prompt-template-designer
Design reusable prompt templates that encode domain-specific patterns for recurring AI tasks. Use when you've executed similar prompts 2+ times and need to capture the pattern as reusable intelligence. NOT for one-off prompts or generic "ask AI a question" patterns.
Best use case
prompt-template-designer is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Design reusable prompt templates that encode domain-specific patterns for recurring AI tasks. Use when you've executed similar prompts 2+ times and need to capture the pattern as reusable intelligence. NOT for one-off prompts or generic "ask AI a question" patterns.
Teams using prompt-template-designer should expect a more consistent output, faster repeated execution, less prompt rewriting, better workflow continuity with your supporting tools.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
- You already have the supporting tools or dependencies needed by this skill.
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/prompt-template-designer/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How prompt-template-designer Compares
| Feature / Agent | prompt-template-designer | 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?
Design reusable prompt templates that encode domain-specific patterns for recurring AI tasks. Use when you've executed similar prompts 2+ times and need to capture the pattern as reusable intelligence. NOT for one-off prompts or generic "ask AI a question" patterns.
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
# Prompt Template Designer
## Quick Start
```bash
# 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
```
## Persona
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.
## Analysis Questions
### 1. Recurrence Check
**"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.
### 2. Variation Analysis
**"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 |
### 3. Complexity Check
**"Does this have 5+ decision points?"**
**High-value template** (5+ decisions): Code review prompt (action verb, language, review focus, output format, severity levels, style guide, context, fixes vs identify)
**Low-value** (1-3 decisions): "Explain specific Git command" → just ask directly
### 4. Domain Knowledge Check
**"Does this encode domain-specific intelligence?"**
**High-value**: Team conventions, quality standards, project constraints
**Low-value**: Generic "ask AI a question" or "generate code"
### 5. Parameter Design
**"What parameters make this flexible but not vague?"**
| Pattern | Example |
|---------|---------|
| Enum | `{{ACTION_TYPE}}` - Values: [CREATE, DEBUG, REFACTOR] |
| Path | `{{TARGET_FILE}}` - Type: file_path |
| Text | `{{CHANGES_MADE}}` - Type: bullet_list |
| Composite | `{{ERROR_CONTEXT}}` - Required fields: error_message, file_location |
**Rule**: 3-7 parameters optimal. More → split into multiple templates.
## Principles
### Principle 1: Template When Recurs 2+
- **First use**: Write prompt directly
- **Second use**: Notice pattern, extract template
- **Third+ uses**: Use template, refine
### Principle 2: Parameters Over Hard-Coding
```
# Before (hard-coded)
DEBUG backup.sh with "Permission denied" error
# After (parameterized)
DEBUG {{SCRIPT_NAME}} with "{{ERROR_MESSAGE}}" error
```
### Principle 3: Document Success Patterns
Templates must include:
- When to use (trigger conditions)
- Why it works (pattern encoded)
- Example filled (concrete instantiation)
- Common mistakes (what to avoid)
### Principle 4: Version and Evolve
```markdown
### v2.0.0 (2025-11-18)
- BREAKING: Changed {{CHANGES}} to structured {{CHANGES_MADE}}
- Added "business value" requirement
### v1.0.0 (2025-10-15)
- Initial extraction from successful prompts
```
### Principle 5: Measure Effectiveness
| Metric | Target |
|--------|--------|
| Success rate | >85% |
| Time saved | Measurable |
| Iterations needed | <3 |
| Team adoption | >50% |
## Template Structure
```markdown
---
template_name: {{descriptive-name}}
category: {{create|debug|refactor|optimize|analyze|generate}}
domain: {{backend|frontend|devops|testing|documentation}}
version: {{semantic-version}}
success_rate: {{percentage}}
---
# {{Template Name}}
## When to Use
{{Trigger conditions}}
## Parameters
### {{PARAM_1}}
- **Type**: {{enum|path|text|composite}}
- **Example**: {{value}}
- **Required**: {{yes|no}}
## Template
\```
INTENT:
{{ACTION_VERB}} {{description with parameters}}
CONSTRAINTS:
- {{invariant constraint}}
- {{parameterized constraint using {{PARAM}}}}
SUCCESS CRITERIA:
- {{measurable criterion}}
\```
## Example (Filled)
{{Concrete instantiation}}
## Common Mistakes
- {{Anti-pattern and how to avoid}}
```
## Example: Git Commit Message Generator
```markdown
---
template_name: git-commit-message-conventional
category: generate
version: 2.0.0
success_rate: 95%
---
## Parameters
- **CHANGES_MADE**: List of changes (required)
- **JIRA_TICKET**: PROJ-NNNN format (required)
- **SCOPE**: [auth, api, ui, db, devops] (required)
- **TYPE**: [feat, fix, docs, refactor, test] (required)
## Template
\```
GENERATE Git commit message
CHANGES: {{CHANGES_MADE}}
CONSTRAINTS:
- Format: {{TYPE}}({{SCOPE}}): <description> [{{JIRA_TICKET}}]
- Subject: Imperative mood, <50 chars
- Body: Explain WHY (business value)
SUCCESS CRITERIA:
- Passes commitlint
- Teammate understands without reading diff
\```
## Example (Filled)
\```
feat(auth): add JWT refresh endpoint [PROJ-1234]
- Add /auth/refresh: improves mobile UX by eliminating re-logins
- Extend token to 24h: reduces authentication friction
\```
## Common Mistakes
- Forgetting Jira ticket
- Past tense ("Added") instead of imperative ("Add")
- Explaining WHAT instead of WHY
```
## Anti-Patterns
| Anti-Pattern | Symptom | Fix |
|--------------|---------|-----|
| Template Before Pattern | Creating for unused prompts | Use manually 2+ times first |
| Over-Parameterization | 15+ parameters | Aim for 3-7, split if more |
| Under-Parameterization | Only works for one scenario | Must apply to 3+ use cases |
| No Success Metrics | Never tracked quality | Track success rate, time saved |
## Quick Reference
**Template Creation Checklist**:
- [ ] Used pattern 2+ times?
- [ ] Identified constants vs variants?
- [ ] 5+ decision points?
- [ ] Parameters with examples?
- [ ] Filled example included?
- [ ] Success criteria defined?
**Parameter Design**:
- [ ] Descriptive names (not {{X}})?
- [ ] Type specified?
- [ ] Examples provided?
- [ ] Required/optional marked?
## Teaching Integration (L2 → L3)
**Transition signal**: Student has written similar prompts 2+ times with 85%+ success
**Teaching sequence**:
1. "You've done this before. What's similar?" (identify recurrence)
2. "What constraints appear in both?" (extract constants)
3. "What changed?" (parameterize variants)
4. "Let's formalize as reusable intelligence" (create template)
## If Verification Fails
1. Check recurrence: "Has this been used 2+ times?"
2. Check complexity: "Are there 5+ decision points?"
3. Check parameters: "3-7 well-designed parameters?"
4. **Stop and report** if template adds more cognitive load than it savesRelated Skills
cloudformation-template-creator
Cloudformation Template Creator - Auto-activating skill for AWS Skills. Triggers on: cloudformation template creator, cloudformation template creator Part of the AWS Skills skill category.
optimizing-prompts
This skill optimizes prompts for Large Language Models (LLMs) to reduce token usage, lower costs, and improve performance. It analyzes the prompt, identifies areas for simplification and redundancy removal, and rewrites the prompt to be more concise and effective. It is used when the user wants to reduce LLM costs, improve response speed, or enhance the quality of LLM outputs by optimizing the prompt. Trigger terms include "optimize prompt", "reduce LLM cost", "improve prompt performance", "rewrite prompt", "prompt optimization".
promptify
Transform user requests into detailed, precise prompts for AI models. Use when users say "promptify", "promptify this", or explicitly request prompt engineering or improvement of their request for better AI responses.
prompt-improver
Optimize prompts for better AI responses. Use when user asks to improve a prompt, refine a prompt, make a prompt better, optimize prompting, review their prompt, or says "/improve-prompt". Transforms vague requests into clear, specific, actionable prompts.
recipe-create-doc-from-template
Copy a Google Docs template, fill in content, and share with collaborators.
gws-modelarmor-sanitize-prompt
Google Model Armor: Sanitize a user prompt through a Model Armor template.
gws-modelarmor-create-template
Google Model Armor: Create a new Model Armor template.
tldr-prompt
Create tldr summaries for GitHub Copilot files (prompts, agents, instructions, collections), MCP servers, or documentation from URLs and queries.
prompt-builder
Guide users through creating high-quality GitHub Copilot prompts with proper structure, tools, and best practices.
make-skill-template
Create new Agent Skills for GitHub Copilot from prompts or by duplicating this template. Use when asked to "create a skill", "make a new skill", "scaffold a skill", or when building specialized AI capabilities with bundled resources. Generates SKILL.md files with proper frontmatter, directory structure, and optional scripts/references/assets folders.
ui-designer
Extract design systems from reference UI images and generate implementation-ready UI design prompts. Use when users provide UI screenshots/mockups and want to create consistent designs, generate design systems, or build MVP UIs matching reference aesthetics.
promptfoo-evaluation
Configures and runs LLM evaluation using Promptfoo framework. Use when setting up prompt testing, creating evaluation configs (promptfooconfig.yaml), writing Python custom assertions, implementing llm-rubric for LLM-as-judge, or managing few-shot examples in prompts. Triggers on keywords like "promptfoo", "eval", "LLM evaluation", "prompt testing", or "model comparison".