Principles

• Keep it simple (KISS) — prefer the simplest solution that works

• Don't repeat yourself (DRY) — extract when you see three duplicates, not before

• Single Responsibility — each module/function does one thing

• Use descriptive, intention-revealing names

• Use kebab-case for files and folders

• Functions should have clear inputs and outputs with minimal side effects

• Keep functions right-sized — extract when logic needs a comment to explain

• Delete dead code — don't comment it out

• Never swallow errors silently

• Measure before optimizing — no premature performance work

• No premature abstraction — wait for three concrete duplicates before extracting

Rules

See rules index for detailed patterns.

Examples

Positive Trigger

User: "Review this service and remove duplication while keeping behavior unchanged."

Expected behavior: Use core-coding-standards guidance, follow its workflow, and return actionable output.

Non-Trigger

User: "Generate a one-off product marketing tagline."

Expected behavior: Do not prioritize core-coding-standards ; choose a more relevant skill or proceed without it.

Troubleshooting

Skill Does Not Trigger

• Error: The skill is not selected when expected.

• Cause: Request wording does not clearly match the description trigger conditions.

• Solution: Rephrase with explicit domain/task keywords from the description and retry.

Guidance Conflicts With Another Skill

• Error: Instructions from multiple skills conflict in one task.

• Cause: Overlapping scope across loaded skills.

• Solution: State which skill is authoritative for the current step and apply that workflow first.

Output Is Too Generic

• Error: Result lacks concrete, actionable detail.

• Cause: Task input omitted context, constraints, or target format.

• Solution: Add specific constraints (environment, scope, format, success criteria) and rerun.

Workflow

• Identify whether the request clearly matches core-coding-standards scope and triggers.

• Apply the skill rules and referenced guidance to produce a concrete result.

• Validate output quality against constraints; if gaps remain, refine once with explicit assumptions.