Awesome OpenClaw Use Cases Chinese is a Chinese-language collection of practical examples showing how an AI agent can automate work and personal tasks across areas such as messaging, research, content creation, and operations. It is for people learning or adapting OpenClaw workflows, and its catalogue entries provide instructions and a command for using those examples.
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.
git clone --depth 1 https://github.com/AlexAnys/awesome-openclaw-usecases-zhWrote 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/commands/alexanys/awesome-openclaw-usecases-zh/screen-usecase)<a href="https://agentmods.dev/commands/alexanys/awesome-openclaw-usecases-zh/screen-usecase"><img src="https://agentmods.dev/badge/commands/alexanys/awesome-openclaw-usecases-zh/screen-usecase/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/commands/alexanys/awesome-openclaw-usecases-zh/screen-usecase"><img src="https://agentmods.dev/badge/commands/alexanys/awesome-openclaw-usecases-zh/screen-usecase.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 | $0.00035 | $0.02740 |
| Opus 5.5 | $0.00014 | $0.01096 |
| Sonnet 5.5 | $0.00007 | $0.00548 |
| Haiku 4.5 | $0.00003 | $0.00274 |
Grade A, and why
screen-usecase 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.
How it starts
The opening of the file, as written. The whole thing — 282 lines — stays where its author put it; the contents beside it link to each section on GitHub.
评估并处理用户提供的 OpenClaw 用例来源。
$ARGUMENTS — 用例来源(URL、推文链接、文章描述,或用户粘贴的内容)
工作流总览
输入来源 → 深度研究 → 三条收录标准评估 → 依赖验证 → 决策
↓
通过 → 撰写用例 → 自审 → 提交 PR
不通过 → 输出评估报告 + 改进建议
Phase 1: 深度研究
1.1 获取主源内容
根据来源类型选择获取方式:
| 来源类型 | 获取方式 |
|---|---|
| 博客/文章 URL | WebFetch 获取全文 |
| 推文/X 链接 | WebFetch(可能失败)→ 请用户粘贴内容 |
| GitHub 仓库 | DeepWiki read_wiki_structure + ask_question |
| 用户粘贴内容 | 直接分析 |
提取清单(必须从主源中明确获得,缺一不可):
- 具体配置示例(JSON/YAML/Markdown)
- 实际执行步骤或命令
- 产出证据(截图、日志、成本数据、性能指标)
- 作者/团队信息
缺少任一项,在评估中标注为"证据不足"。
1.2 多源验证
至少找到 2 个独立来源,使用 WebSearch + GitHub CLI 并行搜索:
搜索策略(并行启动 3 个 Agent):
Agent 1: WebSearch 搜索中文社区(CSDN、知乎、V2EX、53AI)
Agent 2: WebSearch 搜索英文社区(Reddit、HN、X/Twitter、Dev.to)
Agent 3: GitHub 搜索相关仓库 + 代码(gh search repos / gh search code)
对每个来源记录:
| 字段 | 要求 |
|---|---|
| URL | 完整链接 |
| 类型 | 官方文档 / 开源仓库 / 社区实践 / 内容聚合 |
| 独立性 | 是否与主源独立(转载/翻译不算独立) |
| 证据级别 | 有代码 → 实测 / 有步骤无产出 → 教程 / 仅描述 → 概念 |
1.3 依赖验证
对用例涉及的每个关键技术依赖,检查是否有稳定的开源支撑:
# 搜索相关 SDK/工具
gh search repos "{依赖关键词}" --sort stars --limit 10
# 检查具体仓库
gh api repos/{owner}/{repo} --jq '.stargazers_count, .pushed_at, .archived, .license.spdx_id'
判定标准:
| Stars | 判定 | 处理方式 |
|---|---|---|
| 1000+ 且活跃维护 | 可推荐 | 在用例中作为推荐方案 |
| 500-999 且活跃 | 可提及 | 标注"社区项目,自行评估风险" |
| <500 或已归档 | 不推荐 | 不在用例中引用,或标注"无成熟开源方案" |
对于无开源 SDK 的平台(如国内电商平台 API):
- 检查官方开发者门户是否提供 SDK
- 检查 API 准入要求(是否需要企业资质)
- 在用例中如实说明现状,不回避
Phase 2: 收录标准评估
对照 CONTRIBUTING.md 的三条收录标准逐项打分:
评估卡片模板
## 用例评估:[用例名称]
### 1. 真实跑通
- 是否有端到端执行证据?[是/否/部分]
- 证据类型:[代码仓库 / 配置截图 / 日志输出 / 成本数据 / PoC 报告]
- 证据链完整度:[完整 / 有缺口 / 仅概念]
- 得分:[通过 / 有条件通过 / 不通过]
### 2. 多源验证
- 独立来源数量:[N 个]
- 来源 1:[URL] — [类型] — [证据级别]
- 来源 2:[URL] — [类型] — [证据级别]
- 得分:[通过 / 有条件通过 / 不通过]
### 3. 可操作
- 是否有可复制的配置/提示词?[是/否]
- 读者照做能否复现?[能 / 需适配 / 不能]
- 关键依赖是否可获取?[全部可获取 / 部分受限 / 主要受限]
- 得分:[通过 / 有条件通过 / 不通过]
### 综合判定
- 结论:[适合收录 / 有条件收录 / 不适合收录]
- 理由:[一句话]
- 如果"有条件":需要补充什么?
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 · 282 lines · 35 tokens per session scan A 9028416e5478
screen-usecase is a command published in the GitHub repository AlexAnys/awesome-openclaw-usecases-zh (4,444 stars, last pushed 3mo ago), licensed MIT. It adds 35 tokens to every session and 2,740 once invoked, about $0.0001 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.
Other commands, from other repositories
telemetry
Inspect, opt in/out of, and audit mochi's anonymous usage telemetry.
render
Inspect an archived transcript (gzipped) — show counts, tool calls, errors. Useful after a PreCompact or SessionEnd, before deciding to write a retroactive /continuum:checkpoint.
playbook
Manage Mochi playbooks (list, show, run, delete). Plays through the browser MCP tools.
code-review
Code review — local uncommitted changes or GitHub PR (pass PR number/URL for PR mode).
docs-fe
Generate a concise FE handoff doc for changed endpoints — branch, APIs, request/response contracts, search params, error codes.
next-step
Determine and suggest the next step based on dependencies.