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 agentmods add skills/tencentcloud/octop/cheatsheetnpx skills add TencentCloud/Octop --skill cheatsheetgit 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/cheatsheet)<a href="https://agentmods.dev/skills/tencentcloud/octop/cheatsheet"><img src="https://agentmods.dev/badge/skills/tencentcloud/octop/cheatsheet.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 5.1 | $0.00061 | $0.00682 |
| Opus 5 | $0.00030 | $0.00341 |
| Sonnet 5 | $0.00012 | $0.00136 |
| Haiku 4.5 | $0.00006 | $0.00068 |
Grade A, and why
cheatsheet 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 6d 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
AI 编程工具速查与技巧清单
一、主流工具能力矩阵（选型参考）
| 工具 | 形态 | 强项 | 适合场景 |
|---|---|---|---|
| Claude Code | 终端 agent | 自主执行、子代理、Hook、工程化 | 多步工程任务、自动化流程 |
| Cursor | 编辑器内 AI | 代码库理解、Tab 补全、多文件编辑 | 日常编码、重构 |
| Codex / Copilot CLI | 云端 agent | 一次性补丁、命令行修复 | 补丁、脚本生成 |
| Aider | 终端 pair | 存量代码库结构化修改 | 老项目改造 |
| Gemini CLI | 终端 agent | 长上下文、多模态 | 大文件/文档处理 |
| Kiro / Trae / Windsurf | IDE agent | 不同工作流偏好 | 按团队习惯 |
二、六大方法论技巧分类（源于 ai-coding-guide）
- 提示词（~12）：意图清晰、给示例、区分做/不做、验收可测。
- CLAUDE.md / 项目记忆（~10）：写好项目约定，让 AI 自动遵循。
- Agent 与 Subagent（~10）：用子代理隔离上下文、并行独立任务。
- Hook（~8）：用钩子做自动化检查（格式化、测试、提交前校验）。
- 工作流（~12）：计划-执行-验证循环、TDD、增量提交。
- Git 与 PR（~8）：规范提交、小 PR、AI 辅助审查。
三、调试与成本（~16）
- 调试（~10）：先复现定位，再修；利用错误信息；避免无脑重试。
- 性能与成本（~6）：控制上下文长度、缓存、批量、选对模型档位。
四、通用铁律（给用户的话）
- 上下文比 prompt 措辞更重要：把"项目记忆"写好，胜过每次长提示。
- 小步快跑：每步可验证、可回退，比一次性大改动稳。
- 证据优先：让 AI 跑测试/命令并贴结果，不要信"应该好了"。
- 安全：密钥别进提示；警惕 AI 生成的危险命令；敏感数据不外传。
用户卡住时，先判断是"上下文问题 / 提示词问题 / 工具能力边界"，再给对应解法，附具体命令或模板。
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.
- 6d ago First seen · 41 lines · 61 tokens per session scan A 6a824f6fc8be
cheatsheet is a skill published in the GitHub repository TencentCloud/Octop (1,454 stars, last pushed today), licensed MIT. It adds 61 tokens to every session and 682 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 2026-08-30.
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