Octop is a self-hosted, multi-user AI assistant that runs multiple specialized agents and connects them to chat interfaces, tools, and external services. It is for individuals, families, and teams who want a locally operated assistant with shared experts and persistent capabilities. Catalogue add-ons extend its agent and assistant workflows.
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 TencentCloud/Octop --skill bo-process-optimizationgit clone --depth 1 https://github.com/TencentCloud/OctopWrote 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/tencentcloud/octop/bo-process-optimization)<a href="https://agentmods.dev/skills/tencentcloud/octop/bo-process-optimization"><img src="https://agentmods.dev/badge/skills/tencentcloud/octop/bo-process-optimization/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/tencentcloud/octop/bo-process-optimization"><img src="https://agentmods.dev/badge/skills/tencentcloud/octop/bo-process-optimization.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 | $0.00047 | $0.00814 |
| Opus 5.5 | $0.00019 | $0.00326 |
| Sonnet 5.5 | $0.00009 | $0.00163 |
| Haiku 4.5 | $0.00005 | $0.00081 |
Grade A, and why
bo-process-optimization 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.
What it actually says
流程优化
从已知等待、返工和交接现象提出可行改进。用户只要 N 条建议时，直接写 N 个编号，不要另写瓶颈清单；每条只用必要依据加一个具体动作，观察、候选解释与建议不混成现行事实。
候选和建议
已知小问题可直接支持小改进，不必先假定其他管理措施缺失。材料只给了某角色或环节，不说明它是唯一关口，也不说明其他环节无人检查。原因未证实时用条件式核实问题，避免肯定句藏在“可能原因”标题下。
流程先后本身不能证明问题最早何时被发现、下一角色何时获知、退回期间是否可见或有无跟踪状态。没有这些证据时，不把“登记后才发现”“接单后才知道”写成现状；如确实影响建议，只提出对应的核实问题。标题和建议理由也要逐项对照原材料，不能靠“可能瓶颈”标题把无依据的肯定句变成假设。
例（虚构）：“部分报修退回补照片；值班长审核。”可建议“根据已有缺照片退回，在提交入口增加照片填写提示”；可问“需核实审核阶段是否有等待积压”。不能说值班长是唯一关口、其他人不检查或没有状态记录。
已有授权内的小改进直接提出；更改审批、职责和控制由有相应权限的角色评估，采纳前沿用原规则。样本数和已知原因保留；小样本只描述该样本，不代替长期总体结论。短建议不另列瓶颈表、重复总结或完整试点治理。
衡量方案
只有需要验证具体判断时才加记录方案。为声称的每个指标写出公式与最少字段，字段在能观察该事件的环节取得：
| 指标 | 足够的记录 |
|---|---|
| 两端间总历时 | 同一事项的起点、终点及其定义。 |
| 实际处理或纯等待时长 | 对应状态每次开始、暂停/退出、继续及结束的事件记录；分段相加。 |
| 某原因退回比例 | 同范围全部事项、逐件是否因该原因退回及原因口径。 |
例（虚构）：收单到交单减法只能得到总历时。加一个开工时间，只能拆成收单到开工、开工到交单两个区间；后一区间仍可能含停工、等待或交付延迟，不能称为纯加工时间。若要纯加工时间，建议每单记录加工状态的开始、暂停/继续和结束事件。未知是否中断不能默认为连续加工。
例（虚构）：只有退回次数能统计总退回频度，不能据此算“缺签名退回比例”；还需要退回原因和同范围总件数。若仅建议提示卡，无需顺带声称它会省时或消除返工。
方案的录入角色、未来终点来源未知，仅该方案说明待确认。复杂比较或用户要求实施时再展开基线、试点、周期和成功标准；任何收益估算均需资料或明确假设。
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 First seen · 36 lines · 47 tokens per session scan A e04c60a47922
bo-process-optimization is a skill published in the GitHub repository TencentCloud/Octop (9,896 stars, last pushed yesterday), licensed MIT. It adds 47 tokens to every session and 814 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 2026-10-17.
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