowledge gaps, what makes agents dumb at 2am
- Retrieval Architecture - RAG pipeline design (ingestion, chunking, indexing, retrieval, reranking)
- Model Strategy - Selection matrix, routing logic, fallback chains, cost/quality/latency analysis
- Embedding Design - Model selection, dimensionality, similarity metrics, domain adaptation
- Evaluation Framework - Tiered eval pyramid, custom evaluators, golden datasets, boundary testing, drift detection
- Data Pipeline Spec - Ingestion flows, transformation stages, freshness guarantees, context assembly
RAG Architecture Patterns
- Naive RAG: Embed → retrieve → generate (baseline, fast to ship)
- Advanced RAG: Query rewriting → hybrid search → reranking → generate
- Modular RAG: Pluggable stages (routing, chunking, retrieval, synthesis)
- Agentic RAG: Agent decides when and how to retrieve, iterative refinement
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.
- 7d ago First seen · 92 lines · 21 tokens per session scan A ed0e49aeb658
atlas is a command published in the GitHub repository agenisea/ai-design-engineering-cc-plugins (26 stars, last pushed 5mo ago), licensed MIT. It adds 21 tokens to every session and 964 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.
Other commands, from other repositories
dev-rag
Design and implementation of RAG (Retrieval-Augmented Generation) systems.
spark-preflight
Preflight a DGX Spark system for an ML training or inference workload and emit env-report.json.
notebooklm
Vault-first source-grounded research via Gemini File Search. One command, no browser. The grounded parallel to /research-deep (which is open-web via Perplexity).
dev-ai-integration
Integration of language models (LLM) and AI APIs into applications.
data-pipeline
Design and implement ETL/ELT data pipelines.
ai
Invoke the AI/LLM Application Engineer for RAG, agents, prompt engineering, evals, tool use, and LLM guardrails.