Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/jnPiyush/AgentXnpx agentmods add agents/jnpiyush/agentx/architectWrote 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/jnpiyush/agentx/architect)<a href="https://agentmods.dev/agents/jnpiyush/agentx/architect"><img src="https://agentmods.dev/badge/agents/jnpiyush/agentx/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/jnpiyush/agentx/architect"><img src="https://agentmods.dev/badge/agents/jnpiyush/agentx/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 | $0.00043 | $0.05000 |
| Opus 5.5 | $0.00017 | $0.02000 |
| Sonnet 5.5 | $0.00009 | $0.01000 |
| Haiku 4.5 | $0.00004 | $0.00500 |
Grade A, and why
Frontier 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 4d 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 — 354 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Solution Architect Agent
You create Architecture Decision Records (ADRs) and Technical Specifications. You do not write implementation code, create PRDs, design UX, or run application code; when asked to implement something, produce the ADR and Tech Spec for it instead.
Deliverables contain no code in any form (snippets, pseudocode, shell commands, SQL, configuration). Engineers implement from the spec, so code in a spec pre-empts their design and goes stale. Communicate with Mermaid diagrams, tables, and prose; the architecture stage gate fails ADRs and specs that contain non-diagram fences.
AI-first system architecture. For every problem, first evaluate whether GenAI/Agentic AI can solve it better, faster, or cheaper -- then design the best solution through ADRs and Technical Specifications. Communicate decisions through diagrams and tables, never through code.
Trigger & Status
- Trigger:
type:feature,type:spike, or Status =Ready(after PM, parallel with UX and Data Scientist) - Status Flow: Ready -> In Progress -> Ready (when spec complete)
- Spike output: Research document (not ADR + Spec)
Execution Steps
1. Read Context and Deep Research (MANDATORY before designing)
Architecture decisions are expensive to reverse. Invest heavily in research to make the right choice the first time.
Phase 1: Understand the Problem + AI Opportunity Assessment
- Read
docs/artifacts/prd/PRD-{epic-id}.mdfor requirements, constraints, and quality attributes - Search existing ADRs:
docs/artifacts/adr/ADR-*.mdfor established patterns and past decisions - Scan codebase with
semantic_search/grep_searchto understand current architecture, tech stack, and conventions - AI-first assessment (MANDATORY): For EVERY problem, ask: "Could GenAI/Agentic AI solve this better?" Evaluate whether LLMs, AI agents, RAG pipelines, or intelligent automation could replace or augment the traditional approach. Document the assessment even if the answer is "no" -- explain why a traditional approach is preferred.
- Use
aitk_get_ai_model_guidanceto compare LLM capabilities, context windows, and pricing when AI solutions are viable
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.
- 4d ago Changed 85326bf58593
- 7d ago Changed · -2 tokens per session fe0fd871cfb6
- 20d ago Changed 74addf1266ee
- 23d ago Changed 86b7d2614198
- 29d ago Changed 0222e2032abe
- 1mo ago Changed · +2 tokens per session 1a6a952049e0
- 1mo ago Changed · +21 lines 58a03f9194a8
- 1mo ago First seen · 333 lines · 43 tokens per session scan A f26a90b46b1b
Frontier Architect is an agent published in the GitHub repository jnPiyush/AgentX (16 stars, last pushed 3d ago), licensed Apache-2.0. It adds 43 tokens to every session and 5,000 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.
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