dspy-1-signatures
Sub-skill of dspy: 1. Signatures.
Best use case
dspy-1-signatures is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Sub-skill of dspy: 1. Signatures.
Teams using dspy-1-signatures 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/1-signatures/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How dspy-1-signatures Compares
| Feature / Agent | dspy-1-signatures | 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?
Sub-skill of dspy: 1. Signatures.
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.
SKILL.md Source
# 1. Signatures
## 1. Signatures
**Basic Signatures:**
```python
import dspy
# Configure LLM
lm = dspy.OpenAI(model="gpt-4", max_tokens=1000)
dspy.settings.configure(lm=lm)
# Inline signature (simple)
classify = dspy.Predict("document -> category")
result = classify(document="The mooring line tension exceeded limits.")
print(result.category)
# Class-based signature (recommended)
class SentimentAnalysis(dspy.Signature):
"""Analyze the sentiment of engineering feedback."""
feedback = dspy.InputField(desc="Engineering feedback or review text")
sentiment = dspy.OutputField(desc="Sentiment: positive, negative, or neutral")
confidence = dspy.OutputField(desc="Confidence score 0-1")
# Use signature
analyzer = dspy.Predict(SentimentAnalysis)
result = analyzer(feedback="The mooring design passed all safety checks.")
print(f"Sentiment: {result.sentiment}, Confidence: {result.confidence}")
```
**Complex Signatures with Multiple Fields:**
```python
class EngineeringAnalysis(dspy.Signature):
"""Analyze an engineering report and extract key insights."""
report_text = dspy.InputField(
desc="Full text of the engineering report"
)
domain = dspy.InputField(
desc="Engineering domain (offshore, structural, mechanical)"
)
summary = dspy.OutputField(
desc="Concise 2-3 sentence summary of findings"
)
key_metrics = dspy.OutputField(
desc="List of key metrics mentioned with values"
)
risk_factors = dspy.OutputField(
desc="Identified risk factors and concerns"
)
recommendations = dspy.OutputField(
desc="Actionable recommendations from the report"
)
confidence_level = dspy.OutputField(
desc="Overall confidence in analysis: high, medium, or low"
)
# Create predictor
report_analyzer = dspy.Predict(EngineeringAnalysis)
# Analyze report
result = report_analyzer(
report_text="""
The mooring analysis for Platform Alpha shows maximum tensions
of 2,450 kN under 100-year storm conditions. Safety factors
range from 1.72 to 2.15 across all lines. Line 3 shows the
lowest margin at the fairlead connection. Fatigue life estimates
indicate 35-year service life, exceeding the 25-year requirement.
Chain wear measurements show 8% diameter loss after 5 years.
""",
domain="offshore"
)
print(f"Summary: {result.summary}")
print(f"Key Metrics: {result.key_metrics}")
print(f"Risk Factors: {result.risk_factors}")
print(f"Recommendations: {result.recommendations}")
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