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/shdsjh123-cpu/claude-code-blog-builder/setup-domaingit clone --depth 1 https://github.com/shdsjh123-cpu/claude-code-blog-builderWrote 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/shdsjh123-cpu/claude-code-blog-builder/setup-domain)<a href="https://agentmods.dev/commands/shdsjh123-cpu/claude-code-blog-builder/setup-domain"><img src="https://agentmods.dev/badge/commands/shdsjh123-cpu/claude-code-blog-builder/setup-domain.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 | $0.00035 | $0.00644 |
| Opus 5 | $0.00017 | $0.00322 |
| Sonnet 5 | $0.00007 | $0.00129 |
| Haiku 4.5 | $0.00003 | $0.00064 |
Grade A, and why
setup-domain 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
/setup-domain — 도메인 특화 설정 (Phase 3)
여러분의 주력 카테고리에 맞는 키워드뱅크, 업계 벤치마크, 산업별 금칙어, 이미지 디자인 시스템을 설정합니다.
사전 조건
/setup완료- 가급적
/setup-tone도 완료 (필수는 아님)
실행 절차
setup-interviewer서브에이전트 호출 (Phase 3 모드).knowledge/brand-facts.md에서 주력 카테고리 1~3개 읽기.- 카테고리별로 순회 (각 카테고리당 3질문, 약 5분):
- Q1: "이 카테고리의 주요 키워드 5~10개?" →
keyword-bank/{slug}.yml생성 - Q2: "이 카테고리에 적용되는 법령/규제 단어가 있나요?" →
knowledge/banned-words.jsondomain_specific.words추가 - Q3: "이 카테고리의 업계 벤치마크 수치 아시는 것?" →
knowledge/conversion-benchmarks.md업데이트
- Q1: "이 카테고리의 주요 키워드 5~10개?" →
- 이미지 디자인 시스템 (선택, 5분):
- 브랜드 컬러 3개 (배경 / 메인 / 포인트, hex)
- 폰트 스타일 (산세리프 / 세리프 / 혼합)
- 로고 텍스트 (이미지에 박힐 정확한 표기 — 알파벳 대소문자 정확히)
- →
scripts/generate-images.js의 BRAND_NAME, BG_COLOR, ACCENT_COLOR 상수 자동 치환
- medical-law-checker 활성화 여부: 의료/뷰티/제약 카테고리 있으면 활성화 권장
출력물
keyword-bank/<카테고리1>.ymlkeyword-bank/<카테고리2>.ymlkeyword-bank/<카테고리3>.ymlknowledge/banned-words.json(도메인 단어 추가됨)knowledge/conversion-benchmarks.md(벤치마크 채워짐)scripts/generate-images.js(브랜드 시스템 치환됨)
완료 후 안내
✅ Phase 3 완료 — 도메인 특화 설정 끝났습니다.
이제 다음 명령으로 첫 글을 쓸 수 있어요:
/blog-new "키워드"
추천 시작 키워드: keyword-bank/<카테고리>.yml의 시드 키워드 중 하나
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 · 49 lines · 35 tokens per session scan A 6ab1f3b8af26
setup-domain is a command published in the GitHub repository shdsjh123-cpu/claude-code-blog-builder (21 stars, last pushed 5mo ago), licensed MIT. It adds 35 tokens to every session and 644 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.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
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.
constitution
Create or update the project constitution from interactive or provided principle inputs.
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.