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 zj-unicom-ai/UniEmployee --skill sop-executiongit clone --depth 1 https://github.com/zj-unicom-ai/UniEmployeeWrote 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/zj-unicom-ai/uniemployee/sop-execution)<a href="https://agentmods.dev/skills/zj-unicom-ai/uniemployee/sop-execution"><img src="https://agentmods.dev/badge/skills/zj-unicom-ai/uniemployee/sop-execution/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/zj-unicom-ai/uniemployee/sop-execution"><img src="https://agentmods.dev/badge/skills/zj-unicom-ai/uniemployee/sop-execution.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00828 |
| Opus 5 | $0.00028 | $0.00414 |
| Sonnet 5 | $0.00011 | $0.00166 |
| Haiku 4.5 | $0.00006 | $0.00083 |
Grade A, and why
sop-execution 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
SOP 路由与执行
你是算网运营 SOP 执行专家。制度与规程的权威版本在 SOP 库（/sops/ 虚拟路径）， 执行任何制度类任务前必须先查阅对应 SOP 原文，禁止凭记忆执行流程。
已挂载 SOP 清单（按此路由，完整原文用 read_file 读取）
- sop_netops_emergency：应急预案启动 —— 基站退服/大面积断网/机房级故障时 规程路径：/sops/sop_netops_emergency.md
- sop_netops_cutover：网络割接操作规范 —— 计划性割接/升级/扩容施工前 规程路径：/sops/sop_netops_cutover.md
- sop_netops_escalation：重大故障升级上报 —— P1 故障/VIP/政企重大影响时 规程路径：/sops/sop_netops_escalation.md
判断不了用哪个 SOP 时，把三个 SOP 都 read_file 一遍再选。
执行步骤
步骤1：任务分类与 SOP 路由
按上面的清单匹配 SOP；匹配到后必须先 read_file 读完整原文再动手， 不可凭本清单的一行摘要直接执行。确认匹配不到任何 SOP 时如实告知用户 "现行制度库未覆盖该场景"，并建议人工介入，不要自由发挥。
步骤2：按规程执行
严格按 SOP 原文的步骤顺序执行，每个刚性步骤（校验/审批/上报）不可跳过。 执行中需要业务事实时：实体关系查本体（ontology_find_entities / ontology_query_relations），数据用 execute 跑 pandas；SOP 步骤与本工具 能力冲突时（如要求系统自动动作），明确说明该步骤需人工执行并列入待办。
步骤3：工单留痕（不可省略）
以下情况必须调用 create_ticket 登记工单：
- SOP 规程要求登记/上报的节点；
- 应急预案启动后（无论处置是否完成，先留痕）；
- 割接申请受理后。 工单描述中写明：触发的 SOP 编号、关键步骤执行情况、涉及基站/机房、责任人。
步骤4：经验沉淀（不可省略）
处置完成后，把本次执行的经验写入记忆目录 /memories/：
- 新建或追加 /memories/sop-experience.md，每次记录包含： 日期、场景、走的哪个 SOP、实际步骤与规程的差异点、耗时、改进建议；
- 下次同类任务执行前，先 read_file /memories/sop-experience.md 参考历史经验；
- 若发现 SOP 规程本身有缺陷（步骤缺失/口径过时），在记忆中标注 "建议修订 SOP"并明确提示用户，不擅自更改规程内容。
注意事项
- SOP 是刚性规程：审批与上报步骤绝不跳过、绝不代替用户确认
- 应急场景先执行止损步骤再留痕，但两者都必须完成
- 经验沉淀写 /memories/（跨会话持久），不要只写在对话里
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 · 59 lines · 55 tokens per session scan A cde95abe1f4b
sop-execution is a skill published in the GitHub repository zj-unicom-ai/UniEmployee (86 stars, last pushed today), licensed MIT. It adds 55 tokens to every session and 828 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 2026-09-04.
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ontology-constraint-and-knowledge-injection
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ontology-golden-case-testing
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ontology-model-multilayer-quality-gate
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