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 humanerd-drew/opencode-drewgent --skill im-not-aigit clone --depth 1 https://github.com/humanerd-drew/opencode-drewgentWrote 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/humanerd-drew/opencode-drewgent/im-not-ai)<a href="https://agentmods.dev/skills/humanerd-drew/opencode-drewgent/im-not-ai"><img src="https://agentmods.dev/badge/skills/humanerd-drew/opencode-drewgent/im-not-ai.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 | $0.00109 | $0.00794 |
| Opus 5 | $0.00055 | $0.00397 |
| Sonnet 5 | $0.00022 | $0.00159 |
| Haiku 4.5 | $0.00011 | $0.00079 |
Grade A, and why
im-not-ai 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
im-not-ai — 한글 AI 티 제거기
반드시 읽을 것: 이 스킬은 WordPress 한글 콘텐츠 생성 전에 항상 로드한다 (writing-style-guide.md §8).
AI가 쓴 한국어 텍스트의 AI 티를 탐지·제거한다. 내용은 건드리지 않고 문체·리듬·표현만 자연스럽게 바꾼다.
분류 체계 (10대 카테고리)
| ID | 대분류 | 예시 |
|---|---|---|
| A | 번역투 | ~를 통해, ~에 대해, ~에 있어서, 이중 피동 |
| B | 영어 인용·용어 과다 | 괄호 병기, 번역 가능한 영어 그대로 |
| C | 구조적 AI 패턴 | 첫째/둘째/셋째, 불릿·헤딩 과다, 연결어미 뒤 쉼표 |
| D | AI 특유 관용구 | 결론적으로, 시사하는 바가 크다, 주목할 만하다 |
| E | 리듬 균일성 | 문장 길이 표준편차 낮음, 종결어미 반복 |
| F | 수식·중복 | 매우, 정말, ~적/~성/~화 |
| G | Hedging | ~할 수 있을 것으로 보인다 |
| H | 접속사 남발 | 또한/따라서/즉/나아가 문두 연속 |
| I | 형식명사 과다 | 것, 점, 수, 바, ~할 필요가 있다 |
| J | 시각 장식 남용 | 과도한 볼드/따옴표/대시 |
철칙
- 의미 불변 — 사실·주장·수치·고유명사·인용문 100% 보존
- 근거 기반 — 분류 체계에 없는 구간은 건드리지 않음
- 과윤문 금지 — 변경률 30% 초과 시 경고, 50% 초과 시 롤백
사용법
텍스트를 붙여넣고 요청:
- "AI 티 없애줘"
- "번역투 고쳐줘"
- "사람이 쓴 것처럼 윤문해줘"
참고 파일
references/quick-rules.md— 슬림 룰북 (S1/S2 핵심 패턴)references/ai-tell-taxonomy.md— 분류 체계 본진 (60+ 패턴)references/rewriting-playbook.md— 카테고리별 처방references/scholarship.md— 학술 인용 SSOT
What ships with it
9 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/ai-tell-taxonomy.md 59 KB
- references/baseline_v2.json 13 KB
- references/baseline.json 6.0 KB
- references/metrics_v2.py 27 KB runs code
- references/metrics.py 14 KB runs code
- references/quick-rules.md 8.9 KB
- references/rewriting-playbook.md 11 KB
- references/scholarship.md 24 KB
- references/web-service-spec.md 7.8 KB
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 · 53 lines · 109 tokens per session scan A 35a5323adc13
im-not-ai is a skill published in the GitHub repository humanerd-drew/opencode-drewgent (2 stars, last pushed 1mo ago), licensed MIT. It adds 109 tokens to every session and 794 once invoked, about $0.0005 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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