smart-pole-instructor
- Repo stars 0
- Author updated Live
- Author repo smart-pole-skill
- Domain
- AI
- Compatible agents
-
- Claude Code
- Cursor
- Cline
- Codex
- Windsurf
- Gemini CLI
- +20
- Trust score
- 88 / 100 · community maintained
- Author / version / license
- @zerotohero99 · no license declared
- Token usage
- Lean
- Setup complexity
- Plug-and-play
- External API key
- Not required
- Operating systems
- Unspecified (assume cross-platform)
- Runtime requirements
- No special requirements
- Permissions
-
- Read-only
- Write / modify
- Network behavior
- Local-only
- Install commands
- 26 variants
Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.
Heads up: 未限定 allowed-tools，默认拥有全部工具权限。
---
name: smart-pole-instructor
description: Use when a user provides a vague prompt and needs structured clarification to produce a precise…
category: ai
runtime: no special runtime
---
# smart-pole-instructor output preview
## PART A: Task fit
- Use case: Use when a user provides a vague prompt and needs structured clarification to produce a precise master prompt. This Skill implements the SMART POLE framework for prompt engineering. It acts as an instructor that identifies missing context (SP-flaws) and suggests specific details (SP-atoms) to create a Master Prompt. runs entirely locally. Works with Claud….
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “How to use this Skill / The 9 Categories / Locale Sub-dimensions (L1-L4)” and do not present inference as author intent.
## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “Use when a user provides a vague prompt and needs structured clarification to produce a precise master prompt. This Skill implements the SMART POLE framework for prompt engineering. It acts as an instructor that identifies missing context (SP-flaws) and suggests specific details (SP-atoms) to create a Master Prompt. runs entirely locally. Works with Claud…”.
- **02** When the source has headings, the agent prioritizes “How to use this Skill / The 9 Categories / Locale Sub-dimensions (L1-L4)” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, write/modify files; mostly runs locally; usually needs no extra API key.
## Running Rules
- read files, write/modify files; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options.The source does not require a stable slash command. After installation, invoke the skill by name and describe the task.
Name target files or source material, expected output, forbidden changes, and whether network or shell access is allowed. Permission fingerprint: read files, write/modify files.
Start with a small task and check whether the result follows “How to use this Skill / The 9 Categories / Locale Sub-dimensions (L1-L4)”. Inspect diffs, logs, previews, or tests before expanding scope.
Confirm the final output includes a concrete result, evidence, and next action. If it stays generic, tighten inputs, boundaries, and acceptance criteria.
---
name: smart-pole-instructor
description: Use when a user provides a vague prompt and needs structured clarification to produce a precise…
category: ai
source: zerotohero99/smart-pole-skill
---
# smart-pole-instructor
## When to use
- Use when a user provides a vague prompt and needs structured clarification to produce a precise master prompt. This Sk…
- Use it when the task has clear inputs, repeatable steps, and validation criteria.
## What to provide
- Target material, scope, expected result, and forbidden changes.
- Whether network, commands, file writes, or external services are allowed.
## Execution rules
- Organize steps around “How to use this Skill / The 9 Categories / Locale Sub-dimensions (L1-L4)” and keep inference separate from source facts.
- read files, write/modify files; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding the task.
## Output requirements
- Return the deliverable, key evidence, validation method, and next action.
- Mark missing information as unknown; do not invent commands, platforms, or dependencies.The author source anchors workflow facts; repository files anchor sources and commands; Fluxly only adds fit, limitations, and quality judgment.
skill "smart-pole-instructor" {
input -> user goal + target files + boundaries + acceptance criteria
context -> How to use this Skill / The 9 Categories / Locale Sub-dimensions (L1-L4)
rules -> SKILL.md triggers / order / output contract
runtime -> no special runtime | read files, write/modify files | mostly runs locally
guardrails -> usually needs no extra API key + small-sample validation + diff/log review
output -> copyable result + checklist + next iteration
}SMART POLE Instructor Skill
This Skill implements the SMART POLE framework for prompt engineering. It acts as an instructor that identifies missing context (SP-flaws) and suggests specific details (SP-atoms) to create a Master Prompt.
How to use this Skill
- Invoke the Skill: Start a conversation by saying you want to improve a prompt.
- Provide your Prompt: Paste the vague or draft prompt you have.
- Receive Analysis: The Skill will list the SP-flaws (missing categories). (Note: Models like Claude will use
<thinking>tags for this). - Iterate: Answer the questions or add the atoms suggested.
- Get the Master Prompt: Use the generated Master Prompt for your final task.
The 9 Categories
| Abbrev | Category | Focus | Priority |
|---|---|---|---|
| S | Style | Tone, Persona, Format | 🟢 Accelerator |
| M | Mastery | Expertise level | 🟡 Contextualizer |
| A | Aim | Goal and Success criteria | 🔴 CORE |
| R | Resource | Tools, Constraints, Budget | 🟡 Contextualizer |
| T | Time | Era, Deadlines, Duration | 🟢 Accelerator |
| P | People | Audience, Values, Preferences | 🟡 Contextualizer |
| O | Outline | Structure, Scope | 🔴 CORE |
| L | Locale | Industry, Region, Legal, Cultural | 🔴/🟡 CONDITIONAL |
| E | Example | Samples, Reference styles | 🟢 Accelerator |
Locale Sub-dimensions (L1-L4)
- L1: Industry/Domain (Banking, Healthcare, E-commerce...)
- L2: Geography/Region (Vietnam, EU, Singapore...)
- L3: Legal/Regulatory (GDPR, PCI-DSS...)
- L4: Cultural/Social (Local customs, social norms...)
Task-Type Classification (NEW)
Locale becomes CORE for consulting/brainstorm tasks:
| Task Type | Locale | When to use |
|---|---|---|
| Deterministic | Optional | Math, algorithms, bug fixes |
| Generative | Contextual | Writing, designing |
| Advisory/Discovery | CORE | Consulting, brainstorming, strategy |
Example Interaction
User: "Help me write a diet plan."
Instructor: "Whoa there! That's a classic 'Vague Blob' of a prompt. Let's fix those SP-flaws:
- Category (R): What food do you have? Are you vegan?
- Category (A): Is the goal to lose weight or build muscle?
- Category (T): Is this for a week or a lifetime?
Here are some SP-atoms to add..."
Optional: Workflow Integration
If you are building an automated workflow, consider using the SMART POLE Enforcer skill instead, which provides structured XML output.
Decide Fit First
Design Intent
How To Use It
Boundaries And Review