smart-pole-instructor

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Author repo smart-pole-skill

Interpretation is structured for decision-making; original keeps the upstream SKILL.md unchanged.

Decide Fit First

Core job: Use when a user provides a vague prompt and needs structured clarification to produce a precise master prompt. This Skill implem…
Best fit: Use it when the task has reusable inputs, steps, and validation criteria rather than a one-off answer.
Avoid forcing it: If the source lacks commands, platform support, or external-service evidence, keep those fields unknown instead of guessing.

Design Intent

Structure: The skill is organized around “How to use this Skill”, “The 9 Categories”, “Locale Sub-dimensions (L1-L4)”, “Task-Type Classification (NEW)”, showing how the author expects the agent to judge fit, collect context, and produce verifiable output.
Trigger evidence: Prioritize the author’s wording around when to use it, what context to collect, and what output shape to produce.
Evidence boundary: Author text states facts, repository files prove commands and paths, and Fluxly only adds fit, limits, and usage judgment.

How To Use It

Inputs: Provide target material, scope, expected result, forbidden changes, and validation method.
Invocation: Name smart-pole-instructor directly; if the source includes slash commands, start with the command and then add task context.
Validation: Start small and check whether the result follows “How to use this Skill / The 9 Categories / Locale Sub-dimensions (L1-L4)” before expanding.

Boundaries And Review

Dependencies: It usually needs no extra API key, so start with a small validation task.
Permissions: Declared permissions include read / write; ask the agent to state file, command, and rollback boundaries before acting.
Quality bar: A useful result names the deliverable, evidence, and next action. Generic prose means the task needs tighter context.

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.

Fluxly profile Author and license come from source; runtime, permissions, and network are Fluxly detections or estimates

Fluxly category: AI
Author-declared agents: No explicit declaration found; this is not inferred or tested compatibility
Static check: 88 / 100 · heuristic scan, not runtime safety proof
Author / version / license: @zerotohero99 · no license declared
Fluxly token estimate: Lean
Fluxly setup estimate: Plug-and-play
External API key: No requirement detected
Detected OS requirements: Unspecified
Runtime requirements: Unspecified
Detected file/system behavior: Read-only
Write / modify
Detected network behavior: Local-only
Install commands: None (reference only)

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。

Output preview smart-pole-instructor.preview

The current SKILL.md does not define a fixed output example.

SKILL.md SKILL.md

# 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

1. **Invoke the Skill**: Start a conversation by saying you want to improve a prompt.
2. **Provide your Prompt**: Paste the vague or draft prompt you have.
3. **Receive Analysis**: The Skill will list the **SP-flaws** (missing categories). (Note: Models like Claude will use `<thinking>` tags for this).
4. **Iterate**: Answer the questions or add the atoms suggested.
5. **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 |

…

Evidence boundary and execution chain

Author text anchors workflow facts; Fluxly only indexes current sections, terms, files, and commands.

sections -> How to use this Skill → The 9 Categories → Locale Sub-dimensions (L1-L4) → Task-Type Classification (NEW) → Example Interaction → Optional: Workflow Integration
terms    -> SMART POLE · SP-flaws · SP-atoms · Master Prompt · Invoke the Skill · Provide your Prompt · Receive Analysis · Iterate
files/cmd -> <thinking> · Industry/Domain · Geography/Region · Legal/Regulatory · Cultural/Social · consulting/brainstorm · Advisory/Discovery
body sha256 -> fc04312cc01b