SMART POLE Coding Agent Skill

This Skill implements the SMART POLE framework adapted for AI Coding Agents. Unlike the chatbot versions (Instructor/Enforcer), this skill operates on codebases — scanning project files, extracting context automatically, and ensuring the agent has enough information before writing code.

How to Load This Skill

1. Set system prompt: Load references/system-prompt.md as the agent's system prompt (e.g., paste into AGENTS.md, .claude/system_prompt.md, or the agent's system role configuration).
2. Provide reference files: Make references/logic.md, references/overlap-rules.md, and references/coding-agent-categories.md available in the agent's context or knowledge base.
3. Invoke: Give the agent a vague or specific coding task. The agent will ORIENT (scan the codebase), CLASSIFY the task type, EXTRACT SP-categories, and check execution gates before touching any file.

Reference Files

When to Use This Skill

• A user gives a vague coding task ("fix the login bug", "add pagination")
• Before an agent starts multi-file code changes
• When a task involves unfamiliar parts of a codebase
• Migration or refactoring tasks where scope control is critical

How It Works

1. ORIENT: Agent scans project root (README.md, AGENTS.md, package.json, Dockerfile, etc.)
2. CLASSIFY: Determine task type (Bug Fix / Feature / Refactor / Migration / Infra)
3. EXTRACT: Map request to 9 code-native SP-categories
4. DETECT FLAWS: Identify missing context, overlaps, and conflicts; ask user if critical
5. PLAN: Create implementation plan with file-level scope
6. EXECUTE: Apply file changes within approved scope
7. VERIFY: Run tests, lint, type-check; self-heal on failures

The 9 Code-Native Categories

Category Deep-Dives (SP-Flaw Prevention)

⏱️ T — Time: Urgency Only, Not Quality

T answers: "How much time do I have?" — deadline, sprint slot, hotfix vs planned refactor.

⚠️ T-Flaw (Category Overlap): Do NOT put acceptance criteria or content depth requirements inside T.

• ❌ Wrong: T: "Must be thorough enough to not need follow-up" ← this is an A-atom
• ✅ Right: T: "Hotfix — must ship in 2 hours" / T: "Non-urgent — next sprint"

Quality gates and success criteria belong in Aim (A) or Outline (O), not Time.

👥 P — People: Make Implicit Traits Explicit

P answers: "Who is involved and what are their hidden assumptions?"

Beyond team conventions, always surface:

• Reviewer Persona: Does the reviewer prioritize security over speed? Hate over-engineered abstractions?
• Implicit Values: Prefers functional style? Allergic to ORMs? Values minimal diffs?
• Psychological traits: Team culture (consensus-driven vs individual autonomy), risk tolerance

⚠️ P-Flaw (Implicit People): Do NOT leave personality traits and values implicit — the agent will guess wrong.

• ❌ Wrong: Omitting that the team hates any types in TypeScript
• ✅ Right: P: "Reviewer flags every use of 'any' in TypeScript — use strict types always"

🎓 M — Mastery: Always Split Domain vs Task

M answers: "What does the developer know — and in which dimension?"

Always decompose Mastery into two parts:

⚠️ M-Flaw (Mastery Scope): Missing the gap between Domain and Task mastery causes the agent to pitch solutions that are technically fluent but practically inappropriate.

• ❌ Wrong: M: "Senior Developer" ← domain only, no task dimension
• ✅ Right: M-Domain: "Senior React" | M-Task: "Novice — first time implementing OAuth2 flow" → agent uses familiar React patterns but explains OAuth2 step-by-step

🧰 R — Resource: Tools + People + Data

R answers: "What weapons do I have — and what is forbidden?"

Resource includes three dimensions, not just software:

⚠️ R-Flaw (Resource Underutilization): Listing only software tools leaves the agent unable to suggest "ask the security team" or "check the Confluence runbook."

• ❌ Wrong: R: "Jira, Notion" ← tools only
• ✅ Right: R-Tools: "Jira" | R-Network: "Security team available for review" | R-Data: "Sentry logs + Redis CLI access"

Also supply Negative Atoms (what is forbidden):

R-Forbidden: "No new npm deps without approval, no changes to DB schema"

📌 E — Example: The Anchor Atom

E answers: "What should the output look like — concretely?"

In coding context, a Gold Standard E-atom is a matched pair:

⚠️ E-Flaw (Missing Anchor): Without an Example atom, the agent mimics its training data defaults, not your codebase conventions.

• ❌ Weak: E: "Make it look clean" ← too vague, gives agent no anchor
• ✅ Gold: E: "Follow the pattern in auth_service.py:L45–80 — token invalidation before session update" or E: "This Sentry trace shows the failure chain: A→B→C"

Always ask: "Is there a similar PR, a log trace, or a code section I want the agent to mimic?"

Auto-Extraction Sources

Coding Task Classification

Task Taxonomy Mapping (Generic → Coding)

Use this mapping to bridge generic SMART POLE task types in references/logic.md with coding-agent task types.

Execution Gates (Hard Stops)

Do not execute code changes until all hard-stop gates pass:

1. Gate A (Aim): At least one testable acceptance criterion exists.
2. Gate O (Outline): Authorized scope is explicit (or defaults to minimal scope) and forbidden scope is respected.
3. Gate Conflict: All SP-conflict items are resolved by user decision.
4. Gate Overlap: Apply One Atom, One Slot from references/overlap-rules.md; no double-counted atoms.
5. Gate Score: Weighted readiness score is >= 67% of the applicable max score (per references/logic.md task-type weighting).

If any gate fails:

• Stop before EXECUTE
• Ask targeted clarification questions
• Re-plan only after user confirmation

Worked Example: Bug Fix Context Extraction

User request: "Fix the login bug — users can't login after password reset"

🔍 SP-flaw check: "Write unit tests" is an A-atom (acceptance criterion), NOT a T-atom (time/urgency).

Key Differences from Chatbot Versions

Optional: Integration with Other Skills

This skill works best as a pre-flight layer in any coding agent workflow, ensuring sufficient context before execution begins. Pair with project-specific AGENTS.md or workflow files.

Source: cuongpt083/smart-pole-skill — distributed by TomeVault.