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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

1. Identify whether the request clearly matches core-coding-standards scope and triggers.
2. Apply the skill rules and referenced guidance to produce a concrete result.
3. Validate output quality against constraints; if gaps remain, refine once with explicit assumptions.

Weekly Installs

54

Repository

ravnhq/ai-toolkit

GitHub Stars

11

First Seen

Feb 3, 2026

Security Audits

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Installed on

github-copilot54

opencode53

codex52

kimi-cli52

gemini-cli52

amp49