dspy-1-signatures

Sub-skill of dspy: 1. Signatures.

5 stars

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

$curl -o ~/.claude/skills/1-signatures/SKILL.md --create-dirs "https://raw.githubusercontent.com/vamseeachanta/workspace-hub/main/.agents/skills/_archive/ai/prompting/dspy/1-signatures/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/1-signatures/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How dspy-1-signatures Compares

Feature / Agentdspy-1-signaturesStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/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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