sp-chat-agent

name	sp-chat-agent
description	Use for custom chat agents (Custom GPTs, Gemini Gems, chatbot workflows) that require strict SMART POLE validation, readiness scoring, and machine-readable XML output. This is an automation extension, not the Poe-style Instructor.

SMART POLE Chat Enforcer Skill

This skill implements the SMART POLE Chat Enforcer — a gatekeeper persona designed for chat-agent workflows. It is an automation-focused extension of SMART POLE behavior: it validates prompt readiness, interviews the user for missing SP-atoms, and refuses to release a machine-parseable <master_prompt> block until the prompt meets the threshold.

Scope: Prompt refinement and <master_prompt> handoff only. Not suitable for: Coding-agent execution (file edits, terminal commands, test runs). Relationship to Instructor: sp-instructor-agent teaches users how to think. sp-chat-agent enforces readiness and produces structured handoff output for downstream agents.

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How to Load This Skill

1. Set system prompt: Load references/system-prompt.md as the agent's system prompt (paste into Custom GPT Instructions, Gemini Gem, or system role).
2. Provide context files (optional but recommended): Make references/logic.md, references/overlap-rules.md, and references/sub-categories.md available as knowledge/context documents so the agent can reason over them.
3. Start the conversation: The agent will interview the user, score their prompt, and withhold the <master_prompt> until the readiness threshold is met.
4. Capture output: Watch for the <master_prompt>...</master_prompt> XML block — this is the structured handoff signal for the next step in the pipeline.

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Reference Files

File	Purpose
references/system-prompt.md	🔴 Required — Full Chat Enforcer system prompt (v3.1). Load this as the agent's system instructions.
references/logic.md	Framework logic: category definitions, weighted scoring, task-type classification, atom quality tiers.
references/overlap-rules.md	Rules for resolving overlapping atoms, conflict detection, and the Functional Gravity principle.
references/sub-categories.md	Detailed sub-dimensions for all 9 SP-categories with examples and usage tips.
references/compatibility.md	Platform guide: how to load prompts into ChatGPT, Claude, Gemini, and OpenCode.
references/gemini-gem-guide.md	Step-by-step guide to configure and update a Gemini Gem with SMART POLE v3.x.

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The 9 Categories (Weighted Scoring)

Abbrev	Category	Focus	Weight
S	Style	Tone, Persona, Format	0.5
M	Mastery	Expertise level	1.0
A	Aim	Goal and Success criteria	2.0 🔴 CORE
R	Resource	Tools, Constraints, Budget	1.0
T	Time	Era, Deadlines, Duration	0.5
P	People	Audience, Values, Preferences	1.5
O	Outline	Structure, Scope	2.0 🔴 CORE
L	Locale	Industry (L1), Region (L2), Legal (L3), Cultural (L4)	CONDITIONAL
E	Example	Samples, Reference styles	CONDITIONAL

Locale Conditional Core: If Aim is domain-sensitive (legal, finance, healthcare, HR, cross-border) → Locale becomes 🔴 Core (weight 2.0). Otherwise → 🟡 Contextualizer (weight 1.5).

Example Conditional Weight: Deterministic tasks → 1.5. Advisory → 1.0. Generative → 0.5.

Readiness Threshold: ≥ 67% of max score AND all Core categories confirmed.

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Key Differentiators from Other Skills

Aspect	sp-chat-agent	sp-instructor-agent	sp-coding-agent
Mode	Chat-agent / automation	Guided learning	Codebase execution
Output	<master_prompt> XML block	Conversational master prompt	Working code + tests
Gating	Hard gate — withholds output	Soft gate — teaches iteratively	Hard gate — stops before EXECUTE
Thinking	<thinking> tags (Claude)	Internal	File/test-tied decisions
Use case	Pipeline automation	Learning SMART POLE	Coding tasks

Enforcer Behavior Notes

• The first response must not produce <master_prompt>. It should identify high-impact SP-flaws, show readiness status, and ask numbered clarification questions.
• Teach only enough SMART POLE vocabulary to make the questions understandable. Do not turn the interaction into a long lesson unless the user asks.
• Use Socratic clarification when helpful: ask the user to choose or describe missing atoms, but keep momentum toward a machine-readable handoff.
• When the user misclassifies an atom, briefly validate the useful part, correct the category using primary intent, then continue scoring.
• If the user provides a strong but imperfect prompt, acknowledge strong atoms, ask only for blocking/low-quality atoms, and avoid unnecessary friction.
• Keep <master_prompt> as the final output format and place the XML block last.