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 skills add sngchn/kbo-analyst --skill kbo-data-opsgit clone --depth 1 https://github.com/sngchn/kbo-analystWrote 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/skills/sngchn/kbo-analyst/kbo-data-ops)<a href="https://agentmods.dev/skills/sngchn/kbo-analyst/kbo-data-ops"><img src="https://agentmods.dev/badge/skills/sngchn/kbo-analyst/kbo-data-ops.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 | $0.00060 | $0.00558 |
| Opus 5 | $0.00030 | $0.00279 |
| Sonnet 5 | $0.00012 | $0.00112 |
| Haiku 4.5 | $0.00006 | $0.00056 |
Grade A, and why
kbo-data-ops 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 7d 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
KBO Data Ops
신선도 정책
- 현재 시즌 질문엔
auto_collect=true를 넘긴다. 네이버 일정과 로컬 DB를 diff(~0.5초)해서 15경기 이하 gap은 자동 보충한다. 전체 시즌 스크랩으로 블로킹하지 않는다. - 모든 응답의
freshness/note를 읽는다:note에 INCOMPLETE → 수치는 캐시된 경기만 커버. 누락 범위를 사용자에게 반드시 전달.note에 validation FAIL/UNKNOWN → 비율 지표 해석 전에 품질 경고 언급.freshness.status == "needs_collection"→ 캐시 기준으로 답하되 경고 붙이고 백필 제안.
kbo_database_status는 광범위/역사적/커버리지 불확실 질문에만. 매 선수 질문마다 부르지 않는다.- 수집 후에도 데이터가 없으면 해당 범위의 로컬/네이버 데이터가 없다고 말한다. DB에 없는 시즌의 공식 기록을 지어내지 않는다.
수집 규칙
- 작은 gap은 auto_collect가 처리 — 할 일 없음.
needs_collection백필은kbo_collect_range를 호출당 ≤7일 청크로, 청크 사이에 진행 상황을 보고하면서. 채팅에서kbo_collect_season으로 리그 한 시즌을 한 방에 돌리지 않는다.- 한 선수 질문이면
team="XX"(SK=SSG, HT=KIA, OB=두산)로 ~5배 빠르게. 단, 리그 집계는 전체 수집 전까지 편향된다고 경고. - 대형 백필(100경기+)은 터미널에서
python3 collector.py START END [--team=XX] [--workers=10]을 권한다.
패턴: "2026 시즌 데이터 다 받아줘"
kbo_database_status로 gap 크기 확인.kbo_collect_range≤7일 청크로 진행 상황을 알리며 수집.- 100경기 이상이면 터미널 실행 권장.
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.
- 7d ago First seen · 40 lines · 60 tokens per session scan A df125af29d9c
kbo-data-ops is a skill published in the GitHub repository sngchn/kbo-analyst (0 stars, last pushed 2mo ago), licensed MIT. It adds 60 tokens to every session and 558 once invoked, about $0.0003 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.
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