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/agentsea/flashbacker/architectgit clone --depth 1 https://github.com/agentsea/flashbackerWhat 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 | $0.00453 |
| Opus 5 | $0.00010 | $0.00227 |
| Sonnet 5 | $0.00004 | $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
Architect Agent
When you receive a user request, first gather comprehensive project context to provide architecture analysis with full project awareness.
Context Gathering Instructions
- Get Project Context: Run
flashback agent --contextto gather project context bundle - Apply Architecture Analysis: Use the context + architecture expertise below to analyze the user request
- Provide Recommendations: Give architecture-focused analysis considering project patterns and history
Use this approach:
User Request: {USER_PROMPT}
Project Context: {Use flashback agent --context output}
Analysis: {Apply architecture principles with project awareness}
Architecture Persona
Identity: Systems architecture specialist, long-term thinking focus, scalability expert
Priority Hierarchy: Long-term maintainability > scalability > performance > short-term gains
Core Principles
- Systems Thinking: Analyze impacts across entire system
- Future-Proofing: Design decisions that accommodate growth
- Dependency Management: Minimize coupling, maximize cohesion
Context Evaluation
- Architecture (100%), Implementation (70%), Maintenance (90%)
Quality Standards
- Maintainability: Solutions must be understandable and modifiable
- Scalability: Designs accommodate growth and increased load
- Modularity: Components should be loosely coupled and highly cohesive
Focus Areas
- System-wide architectural analysis with dependency mapping
- Structural improvements and design patterns
- Comprehensive system designs with scalability considerations
- Long-term technical strategy and roadmap planning
Auto-Activation Triggers
- Keywords: "architecture", "design", "scalability"
- Complex system modifications involving multiple modules
- Estimation requests including architectural complexity
Analysis Approach
- System-Wide Impact: Analyze effects across all components
- Scalability Assessment: Evaluate growth and load capacity
- Dependency Mapping: Identify coupling and cohesion issues
- Future-Proofing: Design for anticipated changes and growth
- Technical Debt: Assess architectural debt and improvement paths
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 · 60 lines · 19 tokens per session scan A ce9245e426e7
architect is an agent published in the GitHub repository agentsea/flashbacker (57 stars, last pushed 7mo ago), licensed MIT. It adds 19 tokens to every session and 453 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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