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/epicsagas/toefl-prepWrote 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/epicsagas/toefl-prep/toefl-status)<a href="https://agentmods.dev/commands/epicsagas/toefl-prep/toefl-status"><img src="https://agentmods.dev/badge/commands/epicsagas/toefl-prep/toefl-status.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 | $0.00055 | $0.00816 |
| Opus 5 | $0.00028 | $0.00408 |
| Sonnet 5 | $0.00011 | $0.00163 |
| Haiku 4.5 | $0.00006 | $0.00082 |
Grade A, and why
toefl-status scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
/toefl-status — 학습 현황 대시보드
볼트 데이터 + schedule.yaml을 읽어 진행 상황을 요약한다. LLM 채점을 수행하지 않는다(읽기 전용).
데이터 소스
DATA="${TOEFL_DATA_DIR:-$HOME/Documents/toefl-prep}"
JOURNAL="$DATA/JOURNAL.md"
SCORES="$DATA/SCORES.md"
PROGRESS="$DATA/PROGRESS.md"
PLUGIN=~/.claude/plugins/marketplaces/toefl
출력 항목
- 시험일 카운트다운 + 목표 (schedule.yaml 기반 — 하드코딩 없음):
D-day와 목표 점수는"$PLUGIN/scripts/roadmap.sh" dday # D-day to test window "$PLUGIN/scripts/roadmap.sh" json | jq -r '.target_score, .week'roadmap.sh에서 schedule.yaml 값을 그대로 사용 — 하드코딩 금지. - 현재 주차 + 진행도:
roadmap.sh week로 주차,JOURNAL.md에서 이번 주 학습일 수 카운트. - 영역별 최근 점수 + 추이 (
SCORES.md파싱):- 각 영역 최근 3회 점수를 0–30 환산.
- 추이 화살표: 상승 ↑ / 하락 ↓ / 정지 →.
- 목표 대비 갭: 4영역 합산 추정 점수 vs
schedule.yaml의target_score.추정: R22 + L18 + S20 + W22 = 82 / 목표 <target_score> → 갭 - 최상 약점 2개: 최근 채점에서 가장 낮은 dimension + 빈도 높은 오류 유형.
- 다음 추천 액션: 주차 + 약점 기반으로
/toefl-drill,/toefl-practice안내.
빈 데이터 처리
SCORES.md에 데이터가 없으면: "아직 채점 기록 없음 —/toefl-practice후/toefl-grade로 베이스라인 측정을 시작하세요."- 거짓 데이터 생성 금지. 누락 영역은 "—"로 표시.
출력 형식 (예시 — 날짜/점수는 schedule.yaml 기반)
📊 TOEFL Status | <today> | D-<N> to test
Week <n> — 진행 <m>일
영역별 추정 (최근 점수, 0–30):
Reading 24 ↑ (was 20)
Listening 19 ↑ (was 16)
Speaking 18 → (전사 기반 추정)
Writing 22 ↑ (was 19)
─────────────────────
합산 추정 83 / 목표 <target_score> → <갭>
최상 약점: Listening detail-dropped (3회 연속)
추천: /toefl-drill detail-dropped
정직성 원칙
- 추정 점수는 로컬 모델 기반이라 "추정" 표기를 항상 붙인다.
- 데이터가 부족해 통계가 무의미하면(예: 영역당 1회) "샘플 부족, 추이 참고용" 명시.
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 · 69 lines · 55 tokens per session scan A 4fd0f274cfe5
toefl-status is a command published in the GitHub repository epicsagas/toefl-prep (1 stars, last pushed 4d ago), licensed MIT. It adds 55 tokens to every session and 816 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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