Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/shdsjh123-cpu/claude-code-blog-buildernpx agentmods add commands/shdsjh123-cpu/claude-code-blog-builder/blog-researchWrote 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/blog-research)<a href="https://agentmods.dev/commands/shdsjh123-cpu/claude-code-blog-builder/blog-research"><img src="https://agentmods.dev/badge/commands/shdsjh123-cpu/claude-code-blog-builder/blog-research/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/shdsjh123-cpu/claude-code-blog-builder/blog-research"><img src="https://agentmods.dev/badge/commands/shdsjh123-cpu/claude-code-blog-builder/blog-research.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00020 | $0.00259 |
| Opus 5 | $0.00010 | $0.00130 |
| Sonnet 5 | $0.00004 | $0.00052 |
| Haiku 4.5 | $0.00002 | $0.00026 |
Grade A, and why
blog-research scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
"$ARGUMENTS" 키워드의 네이버 리서치만 수행합니다.
-
scripts/research.js실행:set -a && . ./.env && set +a && node scripts/research.js --keyword "$ARGUMENTS" -
API 인증 실패 시 웹 검색(
WebSearch)으로 대체:- 해당 키워드로 네이버 블로그 상위 10개 제목
- 연관 롱테일 키워드 후보
- 경쟁도 체감치
-
분석 리포트를 사용자에게 제시:
- 경쟁도 평가 (높음/보통/낮음)
- 추천 롱테일 키워드 5~8개
- 글 작성 시 어떤 패턴(12종 중)이 적합할지 제안
- 피해야 할 표현 (이미 포화된 제목 유형)
글은 작성하지 않습니다. 사용자가 "좋아, 이 방향으로 써줘" 라고 하면 그때 /blog-new 로 진행.
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.
- 9d ago First seen · 25 lines · 20 tokens per session scan A e8170da536a3
blog-research is a command published in the GitHub repository shdsjh123-cpu/claude-code-blog-builder (22 stars, last pushed 5mo ago), licensed MIT. It adds 20 tokens to every session and 259 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.
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.
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.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.