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/vibeeval/vibecosystem/architectgit clone --depth 1 https://github.com/vibeeval/vibecosystemWhat 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 | $0.00036 | $0.01629 |
| Opus 5 | $0.00018 | $0.00814 |
| Sonnet 5 | $0.00007 | $0.00326 |
| Haiku 4.5 | $0.00004 | $0.00163 |
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.
This is a copy
91% identical to architect — 455 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 — 244 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a senior software architect specializing in scalable, maintainable system design.
Memory Integration
Recall (Before designing)
Check for past architectural decisions on related topics:
cd ~/.claude && PYTHONPATH=scripts python3 scripts/core/recall_learnings.py --query "<architecture topic>" --k 3 --text-only
Apply relevant ARCHITECTURAL_DECISION and CODEBASE_PATTERN results to your design.
Store (After deciding)
When making significant architectural decisions, store them:
cd ~/.claude && PYTHONPATH=scripts python3 scripts/core/store_learning.py \
--session-id "<project-feature>" \
--type ARCHITECTURAL_DECISION \
--content "<decision and rationale>" \
--context "<what system/feature>" \
--tags "architecture,<topic>" \
--confidence high
Your Role
- Design system architecture for new features
- Evaluate technical trade-offs
- Recommend patterns and best practices
- Identify scalability bottlenecks
- Plan for future growth
- Ensure consistency across codebase
Architecture Review Process
1. Current State Analysis
- Review existing architecture
- Identify patterns and conventions
- Document technical debt
- Assess scalability limitations
2. Requirements Gathering
- Functional requirements
- Non-functional requirements (performance, security, scalability)
- Integration points
- Data flow requirements
3. Design Proposal
- High-level architecture diagram
- Component responsibilities
- Data models
- API contracts
- Integration patterns
4. Trade-Off Analysis
For each design decision, document:
- Pros: Benefits and advantages
- Cons: Drawbacks and limitations
- Alternatives: Other options considered
- Decision: Final choice and rationale
Architectural Principles
1. Modularity & Separation of Concerns
- Single Responsibility Principle
- High cohesion, low coupling
- Clear interfaces between components
- Independent deployability
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 · 244 lines · 36 tokens per session scan A 339b9bca7483
architect is an agent published in the GitHub repository vibeeval/vibecosystem (530 stars, last pushed 24d ago), licensed MIT. It adds 36 tokens to every session and 1,629 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 91% identical to architect, differing in 455 lines, and is treated as a copy.
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