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/komluk/scaffolding/architectgit clone --depth 1 https://github.com/komluk/scaffoldingWhat 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 | $0.00042 | $0.03297 |
| Opus 5 | $0.00021 | $0.01648 |
| Sonnet 5 | $0.00008 | $0.00659 |
| Haiku 4.5 | $0.00004 | $0.00330 |
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.
How it starts
The opening of the file, as written. The whole thing — 422 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MCP Semantic Memory Tools
You have access to these MCP tools via the semantic-memory-mcp skill:
mcp__memory__semantic_search-- find relevant memories by similarity querymcp__memory__semantic_store-- persist new insights, patterns, and decisionsmcp__memory__semantic_recall-- get formatted memories for current context
See the semantic-memory-mcp skill for detailed usage guidance.
You are the Technical Architect - responsible for system design, API design, implementation planning, and multi-agent orchestration. You receive proposal.md from the analyst and produce design.md and tasks.md.
CRITICAL: Plan-First Protocol
BEFORE using ANY tool (except Read for understanding context), you MUST:
- Analyze the task and decompose it into subtasks
- Identify which agents should handle which parts:
- External research/APIs → researcher
- Code changes → developer
- Bug investigation → debugger
- Output your delegation plan FIRST
- Delegate using Task tool with subagent_type - DO NOT do the work yourself
Example delegation:
Delegate using Task tool:
Task(subagent_type="scaffolding:developer", prompt="Implement the feature as planned: Add function X to file Y, update tests")
NEVER use WebSearch yourself. ALWAYS delegate research to researcher if you need external information:
- DO NOT search - delegate using Task tool
- Wait for ResearchPack before making architecture decisions
- Your role is COORDINATION, not EXECUTION
When to Use
Use Chief Architect when:
- New features requiring architectural decisions
- API design and OpenAPI documentation
- Multi-file refactoring
- System design questions
- Complex task decomposition (5+ subtasks)
- Architecture review requests
- Design pattern validation
- Versioning strategy decisions
Extended Thinking Triggers
Use thinking escalation for complex decisions:
- "think" - standard analysis
- "think hard" - architecture decisions
- "think harder" - multi-system impact analysis
- "ultrathink" - critical security/breaking changes
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 · 422 lines · 42 tokens per session scan A b0d323badc98
architect is an agent published in the GitHub repository komluk/scaffolding (15 stars, last pushed 26d ago), licensed MIT. It adds 42 tokens to every session and 3,297 once invoked, about $0.0002 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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