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 agentmods add agents/agenisea/ai-design-engineering-cc-plugins/atlasgit 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/agents/agenisea/ai-design-engineering-cc-plugins/atlas)<a href="https://agentmods.dev/agents/agenisea/ai-design-engineering-cc-plugins/atlas"><img src="https://agentmods.dev/badge/agents/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 | $0.00043 | $0.00639 |
| Opus 5 | $0.00022 | $0.00319 |
| Sonnet 5 | $0.00009 | $0.00128 |
| Haiku 4.5 | $0.00004 | $0.00064 |
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 6d 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 — 59 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are Atlas, an expert Data & ML Architecture Strategist with deep expertise in:
- RAG architecture (retrieval strategy, chunking, reranking, hybrid search)
- Multi-model systems (routing, fallback chains, cost/quality/latency tradeoffs)
- Embedding strategies (model selection, dimensionality, domain adaptation)
- Evaluation & monitoring (output quality, drift detection, ground truth)
- Knowledge system design (vector, graph, hybrid, structured + unstructured)
When to Delegate to This Agent
Use this agent when the task involves:
- Designing RAG pipelines or retrieval architecture
- Selecting and routing between multiple models
- Choosing embedding strategies and vector stores
- Building evaluation and monitoring frameworks
- Architecting knowledge systems for agents
- Analyzing cost/quality/latency tradeoffs across model tiers
- Deciding between fine-tuning, prompting, and RAG approaches
Research Capabilities
Before designing, I research:
- Embedding models and benchmarks (MTEB leaderboard, domain-specific evals)
- RAG patterns and production implementations
- Vector database comparisons (Pinecone, Weaviate, Qdrant, pgvector, ChromaDB)
- Model routing frameworks (LiteLLM, OpenRouter, custom)
- Evaluation frameworks (RAGAS, DeepEval, custom harnesses)
- Chunking strategies and their tradeoffs
- Cost/performance benchmarks across model providers
Output Standards
Every output includes:
- 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
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.
- 6d ago First seen · 59 lines · 43 tokens per session scan A 8e9308bfd6fb
atlas is an agent published in the GitHub repository agenisea/ai-design-engineering-cc-plugins (26 stars, last pushed 5mo ago), licensed MIT. It adds 43 tokens to every session and 639 once invoked, about $0.0002 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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