signal_processing
Signal Processing Analysis - Analyze signals: duty cycle, frequency range, electron wavelength, and measurement error analysis. Use this skill for signal processing tasks involving calculate duty cycle calculate frequency range electron wavelength calculate absolute error. Combines 4 tools from 3 SCP server(s).
Best use case
signal_processing is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Signal Processing Analysis - Analyze signals: duty cycle, frequency range, electron wavelength, and measurement error analysis. Use this skill for signal processing tasks involving calculate duty cycle calculate frequency range electron wavelength calculate absolute error. Combines 4 tools from 3 SCP server(s).
Teams using signal_processing should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/signal_processing/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How signal_processing Compares
| Feature / Agent | signal_processing | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Signal Processing Analysis - Analyze signals: duty cycle, frequency range, electron wavelength, and measurement error analysis. Use this skill for signal processing tasks involving calculate duty cycle calculate frequency range electron wavelength calculate absolute error. Combines 4 tools from 3 SCP server(s).
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
Related Guides
SKILL.md Source
# Signal Processing Analysis
**Discipline**: Signal Processing | **Tools Used**: 4 | **Servers**: 3
## Description
Analyze signals: duty cycle, frequency range, electron wavelength, and measurement error analysis.
## Tools Used
- **`calculate_duty_cycle`** from `server-21` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/21/Electrical_Engineering_and_Circuit_Calculations`
- **`calculate_frequency_range`** from `server-23` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/23/Optics_and_Electromagnetics`
- **`electron_wavelength`** from `server-23` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/23/Optics_and_Electromagnetics`
- **`calculate_absolute_error`** from `server-26` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/26/Data_processing_and_statistical_analysis`
## Workflow
1. Calculate duty cycle
2. Calculate frequency range
3. Compute electron wavelength
4. Analyze measurement error
## Test Case
### Input
```json
{
"pulse_width": 0.005,
"period": 0.02
}
```
### Expected Steps
1. Calculate duty cycle
2. Calculate frequency range
3. Compute electron wavelength
4. Analyze measurement error
## Usage Example
> **Note:** Replace `sk-b04409a1-b32b-4511-9aeb-22980abdc05c` with your own SCP Hub API Key. You can obtain one from the [SCP Platform](https://scphub.intern-ai.org.cn).
```python
import asyncio
import json
from contextlib import AsyncExitStack
from mcp import ClientSession
from mcp.client.streamable_http import streamablehttp_client
from mcp.client.sse import sse_client
SERVERS = {
"server-21": "https://scp.intern-ai.org.cn/api/v1/mcp/21/Electrical_Engineering_and_Circuit_Calculations",
"server-23": "https://scp.intern-ai.org.cn/api/v1/mcp/23/Optics_and_Electromagnetics",
"server-26": "https://scp.intern-ai.org.cn/api/v1/mcp/26/Data_processing_and_statistical_analysis"
}
async def connect(url, stack):
transport = streamablehttp_client(url=url, headers={"SCP-HUB-API-KEY": "sk-b04409a1-b32b-4511-9aeb-22980abdc05c"})
read, write, _ = await stack.enter_async_context(transport)
ctx = ClientSession(read, write)
session = await stack.enter_async_context(ctx)
await session.initialize()
return session
def parse(result):
try:
if hasattr(result, 'content') and result.content:
c = result.content[0]
if hasattr(c, 'text'):
try: return json.loads(c.text)
except: return c.text
return str(result)
except: return str(result)
async def main():
async with AsyncExitStack() as stack:
# Connect to required servers
sessions = {}
sessions["server-21"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/21/Electrical_Engineering_and_Circuit_Calculations", stack)
sessions["server-23"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/23/Optics_and_Electromagnetics", stack)
sessions["server-26"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/26/Data_processing_and_statistical_analysis", stack)
# Execute workflow steps
# Step 1: Calculate duty cycle
result_1 = await sessions["server-21"].call_tool("calculate_duty_cycle", arguments={})
data_1 = parse(result_1)
print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}")
# Step 2: Calculate frequency range
result_2 = await sessions["server-23"].call_tool("calculate_frequency_range", arguments={})
data_2 = parse(result_2)
print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}")
# Step 3: Compute electron wavelength
result_3 = await sessions["server-23"].call_tool("electron_wavelength", arguments={})
data_3 = parse(result_3)
print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}")
# Step 4: Analyze measurement error
result_4 = await sessions["server-26"].call_tool("calculate_absolute_error", arguments={})
data_4 = parse(result_4)
print(f"Step 4 result: {json.dumps(data_4, indent=2, ensure_ascii=False)[:500]}")
# Cleanup
print("Workflow complete!")
if __name__ == "__main__":
asyncio.run(main())
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