dspy-example-1-engineering-report-analysis-pipeline
Sub-skill of dspy: Example 1: Engineering Report Analysis Pipeline (+2).
Best use case
dspy-example-1-engineering-report-analysis-pipeline is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Sub-skill of dspy: Example 1: Engineering Report Analysis Pipeline (+2).
Teams using dspy-example-1-engineering-report-analysis-pipeline 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/example-1-engineering-report-analysis-pipeline/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How dspy-example-1-engineering-report-analysis-pipeline Compares
| Feature / Agent | dspy-example-1-engineering-report-analysis-pipeline | 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: Example 1: Engineering Report Analysis Pipeline (+2).
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
# Example 1: Engineering Report Analysis Pipeline (+2)
## Example 1: Engineering Report Analysis Pipeline
```python
import dspy
from dspy.teleprompt import BootstrapFewShot
from typing import List
import json
# Configure DSPy
dspy.settings.configure(lm=dspy.OpenAI(model="gpt-4", max_tokens=2000))
# Define signatures
*See sub-skills for full details.*
## Example 2: Optimized Technical Q&A System
```python
import dspy
from dspy.teleprompt import BootstrapFewShotWithRandomSearch
from dspy.retrieve.chromadb_rm import ChromadbRM
# Setup retriever
retriever = ChromadbRM(
collection_name="engineering_knowledge",
persist_directory="./chroma_db",
k=5
*See sub-skills for full details.*
## Example 3: Comparison with Baseline
```python
import dspy
from dspy.evaluate import Evaluate
from dspy.teleprompt import BootstrapFewShot
import pandas as pd
class SimpleQA(dspy.Module):
"""Baseline: Simple prediction without optimization."""
def __init__(self):
super().__init__()
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