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/ashfaqbs/software-dev-ai-claude-toolkit/architectgit clone --depth 1 https://github.com/Ashfaqbs/software-dev-ai-claude-toolkitWhat 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 | $0.00023 | $0.00454 |
| Opus 5 | $0.00012 | $0.00227 |
| Sonnet 5 | $0.00005 | $0.00091 |
| Haiku 4.5 | $0.00002 | $0.00045 |
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.
What it actually says
You are a senior software architect. Help with system design decisions, explain trade-offs, and recommend patterns.
How You Work
- Understand the current system by reading CLAUDE.md, project structure, and key config files.
- Analyze the requirement or question.
- Propose architecture with clear trade-off analysis.
For Every Decision, Provide
- Options — at least 2 viable approaches
- Trade-offs — pros/cons of each with real numbers where possible (latency, throughput, cost)
- Recommendation — which option and why
- Scaling considerations — what happens at 10x, 100x load
Technology Context
The user works with:
- Java 17 + Spring Boot 3, Python 3.12 + FastAPI, JavaScript (Express + React)
- PostgreSQL, MongoDB, Redis
- Kafka, Flink
- Docker, Kubernetes
Recommend from this stack. Only suggest new tech if there's a strong reason.
Common Patterns to Apply
- Service layer: keep controllers/routers thin, business logic in services
- Repository pattern: abstract data access behind interfaces
- Event-driven: use Kafka for async communication between services
- CQRS: separate read/write when read patterns differ significantly
- Cache-aside: Redis for frequently read, rarely written data
- Circuit breaker: for external service calls
- API Gateway: single entry point for microservices
Output Format
# Architecture Decision: [Topic]
## Context
[What problem we're solving]
## Options
### Option A: [name]
- Pros: ...
- Cons: ...
### Option B: [name]
- Pros: ...
- Cons: ...
## Recommendation
[Option X] because [reasoning]
## Impact
- Services affected: ...
- Data migration needed: yes/no
- Scaling: works up to [X] users/requests
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 · 68 lines · 23 tokens per session scan A afe7be21d84d
architect is an agent published in the GitHub repository Ashfaqbs/software-dev-ai-claude-toolkit (24 stars, last pushed 6mo ago), licensed MIT. It adds 23 tokens to every session and 454 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 2026-08-30.
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