Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
직접 명령은 검토 Prompt를 거치지 않습니다. 실행하기 전에 소스를 확인하세요.
npx skills add https://github.com/vamseeachanta/workspace-hub --skill dspy-1-signatures명령은 한 줄로 유지됩니다. 복사하기 전에 가로로 스크롤해 전체 내용을 확인하세요.
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SOC 직업 분류 기준
SKILL.md 표시 중
| name | dspy-1-signatures |
| description | Sub-skill of dspy: 1. Signatures. |
| version | 1.0.0 |
| category | ai-prompting |
| type | reference |
| scripts_exempt | true |
Basic Signatures:
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:
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}")
Write outbound email and external messages in Vamsee Achanta's voice — a subtle offer to help, never bold or rash claims. Load before drafting ANY email, LinkedIn/Collide reply, proposal note, or outreach sent under his name.
Save/publish analysis or computation results from ANY ecosystem repo to Hugging Face as a queryable, viewer-renderable dataset. Use when the user wants to "save results to hugging face", "publish dataset to HF", "hugging face data saving", "save analysis results", "hf dataset", "make results queryable", or "render via datasets-server API". Reshapes nested results into flat parquet tables, writes a dataset card with a viewer `configs:` block and provenance, applies license/public-vs-private routing, enforces a domain data-quality gate (faithful-to-source != correct), publishes to `aceengineer/<repo>-<projection>`, and verifies via the datasets-server API.
Clone, create, fork, configure, and manage GitHub repositories. Manage remotes, secrets, releases, and workflows. Works with gh CLI or falls back to git + GitHub REST API via curl.