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/codenamev/ai-software-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/codenamev/ai-software-architect/ai-engineer)<a href="https://agentmods.dev/agents/codenamev/ai-software-architect/ai-engineer"><img src="https://agentmods.dev/badge/agents/codenamev/ai-software-architect/ai-engineer/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/codenamev/ai-software-architect/ai-engineer"><img src="https://agentmods.dev/badge/agents/codenamev/ai-software-architect/ai-engineer.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 | $0.00044 | $0.00569 |
| Opus 5.5 | $0.00018 | $0.00228 |
| Sonnet 5.5 | $0.00009 | $0.00114 |
| Haiku 4.5 | $0.00004 | $0.00057 |
Grade A, and why
ai-engineer 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 8d 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.
The source is not reproduced here
No licence file
A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.
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.
- 8d ago Changed · +21 lines f32d2fc32322
- 1mo ago First seen · 50 lines · 44 tokens per session scan A cd551afeb275
ai-engineer is an agent published in the GitHub repository codenamev/ai-software-architect (79 stars, last pushed yesterday), with no licence file. It adds 44 tokens to every session and 569 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.
Other agents, from other repositories
decision-checker
Use to check a plan, diff, or set of changed paths against the architecture decisions that govern them, and get a per-decision verdict. Read-only; never writes a record.
prompt-engineer
Prompt & guardrail engineering specialist. MUST BE USED for system-prompt design, prompt templates, prompt eval/test suites, prompt-injection defense, and LLM-judge rubrics. PROACTIVELY treats prompts as versioned, test-covered, injection-resistant contracts.
pixeltable-pipeline-architect
Designs Pixeltable TableModel schemas. Tables, views/iterators, computed columns, embedding indexes, and UDFs. Use when the user needs to model a data/AI workflow or decide between a view, a computed column, and a UDF.
prompt-engineer
Optimizes prompts for LLMs and AI systems. Use when building AI features, improving agent performance, or crafting system prompts. Expert in prompt patterns and techniques, including synthetic test data generation.
code-reviewer
Reviewer of finished code (not of proposals — for that, use @architect). Reads the diff/modified files and points out bugs, violated patterns, missing tests, poor naming, ADRs not followed in code. Use before creating a PR, before claiming "feature done", or when you suspect quality issues.
perf-auditor
Performance auditor. Analyzes code for bottlenecks, hot paths, N+1, memory leaks, missing cache, sync I/O in an async path. Use when latency/throughput misses the PRD target, before a release, or when planning an optimization. Don't confuse with @code-reviewer (which is shallow on perf).