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Design reusable prompt templates that encode domain-specific patterns for recurring AI tasks.
Passed 20 of 22 test cases with the skill, and 14 of 22 without it.
and 3 more in the full report
6 fixed14 passed either way2 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 6 test cases the skill fixed. Same model, same prompt, with and without the skill.
We are building a template to audit Helm chart values files for infrastructure misconfigurations. Which engineering domain best describes this template when organizing templates across devops, frontend, backend, or security?
| Checked | Without skill | With skill |
|---|---|---|
| Categorizes the template under devops based on infrastructure configuration scope | Missed | Passed |
The instructions your model reads when the skill loads.
# 1. Check if template-worthy
# Criteria: 2+ uses, 5+ decision points, domain-specific
# 2. Extract pattern
# Identify invariants (constants) vs variants (parameters)
# 3. Create template
# Use Intent → Constraints → Success Criteria structure
Think like a patterns library designer extracting recurring prompt structures. Identify what varies (parameters) vs what stays constant (pattern), encode domain knowledge into constraints, and design templates that reduce cognitive load while maintaining quality.
"Have I used this pattern 2+ times?"
| Scenario | Template? |
|---|---|
| "Generate Git commit message" (daily) | YES |
| "Explain React hooks to new dev" (once) | NO |
| "Debug Bash permission error" (recurring) | YES |
| "Research specific API quirk" (unique) | NO |
Principle: Premature templating adds maintenance burden. Wait for second use.
"What stays constant vs what changes?"
| Type | What It Is | Examples |
|---|---|---|
| Invariants | Template structure | Intent verb, core constraints, output format |
| Variants | Parameters | File names, project context, thresholds |
test cases passed, vs 14 / 22 without
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