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 s0912758806p/agentic-sop-to-work --skill agentic-sopgit clone --depth 1 https://github.com/s0912758806p/agentic-sop-to-workWrote 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/s0912758806p/agentic-sop-to-work/agentic-sop)<a href="https://agentmods.dev/skills/s0912758806p/agentic-sop-to-work/agentic-sop"><img src="https://agentmods.dev/badge/skills/s0912758806p/agentic-sop-to-work/agentic-sop/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/skills/s0912758806p/agentic-sop-to-work/agentic-sop"><img src="https://agentmods.dev/badge/skills/s0912758806p/agentic-sop-to-work/agentic-sop.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 | $0.00127 | $0.02076 |
| Opus 5 | $0.00063 | $0.01038 |
| Sonnet 5 | $0.00025 | $0.00415 |
| Haiku 4.5 | $0.00013 | $0.00208 |
Grade A, and why
agentic-sop 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 2d 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.
How it starts
The opening of the file, as written. The whole thing — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agentic SOP — 把人工流程做成可治理的 agent 工作流
何時用 / 不用
- 用：要把人工 SOP／手動流程自動化、建新 agent 工具、設計「拆解＋閘門」、或開新的 agent 工作流專案時。任何專案皆適用，尤其未來新項目。
- 不用：稽核既有工作流是否退化成 mega agent → 用
agentic-workflow-audit；執行既有 GMP 產生器 → 用該工具自己的 skill。
開場：先判斷輸入型態（intake 分流）
觸發後第一件事——看使用者怎麼來，別急著動工：
- 已有正式輸入（寫好的 spec／既有 requirement 或 runner skill／已填好的
templates/human_sop_template.md）→ 照原樣採用、不重寫，直接進下面的拆解。 - 只有自然語言需求/任務 → 先用
templates/human_sop_template.md起草一份 Human SOP：能由輸入得到的填上、未知的標【待補】（絕不臆造）；草稿好 STOP 給人看一眼（「這樣有抓到你的流程嗎？改／確認」）→ 確認後才續入拆解。 - 判不準 → 問一個澄清問題，別猜路。 草稿 SOP 也是 DRAFT（受「DRAFT＋人核准」約束）；本步只動生成層，不碰引擎/閘門。
核心模型（三段鏈，缺一不可）
Human SOP → 工具 Skill（SKILL.md） → Agentic Workflow（拓撲宣告 + 誠實閘門 + hook）。 中間的 Skill 環最易被忘：工具一改，SKILL.md 必須同次更新。每支工具實作同一條七階段迴圈： intake → 分類/前置檢查 → 確定性層(程式) → 生成層(Claude，只整理輸入) → 組裝 DRAFT → 閘門自評(≤2 次) → 覆核包 → 人核准 STOP。
拆解完，先畫拓撲（動手接線之前）
拆出節點之後、寫 flow.json 之前，先把圖講清楚——這一步只花幾分鐘，卻是後面所有閘門的地基：
- 節點：列出每個節點（＝一個工具的一步）。 節點不是 agent：把工具換成模型的節點常被叫做 agent，但一 skill 一工具不變； 一個節點裡塞了整條流程就是 mega agent，名字叫什麼都一樣。
- 邊：每條邊取一個名字——「誰把什麼交給誰」。交接的是具名產物，不是「整包 context 丟過去」。
接著給它型別（
schema_ref指向workflow/schemas/<tag>.json）：邊沒有型別，交接協定就是假的。 - 狀態欄位與 owner：列出流程要共用的欄位，每個欄位指定唯一一個 writer 節點；
讀取方宣告
reads。值不另外存——留在該節點的 artifact 裡（written_by就是produced_by）。 - 退回邊：哪一步的判定會把工作退回上游？那條邊宣告
back:true＋max_revisits:<N>。 沒有上界的環不准存在；退回的判定必須由確定性的欄位決定，不是由模型當場決定。 - 驗證：
python3 workflow/run.py --plan會把整張圖靜態判過（不可達節點、read-before-write、 寫入衝突、無界環、孤邊），不合法在跑之前就 exit 2；--graph畫出來給人看。
五題有任一題答不出來，就是還沒拆完——別急著寫 flow.json。 若流程本來就是一條直線、也沒有退回，那就照直線做：以上宣告全是選擇性的，不宣告即維持線性行為。
落地：導入 agentic-sop-kit（不要重造）
方法論已封裝成可攜套件，直接導入、別重寫：
- 一鍵導入：
python3 ~/.claude/agentic-sop-kit/bootstrap.py --project /path/to/project（複製 kit＋裝/sop-flow＋合併 Stop-hook）。canonical 在~/.claude/agentic-sop-kit/。 python3 agentic-sop-kit/check_deps.py驗依賴；workflow/run.py跑通範例。- 用
templates/human_sop_template.md寫 SOP（每步標工具）；依拆解規則把每個「步驟×工具」用templates/skill_template/建成skills/<name>/。 - 依上一節的拓撲在
workflow/flow.json接線（節點順序、schema_ref、reads/writes、退回邊）；--plan驗過再跑；裝commands/+hooks/。 詳見 kit 的SOP.md（方法論 canonical）與README.md（安裝）；圖示範流程見workflow/examples/graph.json。
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.
- 2d ago Changed · +26 lines 11f448c72692
- 11d ago First seen · 40 lines · 127 tokens per session scan A 48d3a1581f55
agentic-sop is a skill published in the GitHub repository s0912758806p/agentic-sop-to-work (206 stars, last pushed 2d ago), licensed MIT. It adds 127 tokens to every session and 2,076 once invoked, about $0.0006 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.
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