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Case Study Factory
by
vx:17605205782
· GitHub ↗
· v1.0.1
· MIT-0
237
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Install in OpenClaw
/install case-study-factory
Description
把项目材料转成案例文章,提炼起点、动作、结果和复用价值。;use for case-study, content, storytelling workflows;do not use for 伪造客户背书, 公开敏感数据.
README (SKILL.md)
案例研究工厂
你是什么
你是“案例研究工厂”这个独立 Skill,负责:把项目材料转成案例文章,提炼起点、动作、结果和复用价值。
Routing
适合使用的情况
- 把这个项目写成案例文章
- 整理成可复用案例模板
- 输入通常包含:项目背景、动作、结果、证据
- 优先产出:项目背景、关键动作、对外版本注意事项
不适合使用的情况
- 不要伪造客户背书
- 不要公开敏感数据
- 如果用户想直接执行外部系统写入、发送、删除、发布、变更配置,先明确边界,再只给审阅版内容或 dry-run 方案。
工作规则
- 先把用户提供的信息重组成任务书,再输出结构化结果。
- 缺信息时,优先显式列出“待确认项”,而不是直接编造。
- 默认先给“可审阅草案”,再给“可执行清单”。
- 遇到高风险、隐私、权限或合规问题,必须加上边界说明。
- 如运行环境允许 shell / exec,可使用:
python3 "{baseDir}/scripts/run.py" --input \x3C输入文件> --output \x3C输出文件>
- 如当前环境不能执行脚本,仍要基于
{baseDir}/resources/template.md与{baseDir}/resources/spec.json的结构直接产出文本。
标准输出结构
请尽量按以下结构组织结果:
- 项目背景
- 关键动作
- 结果与指标
- 经验总结
- 可复用方法
- 对外版本注意事项
本地资源
- 规范文件:
{baseDir}/resources/spec.json - 输出模板:
{baseDir}/resources/template.md - 示例输入输出:
{baseDir}/examples/ - 冒烟测试:
{baseDir}/tests/smoke-test.md
安全边界
- 默认保留匿名化与保密提醒。
- 默认只读、可审计、可回滚。
- 不执行高风险命令,不隐藏依赖,不伪造事实或结果。
Usage Guidance
This skill appears to do what it says: produce structured case-study drafts from user-provided inputs and optionally audit a project directory or CSV. Before running: (1) review the bundled scripts if you want extra assurance (they are included and use only the stdlib); (2) avoid passing broad or sensitive paths (do not point --input at /, your home, or system directories) because the script will read files you give it; (3) prefer using the examples or a sanitized copy of project materials; (4) use --dry-run or redirect output to files for review before taking any external actions; (5) if you enable autonomous agent invocation, be aware the agent could choose to run the local script on files it can access — restrict the skill to explicit user invocation if that concerns you.
Capability Analysis
Type: OpenClaw Skill
Name: case-study-factory
Version: 1.0.1
The skill bundle is a legitimate tool designed to transform project materials into structured case studies and perform basic file/directory audits. The core logic in `scripts/run.py` is transparent, using standard libraries to process text, CSVs, and directory structures without any network activity or unauthorized execution. Notably, the script includes a `pattern_audit` mode that scans for security risks like hardcoded secrets and dangerous shell commands, indicating a security-conscious design rather than malicious intent. The instructions in `SKILL.md` are well-aligned with the stated purpose and include explicit safety boundaries regarding sensitive data.
Capability Assessment
Purpose & Capability
Name/description, SKILL.md, README, resources, and scripts all align: the skill converts project materials into structured case-study Markdown and can audit directories or CSVs. Required binary (python3) is appropriate and no unrelated credentials, binaries, or config paths are requested.
Instruction Scope
SKILL.md confines the skill to producing draft outputs and using local resources. The provided script reads files and directories the user supplies and performs pattern scans (e.g., searching for headings, CSV fields, or secret-like strings). This is appropriate for a content/audit tool, but it means the tool will read any path you pass it—do not point it at broad or sensitive locations (e.g., / or your whole home) unless you intend that.
Install Mechanism
No install spec is present (instruction-only with bundled script). That is low-risk: nothing is downloaded or installed from remote sources and the script relies only on the Python standard library.
Credentials
The skill requires no environment variables or credentials. The script does not reference external auth tokens or network endpoints; it only reads local files supplied by the user.
Persistence & Privilege
always is false and the skill is user-invocable. It does not request permanent presence or modify other skills or system-wide settings. Autonomous invocation is allowed by platform default but not inherently risky here given the limited footprint.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install case-study-factory - After installation, invoke the skill by name or use
/case-study-factory - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.1
Version 1.0.1
- No visible file changes detected from the previous version.
- Functionality, documentation, and metadata remain the same as version 1.0.0.
v1.0.0
Initial release of case-study-factory.
- Converts project materials into structured case study articles.
- Extracts key elements: project background, actions, results, reusable value.
- Includes clear rules for output structure, privacy, and information boundaries.
- Ensures output is anonymized, reviewable, and avoids sensitive or false data.
- Provides guidance for workflows around content, case studies, and storytelling.
Metadata
Frequently Asked Questions
What is Case Study Factory?
把项目材料转成案例文章,提炼起点、动作、结果和复用价值。;use for case-study, content, storytelling workflows;do not use for 伪造客户背书, 公开敏感数据. It is an AI Agent Skill for Claude Code / OpenClaw, with 237 downloads so far.
How do I install Case Study Factory?
Run "/install case-study-factory" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Case Study Factory free?
Yes, Case Study Factory is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does Case Study Factory support?
Case Study Factory is cross-platform and runs anywhere OpenClaw / Claude Code is available (darwin, linux, win32).
Who created Case Study Factory?
It is built and maintained by vx:17605205782 (@52yuanchangxing); the current version is v1.0.1.
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