10-of-my-most-popular-text-to-image-series-prompts-78b0897e
generate a bunch of images, then you curate the results to handpick the best ones
text summarize
请根据来源仓库和你的 AI 工具说明安装此技能。先阅读技能文件和依赖要求,再添加到工作流。
SkillHub CLI
skillhub install aronchick/expanso-text-summarize打开来源仓库,查看说明和兼容环境后,按来源提供的命令或步骤安装。
将来源链接提供给你的 Agent,让它先读取说明、核对依赖,并向你说明将执行的安装步骤。
Summarize text into 3-5 bullet points using AI
expanso-edge binary in PATH)clawhub install expanso-edge
# Run standalone
echo '<input>' | expanso-edge run pipeline-cli.yaml
# Start as MCP server
expanso-edge run pipeline-mcp.yaml
expanso-cli job deploy https://skills.expanso.io/text-summarize/pipeline-cli.yaml
| File | Purpose |
|---|---|
skill.yaml |
Skill metadata (inputs, outputs, credentials) |
pipeline-cli.yaml |
Standalone CLI pipeline |
pipeline-mcp.yaml |
MCP server pipeline |
这些信息来自收录时的来源分析,可能滞后;使用前请核对最新来源文件。
Summarize any text into 3-5 bullet points while keeping your API keys local.
This is the "Hello World" of Expanso + OpenClaw skills. It demonstrates the core value proposition:
${OPENAI_API_KEY} is resolved on your machineexpanso-cli job validate
# Set your API key
export OPENAI_API_KEY=sk-...
# Summarize text from stdin
echo "Your long article or document text here..." | \
expanso-edge run pipeline-cli.yaml
# Or pipe a file
cat my-article.txt | expanso-edge run pipeline-cli.yaml
# Start the skill server
PORT=8080 expanso-edge run pipeline-mcp.yaml &
# Call from curl (or OpenClaw MCP)
curl -X POST http://localhost:8080/summarize \
-H "Content-Type: application/json" \
-d '{"text": "Your long article or document text here..."}'
| Environment Variable | Required | Description |
|---|---|---|
OPENAI_API_KEY |
Yes* | OpenAI API key |
PORT |
No | HTTP port for MCP mode (default: 8080) |
*Not required if using Ollama backend locally.
{
"summary": "• Key insight from the text\n• Another important point\n• Action item or recommendation\n• Supporting detail\n• Conclusion or next steps",
"metadata": {
"skill": "text-summarize",
"mode": "cli",
"model": "gpt-4o-mini",
"input_hash": "a1b2c3d4e5f6...",
"input_length": 2847,
"trace_id": "550e8400-e29b-41d4-a716-446655440000",
"timestamp": "2026-01-31T12:00:00Z"
}
}
For complete privacy, you can use Ollama instead of OpenAI. Edit the pipeline to replace openai_chat_completion with `o...
# text-summarize Summarize text into 3-5 bullet points using AI ## Requirements - Expanso Edge installed (`expanso-edge` binary in PATH) - Install via: `clawhub install expanso-edge` ## Usage ### CLI Pipeline ```bash # Run standalone echo '<input>' | expanso-edge run pipeline-cli.yaml ``` ### MCP Pipeline ```bash # Start as MCP server expanso-edge run pipeline-mcp.yaml ``` ### Deploy to Expanso Cloud ```bash expanso-cli job deploy https://skills.expanso.io/text-summarize/pipeline-cli.yaml ``` ## Files | File | Purpose | |------|---------| | `skill.yaml` | Skill metadata (inputs, outputs, credentials) | | `pipeline-cli.yaml` | Standalone CLI pipeline | | `pipeline-mcp.yaml` | MCP server pipeline |
# text-summarize
> Summarize any text into 3-5 bullet points while keeping your API keys local.
This is the "Hello World" of Expanso + OpenClaw skills. It demonstrates the core value proposition:
- **Your API keys stay local** - `${OPENAI_API_KEY}` is resolved on your machine
- **Validated pipelines** - This skill passes `expanso-cli job validate`
- **Full audit trail** - Every invocation is logged with input hash and trace ID
- **Backend flexibility** - Use OpenAI or Ollama without code changes
## Quick Start
### CLI Mode (for shell scripting)
```bash
# Set your API key
export OPENAI_API_KEY=sk-...
# Summarize text from stdin
echo "Your long article or document text here..." | \
expanso-edge run pipeline-cli.yaml
# Or pipe a file
cat my-article.txt | expanso-edge run pipeline-cli.yaml
```
### MCP Mode (for OpenClaw integration)
```bash
# Start the skill server
PORT=8080 expanso-edge run pipeline-mcp.yaml &
# Call from curl (or OpenClaw MCP)
curl -X POST http://localhost:8080/summarize \
-H "Content-Type: application/json" \
-d '{"text": "Your long article or document text here..."}'
```
## Configuration
| Environment Variable | Required | Description |
|---------------------|----------|-------------|
| `OPENAI_API_KEY` | Yes* | OpenAI API key |
| `PORT` | No | HTTP port for MCP mode (default: 8080) |
*Not required if using Ollama backend locally.
## Example Output
```json
{
"summary": "• Key insight from the text\n• Another important point\n• Action item or recommendation\n• Supporting detail\n• Conclusion or next steps",
"metadata": {
"skill": "text-summarize",
"mode": "cli",
"model": "gpt-4o-mini",
"input_hash": "a1b2c3d4e5f6...",
"input_length": 2847,
"trace_id": "550e8400-e29b-41d4-a716-446655440000",
"timestamp": "2026-01-31T12:00:00Z"
}
}
```
## Using with Ollama (Local, No API Key)
For complete privacy, you can use Ollama instead of OpenAI. Edit the pipeline to replace `openai_chat_completion` with `o...generate a bunch of images, then you curate the results to handpick the best ones
generate a bunch of images, then you curate the results to handpick the best ones
Productivity system based on Maya calendar with 13 natural tones for project management and personal development
Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in (single or multi-account), or reading/injecting/running secrets via op.