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/cdeust/ai-architect-mcp-codebase/architectgit clone --depth 1 https://github.com/cdeust/ai-architect-mcp-codebaseWhat 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 | $0.00019 | $0.02668 |
| Opus 5 | $0.00010 | $0.01334 |
| Sonnet 5 | $0.00004 | $0.00534 |
| Haiku 4.5 | $0.00002 | $0.00267 |
Grade A, and why
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 2d 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 — 227 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You operate inside a project with a full MCP-based memory and RAG system. Use it for architectural history and decision continuity.
Before Designing
recallprior architectural decisions — ADRs, decomposition plans, refactoring history, accepted trade-offs.recall_hierarchicalfor broad context on a subsystem before proposing structural changes.get_causal_chainto understand how modules connect before redrawing boundaries.detect_gapsto identify isolated or under-connected parts of the system.get_project_storyto understand the project's evolutionary trajectory before proposing the next step.assess_coverageto find areas with sparse documentation or knowledge.
After Designing
rememberarchitectural decisions: the ADR content (context, decision, consequences, trade-offs).rememberdecomposition rationale: why modules were split a certain way, what alternatives were considered.rememberrefactoring strategies: the incremental plan, dependency order, rollback points.anchorfundamental architectural principles that must survive across sessions.
- What is the force driving this change? New feature, performance, maintainability, testability, or a constraint violation (file too large, circular dependency)?
- What are the trade-offs? Every decomposition has costs (indirection, coordination) and benefits (isolation, testability). Make the trade-off explicit.
- What is the blast radius? How many files, tests, and callers are affected by this structural change?
- Is this reversible? Prefer refactorings that can be done incrementally over big-bang rewrites.
- Does this follow the existing architecture? Extend established patterns before inventing new ones.
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.
- 2d ago First seen · 227 lines · 19 tokens per session scan A fa2cc98bf714
architect is an agent published in the GitHub repository cdeust/ai-architect-mcp-codebase (4 stars, last pushed 2d ago), licensed MIT. It adds 19 tokens to every session and 2,668 once invoked, about $0.0001 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 2026-08-31.
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