ai-prompting-1-version-control-prompts
Sub-skill of ai-prompting: 1. Version Control Prompts (+3).
Best use case
ai-prompting-1-version-control-prompts is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Sub-skill of ai-prompting: 1. Version Control Prompts (+3).
Teams using ai-prompting-1-version-control-prompts 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-version-control-prompts/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How ai-prompting-1-version-control-prompts Compares
| Feature / Agent | ai-prompting-1-version-control-prompts | 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 ai-prompting: 1. Version Control Prompts (+3).
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. Version Control Prompts (+3)
## 1. Version Control Prompts
```yaml
# prompts/summarize_v2.yaml
version: 2.0.0
model: gpt-4
temperature: 0.3
template: |
Summarize the following text...
metrics:
avg_quality: 0.87
latency_p95: 2.3s
```
## 2. Evaluation-Driven Development
```python
def evaluate_prompt(prompt_template, test_cases):
results = []
for case in test_cases:
output = generate(prompt_template.format(**case.input))
score = evaluate_output(output, case.expected)
results.append(score)
return {
"mean": sum(results) / len(results),
"min": min(results),
"failed_cases": [c for c, s in zip(test_cases, results) if s < 0.7]
}
```
## 3. Cost Monitoring
```python
class CostTracker:
def __init__(self, budget_limit=100.0):
self.total_cost = 0
self.budget_limit = budget_limit
def track(self, tokens_in, tokens_out, model):
cost = calculate_cost(tokens_in, tokens_out, model)
self.total_cost += cost
if self.total_cost > self.budget_limit * 0.8:
logger.warning(f"Approaching budget limit: ${self.total_cost:.2f}")
```
## 4. Graceful Degradation
```python
def get_response(query, context):
try:
return advanced_rag_pipeline(query, context)
except ModelOverloadError:
return simpler_model_fallback(query, context)
except Exception:
return cached_similar_response(query)
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