| name | prompt-refiner |
| description | Transforms casual or voice-transcribed user requests into precise, AI-optimized prompts. Handles mixed languages, vague input, and ambiguity. Reduces task execution time by 2-3x and improves accuracy by 40-60%. Applies prompt engineering best practices including persona assignment, few-shot examples, chain of thought, and prompt chaining. |
Permission Setup Review
python scripts/_setup_logs.py is an optional setup helper for the Prompt Refiner skill. It may apply broad filesystem permissions for compatibility. Show the target paths and permission mode, explain the risk, and prefer a narrower mode when shared write access is not needed.
Prompt Refiner
Turn messy input into structured, AI-optimized prompts on the first try.
When to Use
- Voice transcription input (speech-to-text)
- Casual, informal, or mixed-language requests (English + Chinese)
- Vague or ambiguous requests (missing target, unclear scope)
- Complex multi-step tasks that benefit from chaining
- Before destructive actions (delete, restart, deploy)
Skip if: request is already specific, task is simple/low-stakes, or user says "just do it."
Core Framework: TCREI
Google's prompt engineering framework — apply to every refined prompt:
| Component | What to include |
|---|
| Task | Action verb + specific target. "Summarize the sales report for Q1" |
| Context | Background, environment, constraints. "Account: jamesxu81@gmail.com, NZ timezone" |
| References | Examples, templates, tone samples. "Match this format: [example]" |
| Evaluate | How to judge the output. "Flag any missing data" |
| Iterate | How to improve if result is off |
The Process (5 Steps)
1. Analyze
Identify: Intent · Target · Constraints · Gaps · Language
2. Assign Persona (Always)
Give the AI a role that matches the task:
- Code task →
"You are a senior Node.js engineer"
- Email task →
"You are a professional business writer"
- Data task →
"You are a data analyst specializing in sales metrics"
- Security task →
"You are a cybersecurity expert reviewing for vulnerabilities"
3. Clarify (If Critical Gaps Exist)
Ask ONE focused question — not multiple.
- ✅ "Which file —
api/validate.js or api/auth.js?"
- ❌ "Which file? What language? What to check? When is the deadline?"
4. Construct the Structured Prompt
Persona: [Role + expertise relevant to the task]
Task: [Action verb + specific target]
Context: [System, environment, account, paths, dates]
References: [Examples, templates, or few-shot samples when format matters]
Requirements: [Constraints, scope, edge cases, what NOT to do]
Output: [Format, destination, success criteria, level of detail]
Advanced techniques — apply when appropriate:
- Few-shot: Add 1–2 input/output examples when format consistency matters
- Chain of Thought: Add
"Think step by step:" for complex reasoning
- Prompt Chaining: Break multi-step tasks into linked sub-prompts
- Meta Prompting: Ask AI to refine the prompt itself before executing
See references/techniques.md for when/how to use each technique.
5. Confirm & Execute
- Destructive/complex actions: Show 1-sentence summary → get confirmation
- Safe/obvious tasks: Execute directly
Quick Checklist
Before executing, verify:
- ✅ Persona assigned
- ✅ Intent is clear (specific action + target)
- ✅ Context is concrete (real paths, accounts, dates)
- ✅ Requirements are testable
- ✅ Output format defined
- ✅ Success criteria stated
Real Examples
See references/examples.md for complete worked examples including:
- Voice transcription (Chinese) → Gmail check
- Vague code review → structured debug prompt
- Mixed-language service restart
- Complex multi-step task with chaining
Common Anti-Patterns to Avoid
| Anti-Pattern | Fix |
|---|
| Too many requirements in one prompt | Split into chained sub-prompts |
| Vague success criteria ("write a good report") | Define measurable criteria |
| No edge case handling | Add: "If X is missing, do Y" |
| Tweaking temperature instead of the prompt | Improve prompt structure first |
| Negative instructions only ("don't do X") | Tell it what TO do instead |
