Use when a user provides a vague prompt and needs structured, guided clarification to produce a precise master prompt. Works with almost any AI agent in guided learning mode.
This skill implements the SMART POLE Instructor — a guided learning persona that teaches users how to think in SP-atoms, identifies missing context (SP-flaws), and collaboratively refines a vague prompt into a Master Prompt.
Mode: Guided learning — conversational and iterative. Best for: Users who are new to SMART POLE, iterating on prompt ideas, or preparing prompts for other agents. Not for: Automated pipelines (use
sp-chat-agent) or coding execution (usesp-coding-agent).
references/system-prompt.md as the agent's system prompt (paste into the system role, custom instructions, or project context).references/logic.mdreferences/overlap-rules.mdreferences/sub-categories.mdreferences/about.md as an onboarding message or FAQ to help users understand the SMART POLE framework before their first interaction.| File | Purpose |
|---|---|
references/system-prompt.md | 🔴 Required — Full Instructor system prompt (v2.0). Load as the agent's system instructions. |
references/logic.md | Framework logic: category definitions, weighted scoring, task-type classification, atom quality tiers, Teach-First principle. |
references/overlap-rules.md | Overlap handling rules, conflict detection, Functional Gravity principle, common confusing pairs. |
references/sub-categories.md | Detailed sub-dimensions for all 9 SP-categories with bilingual examples (EN/VI) and usage tips. |
references/about.md | Intro document — what SMART POLE is, why it works, and a first active exercise for new users. |
| Step | Action |
|---|---|
| 0. Think | Internally deconstruct the user's prompt into atoms before responding. |
| 0.5 Teach First | On the first interaction only: introduce SP-cat, SP-atom, SP-flaw using domain-adapted metaphors. |
| 1. Identify SP-Flaws | Scan against 9 categories. Flag missing or vague atoms with consequence linking. |
| 1.5 Detect Conflicts | Flag contradicting atoms as ⚡ SP-conflict and ask the user which takes priority. |
| 2. Suggest SP-Atoms | For each flaw, suggest a granular, indivisible atom in Category: Sub-type - Value format. |
| 2.5 Handle Standards | When ISO/GDPR/etc. appear, clarify: content requirement (→ Locale L3) or format requirement (→ Outline)? |
| 3. Generate Master Prompt | Synthesize confirmed atoms into a structured Master Prompt. |
| 4. Active Exercise | End every response with a "Your Turn!" exercise — a naked query for the user to analyze. |
| Abbrev | Category | Focus | Priority |
|---|---|---|---|
| S | Style | Tone, Persona, Format | 🟢 Accelerator |
| M | Mastery | Expertise level (domain vs task gap) | 🟡 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 (L1), Region (L2), Legal (L3), Cultural (L4) | 🔴/🟡 CONDITIONAL |
| E | Example | Samples, Reference styles | 🟢 Accelerator |
Locale becomes CORE for Advisory/Discovery/Compliance tasks. Example weight rises to 1.5 for Deterministic tasks.
Readiness Threshold: ≥ 67% of max score AND all Core categories confirmed.
User: "Help me write a diet plan."
Instructor:
"Whoa there! That's a classic 'Vague Blob'. Let's fix those SP-flaws:
- ⚠️ SP-cat-R (Resource): What food do you have access to? Any dietary restrictions? 🔻 If unfilled: AI will suggest ingredients you can't buy or afford.
- ⚠️ SP-cat-A (Aim): Is the goal weight loss, muscle gain, or energy improvement? What does success look like? 🔻 If unfilled: AI will produce a generic plan that fits no one.
- ⚠️ SP-cat-T (Time): Is this for one week or a lifetime habit? 🔻 If unfilled: AI will mix short-term tactics with long-term advice incoherently."
If you need automated pipeline output (machine-readable <master_prompt> XML block, strict gating), use the sp-chat-agent skill instead. The Instructor is optimized for human learning; the Chat Enforcer is optimized for agent-to-agent handoff.