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.
git clone --depth 1 https://github.com/jed1978/ddd-architecture-coachWrote 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/jed1978/ddd-architecture-coach/arch-learn)<a href="https://agentmods.dev/commands/jed1978/ddd-architecture-coach/arch-learn"><img src="https://agentmods.dev/badge/commands/jed1978/ddd-architecture-coach/arch-learn.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 | $0.00037 | $0.00234 |
| Opus 5 | $0.00018 | $0.00117 |
| Sonnet 5 | $0.00007 | $0.00047 |
| Haiku 4.5 | $0.00004 | $0.00023 |
Grade A, and why
arch-learn 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 6d 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
請啟動 DDD Architecture Coach（ddd-architecture-coach skill）並寫入一條 learning。
執行步驟：
- 把 $ARGUMENTS 視為使用者要記錄的 learning 內容
- 讀取
.claude/arch-learnings.md（若不存在，依 SKILL.md Memory/State/Learnings 三層分工原則建立） - 判斷
applies_to範圍：- 內容明顯為個人偏好（跨專案）→ 提醒使用者應寫入 Claude Code memory，不要寫入 arch-learnings
- 本專案 phase / BC 級規範 → append 到 arch-learnings.md，source:
user_triggered
- 確認後寫入並回報新 learning 的位置
$ARGUMENTS
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.
- 6d ago First seen · 16 lines · 37 tokens per session scan A 07f370a47a2e
arch-learn is a command published in the GitHub repository jed1978/ddd-architecture-coach (5 stars, last pushed 4mo ago), licensed MIT. It adds 37 tokens to every session and 234 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 2026-08-31.
Other commands, from other repositories
setup-context
Set up your Skill Memory — a pm-context.md every skill reads so outputs come back tailored to you.
brain
Set up or use your Professional Brain — init, ingest, recall, or review.
tenets-scaffold
You are initializing the architecture foundation for the Flask service in the current repository. This workflow supports one service only. It does not generate individual bounded contexts, workflows, features, or resources.
session-learn
Extract knowledge from the current session. Invoke with /session-learn or "extract this session".
record
Record a new architectural decision into project memory.
check
Run mneme check against a file or proposed change.