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 reportgit 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/report)<a href="https://agentmods.dev/skills/s0912758806p/agentic-sop-to-work/report"><img src="https://agentmods.dev/badge/skills/s0912758806p/agentic-sop-to-work/report/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/report"><img src="https://agentmods.dev/badge/skills/s0912758806p/agentic-sop-to-work/report.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 | $0.00040 | $0.00341 |
| Opus 5.5 | $0.00016 | $0.00136 |
| Sonnet 5.5 | $0.00008 | $0.00068 |
| Haiku 4.5 | $0.00004 | $0.00034 |
Grade A, and why
report 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 1mo 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
Skill C — report（產出 DRAFT 報告）
SOP 流程的第 3 步。讀上游 stats，輸出標示 DRAFT 的 Markdown 報告（含來源追溯區）。
綁定的單一工具
python3（標準庫）。本 skill 只此一個工具。
依賴（完整宣告；缺項明確報錯）
python>= 3.8
參數化（無硬編碼）
- 輸入：
--in <stats artifact.json> - 輸出：
--out <report.md>
介面
- 輸入 artifact：
stats@1（讀data.stats與trace）。 - 輸出：Markdown
.md，開頭標 DRAFT — 需人員覆核；附來源追溯區。
執行
python3 skills/report/tool.py --in <out/b.json> --out <out/report.md>
獨立重用
skills/report/ + lib/kit.py 可單獨抽出；只要上游給 stats@1 artifact 即可運作。產出永遠是 DRAFT，永不自動歸檔。
What ships with it
1 file 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.
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.
- 1mo ago First seen · 29 lines · 40 tokens per session scan A 715342c640a0
report is a skill published in the GitHub repository s0912758806p/agentic-sop-to-work (211 stars, last pushed 1mo ago), licensed MIT. It adds 40 tokens to every session and 341 once invoked, about $0.0002 per session on Opus 5.5. 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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