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 skills/ozzy-labs/suasor/draftnpx skills add ozzy-labs/suasor --skill draftgit clone --depth 1 https://github.com/ozzy-labs/suasorWrote 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/ozzy-labs/suasor/draft)<a href="https://agentmods.dev/skills/ozzy-labs/suasor/draft"><img src="https://agentmods.dev/badge/skills/ozzy-labs/suasor/draft.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 | $0.00102 | $0.01061 |
| Opus 5 | $0.00051 | $0.00531 |
| Sonnet 5 | $0.00020 | $0.00212 |
| Haiku 4.5 | $0.00010 | $0.00106 |
Grade A, and why
draft 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 3d 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
draft
read-only な下書きの単一入口(ADR-0046)。kind で何を書くかを決める。text を返すだけで persist も外部投稿もしない。
以前は announcement-draft と handoff-draft に分かれていたが、どちらも「取り込み済みの材料を集めてホスト LLM が文章を組む」で、違うのは集める材料と tone だけだった。
write(HITL)系の下書きは統合対象外 — 返信下書きは reply-draft、計画は plan-draft。read と write を 1 本にすると承認境界が壊れる(ADR-0004 / ADR-0046 決定 1)。
いつ発火するか
| 言いかた | kind |
|---|---|
| 「リリース告知文書いて」「announcement 作って」「release notes 草案」「お知らせ案ほしい」 | announcement |
| 「引き継ぎ書作って」「handoff 書く」「後任向け資料まとめて」「業務引継メモほしい」 | handoff |
返信ではない「リリースノートの下書き作って」の類もここが受ける。特定メッセージへの返信の下書きだけが reply-draft(HITL)。
何をするか(MCP tool flow)
すべて read tool(ADR-0004)。
kind=announcement
decision.list(recordedAfter= 前回リリース以降)で「決まったこと」を引くsearchで関連する変更 context を補うbriefで対象期間の動きを取る- ホスト LLM が告知 tone(読み手は社外・他チーム。内輪の未完事項は出さない)で text を構成する
kind=handoff
task.list(state=in_progress)で進行中の作業を引くdecision.listでその領域の決定を引くgraph.related/searchで各作業の背景 source を辿る- ホスト LLM が引き継ぎ tone(読み手は後任。前提・未決事項・連絡先を明示)で text を構成する
制約
- text-only。persist しない・propose 経路を持たない・外部 SaaS に投稿しない(ユーザーが手で貼る)
- 文章の生成はホスト LLM 側。本 skill は材料の集め方のみを定める(ADR-0006)
- 材料が薄いときは「材料が足りない」と言う。埋めるために推測で書かない
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.
- 3d ago First seen · 63 lines · 102 tokens per session scan A b4cd5b82e630
draft is a skill published in the GitHub repository ozzy-labs/suasor (0 stars, last pushed 24d ago), licensed MIT. It adds 102 tokens to every session and 1,061 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.
Other skills, from other repositories
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smoke-test
Use when interactively verifying the MTG MCP server works end-to-end by calling its MCP tools against real APIs.
new-provider
Scaffold a new backend provider following project patterns (service + provider + tests).
capture-fixture
Capture a live API response as a JSON test fixture for a given service and endpoint.
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.