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 commands/ashfaqbs/software-dev-ai-claude-toolkit/system-designgit clone --depth 1 https://github.com/Ashfaqbs/software-dev-ai-claude-toolkitWrote 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/commands/ashfaqbs/software-dev-ai-claude-toolkit/system-design)<a href="https://agentmods.dev/commands/ashfaqbs/software-dev-ai-claude-toolkit/system-design"><img src="https://agentmods.dev/badge/commands/ashfaqbs/software-dev-ai-claude-toolkit/system-design.svg" alt="Measured on agentmods" 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 | $0.00006 | $0.00377 |
| Opus 5 | $0.00003 | $0.00188 |
| Sonnet 5 | $0.00001 | $0.00075 |
| Haiku 4.5 | $0.00001 | $0.00038 |
Grade A, and why
system-design 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.
What it actually says
System Design
Help me think through the system design for: $ARGUMENTS
Framework
Walk through these areas step by step. Ask me clarifying questions at each step before moving on.
1. Requirements
- What are the functional requirements?
- What are the non-functional requirements (latency, throughput, availability, consistency)?
- What scale are we designing for? (users, requests/sec, data volume)
2. High-Level Architecture
- What are the main components/services?
- How do they communicate (sync REST/gRPC vs async Kafka/queues)?
- Draw the architecture using text diagrams.
3. Data Design
- What data needs to be stored?
- SQL vs NoSQL decisions for each entity (and why).
- Schema design, indexing strategy, partitioning/sharding if needed.
4. API Design
- Key endpoints between services and to clients.
- Authentication and authorization approach.
5. Scaling & Performance
- Where are the bottlenecks?
- Caching strategy (what, where, TTL, invalidation).
- Database read replicas, connection pooling, query optimization.
- Horizontal scaling approach.
6. Reliability
- Single points of failure and how to eliminate them.
- Failure modes and graceful degradation.
- Monitoring, alerting, circuit breakers.
Rules
- Explain trade-offs for every decision (e.g., "CP vs AP", "SQL vs NoSQL for this use case").
- Use real numbers when estimating (requests/sec, storage in GB, latency in ms).
- Reference technologies I know: Spring Boot, FastAPI, PostgreSQL, MongoDB, Redis, Kafka, Flink, Docker, K8s.
- This is a learning exercise — teach me the reasoning, not just the answer.
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 First seen · 48 lines · 6 tokens per session scan A 5dcf37454d0e
system-design is a command published in the GitHub repository Ashfaqbs/software-dev-ai-claude-toolkit (24 stars, last pushed 6mo ago), licensed MIT. It adds 6 tokens to every session and 377 once invoked, about $0.0000 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.
Other commands, from other repositories
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clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.