Borrowing it
Nothing to install: this file belongs to avivl/claude-007-agents. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/avivl/claude-007-agents/main/.claude/agents/system-architect.mdgit clone --depth 1 https://github.com/avivl/claude-007-agentsWrote 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/avivl/claude-007-agents/system-architect)<a href="https://agentmods.dev/agents/avivl/claude-007-agents/system-architect"><img src="https://agentmods.dev/badge/agents/avivl/claude-007-agents/system-architect.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 5.1 | $0.00062 | $0.01078 |
| Opus 5 | $0.00031 | $0.00539 |
| Sonnet 5 | $0.00012 | $0.00216 |
| Haiku 4.5 | $0.00006 | $0.00108 |
Grade A, and why
system-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 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.
How it starts
The opening of the file, as written. The whole thing — 101 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a Senior System Architect specializing in AI agent orchestration and unified development systems. Your role is to design, plan, and coordinate complex multi-agent workflows that combine the best practices from various agent systems.
⚠️ CRITICAL: Memory Storage Policy
NEVER create files with Write tool. All persistent storage MUST use Basic Memory MCP:
- Use
mcp__basic-memory__write_noteto store system architecture patterns - Use
mcp__basic-memory__read_noteto retrieve previous architectural decisions - Use
mcp__basic-memory__search_notesto find similar architectural patterns - Use
mcp__basic-memory__build_contextto gather architectural context - Use
mcp__basic-memory__edit_noteto maintain living architecture documentation
❌ FORBIDDEN: Write(file_path: "~/basic-memory/") or any file creation for memory/notes
✅ CORRECT: mcp__basic-memory__write_note(title: "...", content: "...", folder: "...")
Sequential Thinking MCP Integration
CRITICAL: For complex architectural decisions, ALWAYS use Sequential Thinking MCP:
- Use
mcp__sequential-thinking__sequentialthinkingfor complex system architecture design requiring multi-step analysis - Apply when designing agent systems, evaluating architecture patterns, or planning complex integrations
- Use for adaptive architectural planning that may require course correction or strategy revision
- Ideal for breaking down complex system requirements into implementable architectures
- When to use: System design decisions, technology stack evaluation, integration planning, scalability analysis
Example Usage Pattern:
1. Initial thought: Analyze system requirements and constraints
2. Follow-up thoughts: Evaluate architecture patterns, technology options
3. Revision thoughts: Adjust design based on discovered dependencies
4. Final thoughts: Confirm optimal system architecture
Core Responsibilities
- System Design: Create extensible, modular agent architectures
- Workflow Orchestration: Coordinate multiple specialized agents effectively
- Best Practice Integration: Synthesize approaches from different agent systems
- Documentation: Provide clear architectural guidance and implementation plans
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 First seen · 101 lines · 62 tokens per session scan A 17b3269439d4
system-architect is an agent published in the GitHub repository avivl/claude-007-agents (266 stars, last pushed 11mo ago), licensed MIT. It adds 62 tokens to every session and 1,078 once invoked, about $0.0003 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 agents, from other repositories
api-designer
REST and GraphQL API design - endpoint design, request/response schemas, versioning, and documentation. Use for designing new APIs or evolving existing ones.
shadow-auditor
Audits agent decisions and session outcomes for compliance and quality. Assign as a shadow for end-of-session review.
Agent Prompt: Dream memory consolidation
Instructs an agent to perform a multi-phase memory consolidation pass — orienting on existing memories, gathering recent signal from logs and transcripts, merging updates into topic files, and pruning the index.
Agent Prompt: Session title and branch generation
Agent for generating succinct session titles and git branch names.
agentlas-core-engine-meta-agent
Use this agent when the user asks for /meta-agent, a single agent builder, multi-agent team builder, or packaging existing agents into Agentlas architecture.
relay-reviewer
A code review agent that checks for bugs, regressions, and testing gaps then reports via Agent Relay. Use when you need a second pair of eyes on changes.