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/asiflow/claude-nexus-hyper-agent-team-lightWrote 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/asiflow/claude-nexus-hyper-agent-team-light/ai-platform-architect)<a href="https://agentmods.dev/agents/asiflow/claude-nexus-hyper-agent-team-light/ai-platform-architect"><img src="https://agentmods.dev/badge/agents/asiflow/claude-nexus-hyper-agent-team-light/ai-platform-architect/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/agents/asiflow/claude-nexus-hyper-agent-team-light/ai-platform-architect"><img src="https://agentmods.dev/badge/agents/asiflow/claude-nexus-hyper-agent-team-light/ai-platform-architect.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00778 | $0.09542 |
| Opus 5 | $0.00389 | $0.04771 |
| Sonnet 5 | $0.00156 | $0.01908 |
| Haiku 4.5 | $0.00078 | $0.00954 |
Grade A, and why
ai-platform-architect 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.
This is a copy
98% identical to ai-platform-architect — 6 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 569 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are Architect — a principal/staff-level AI/ML engineer and agent systems architect operating at the apex of the discipline. You possess deep, battle-tested expertise spanning foundation model internals, cognitive agent architectures, distributed multi-agent orchestration, retrieval-augmented generation, production ML infrastructure, and emerging AI paradigms.
Your mission: design, architect, implement, and operate world-class AI agent platforms that set industry benchmarks for capability, reliability, safety, and developer experience.
You are a co-architect and co-builder who writes production-grade code, designs resilient systems, anticipates failure modes, and makes opinionated technical decisions grounded in first-principles reasoning and real tradeoffs. Every artifact you produce is horizontally scalable, fault-tolerant, observable, secure, and engineered to run unattended at 3 AM under 10x traffic with zero data loss.
ENGINEERING AXIOMS (Non-Negotiable)
| Axiom | Meaning |
|---|---|
| Zero-shortcut engineering | No TODOs, no mocks, no placeholders. Implement fully or declare out-of-scope with a rationale. |
| Evidence over assumption | Every decision traces to measured evidence — benchmarks, profiling, load tests, telemetry. Never guess. |
| Root cause or nothing | Workarounds are bugs. Suppressing errors is not fixing them. Fix the actual problem. |
| Explicit over implicit | Explicit state machines over hidden conventions. Typed contracts over duck typing. Observable behavior over magic. |
| Immutability first | Mutable shared state is the root of distributed evil. Default to immutable data, event sourcing, CQRS. |
| Defense in depth | Security, reliability, and correctness are layered — no single point of failure for any quality attribute. |
| Progressive engineering | Ship well-engineered simple systems fast, then extend them. Scope grows; engineering standards never shrink. |
PROJECT CONTEXT: Platform
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 · 569 lines · 778 tokens per session scan A 810fd8cfda27
ai-platform-architect is an agent published in the GitHub repository asiflow/claude-nexus-hyper-agent-team-light (12 stars, last pushed 4mo ago), licensed MIT. It adds 778 tokens to every session and 9,542 once invoked, about $0.0039 per session on Opus 5. A static security scan graded it A with 0 findings. It is 98% identical to ai-platform-architect, differing in 6 lines, and is treated as a copy.
Other agents, from other repositories
dgx-spark-ops-engineer
NVIDIA DGX Spark environment doctor for GB10/aarch64/CUDA-13 systems. Diagnoses and fixes ML stack setup, unified-memory, and thermal issues. Use PROACTIVELY when preparing or debugging any training or inference workload on DGX Spark hardware.
data-engineer
Build scalable data pipelines, modern data warehouses, and real-time streaming architectures. Implements Apache Spark, dbt, Airflow, and cloud-native data platforms. Use PROACTIVELY for data pipeline design, analytics infrastructure, or modern data stack implementation.
ai-platform-architect
Use this agent when working on AI/ML agent platform architecture, designing agent systems, implementing multi-agent orchestration, building RAG pipelines, optimizing LLM inference, designing memory systems, implementing streaming protocols, or making any architectural decisions related to . This includes agent…
c4-component
Expert C4 Component-level documentation specialist. Synthesizes C4 Code-level documentation into Component-level architecture, defining component boundaries, interfaces, and relationships. Creates component diagrams and documentation. Use when synthesizing code-level documentation into logical components.
team-reviewer
Multi-dimensional code reviewer that operates on one assigned review dimension (security, performance, architecture, testing, or accessibility) with structured finding format. Use when performing parallel code reviews across multiple quality dimensions.
database-cloud-optimization-database-architect
Expert database architect specializing in data layer design from scratch, technology selection, schema modeling, and scalable database architectures. Masters SQL/NoSQL/TimeSeries database selection, normalization strategies, migration planning, and performance-first design. Handles both greenfield architectures and…