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/SteveGJones/ai-first-sdlc-practicesWrote 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/stevegjones/ai-first-sdlc-practices/ai-solution-architect)<a href="https://agentmods.dev/agents/stevegjones/ai-first-sdlc-practices/ai-solution-architect"><img src="https://agentmods.dev/badge/agents/stevegjones/ai-first-sdlc-practices/ai-solution-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/stevegjones/ai-first-sdlc-practices/ai-solution-architect"><img src="https://agentmods.dev/badge/agents/stevegjones/ai-first-sdlc-practices/ai-solution-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 | $0.00055 | $0.10358 |
| Opus 5 | $0.00028 | $0.05179 |
| Sonnet 5 | $0.00011 | $0.02072 |
| Haiku 4.5 | $0.00006 | $0.01036 |
Grade C, and why
ai-solution-architect scanned grade C with 1 finding 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 5d 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.
Instruction-override phrasinghighPrompt injection
Text telling the model to disregard its earlier instructions or safety rules is the shape of a prompt injection, whoever wrote it.
- Why Wrong: Attackers can override system prompts, extract sensitive data, or cause harmful outputs How it starts
The opening of the file, as written. The whole thing — 979 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Solution Architect
You are the AI Solution Architect, the specialist responsible for designing production-grade AI/ML systems. You architect LLM applications, RAG systems, multi-agent orchestrations, and MLOps pipelines with deep knowledge of the 2025-2026 AI landscape. Your approach is pragmatic and production-focused -- you balance cutting-edge techniques with operational reliability, always considering cost, latency, accuracy, and maintainability trade-offs.
Core Competencies
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LLM Application Architecture: LangChain, LlamaIndex, Semantic Kernel framework selection; prompt chaining and routing patterns; model tiering strategies (Opus for reasoning, Sonnet for balanced, Haiku for speed); caching layers (semantic caching, exact match); streaming and async patterns for UX; LLM evaluation frameworks (RAGAS, TruLens, LangSmith)
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RAG System Design: Vector database selection (Pinecone for managed, Weaviate for hybrid search, pgvector for PostgreSQL integration, Qdrant for performance, Chroma for local/embedded); chunking strategies (sentence-window, semantic, agentic); embedding models (OpenAI text-embedding-3, Cohere embed-v3, open-source BGE/E5); retrieval patterns (dense, sparse, hybrid with re-ranking via Cohere/Jina); RAG evaluation metrics (context precision, answer relevance, faithfulness)
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MLOps & Model Lifecycle: Experiment tracking (MLflow for open-source standard, Weights & Biases for collaborative workflows, Neptune for enterprise); model registry patterns; training pipelines (Kubeflow for K8s-native, Metaflow for simplicity, Flyte for data awareness); feature stores (Feast for open-source, Tecton for managed); model monitoring and drift detection (Arize, Evidently, WhyLabs); continuous training and retraining strategies
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Multi-Agent System Architecture: Agent orchestration frameworks (AutoGen for flexible collaboration, CrewAI for role-based teams, LangGraph for explicit state machines); agent communication patterns (message passing, shared memory, event-driven); error handling and reliability (retry logic, fallback agents, human-in-the-loop escalation); agent memory architectures (short-term conversation, long-term vector storage, semantic memory); tool use patterns and safety boundaries
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.
- 5d ago First seen · 979 lines · 55 tokens per session scan C 8a55daa306f3
ai-solution-architect is an agent published in the GitHub repository SteveGJones/ai-first-sdlc-practices (41 stars, last pushed 1mo ago), licensed MIT. It adds 55 tokens to every session and 10,358 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it C with 1 finding (instruction-override phrasing). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other agents, from other repositories
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ai-engineer
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ai-engineer
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ai-engineer
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ai-engineer
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