screen-usecase

A workflow for checking and writing OpenClaw use cases from a URL, social-media post, article, repository, or pasted text. OpenClaw use cases are documented examples of how OpenClaw is used to perform a task.

In plain words
What is it for?
Researching a proposed use case, checking it against evidence and multiple sources, validating its technical dependencies, and preparing a pull request or an evaluation report.
Why use it?
It separates claims with enough evidence from claims that cannot be verified, then checks important dependencies before recommending or submitting a use case.

Command for Claude Code

Install

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.

agentmods
npx agentmods add commands/alexanys/awesome-openclaw-usecases-zh/screen-usecase
Clone the repo
git clone --depth 1 https://github.com/AlexAnys/awesome-openclaw-usecases-zh

Made for: Claude Code.

Per session 35 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 2,740 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00035 $0.02740
Opus 5 $0.00017 $0.01370
Sonnet 5 $0.00007 $0.00548
Haiku 4.5 $0.00003 $0.00274

Measured yesterday against content hash 9028416e5478, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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 yesterday.

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.

.claude/commands/screen-usecase.md · 282 lines

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?

Read the full file on GitHub · 282 lines

Changes

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.

  1. yesterday First seen · 282 lines · 35 tokens per session scan A 9028416e5478

Subscribe to this mod's changes

screen-usecase is a command published in the GitHub repository AlexAnys/awesome-openclaw-usecases-zh (4,436 stars, last pushed 1mo ago), licensed MIT. It adds 35 tokens to every session and 2,740 once invoked, about $0.0002 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.