llm-cost-optimizer
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skill-optimizer
Optimizes AI skills for activation, clarity, and cross-model reliability. Use when creating or editing skill packs, diagnosing weak skill uptake, reducing regressions, tuning instruction salience, improving examples, shrinking context cost, or setting benchmark/release gates for skills. Trigger terms: skill optimization, activation gap, benchmark skill, with/without skill delta, regression, context budget, prompt salience.
prompt-repetition
A prompt repetition technique for improving LLM accuracy. Achieves significant performance gains in 67% (47/70) of 70 benchmarks. Automatically applied on lightweight models (haiku, flash, mini).
effective-prompting
Master effective prompting techniques for Claude Code. Use when learning prompt patterns, improving task descriptions, optimizing Claude interactions, or troubleshooting why Claude misunderstood a request. Covers @ mentions, thinking keywords, task framing, and iterative refinement.
custom-slash-commands
Guide for creating custom Claude Code slash commands - shortcuts for frequently-used prompts and workflows. Use when creating reusable prompts, automating common tasks, or building team workflows. Covers frontmatter, arguments, bash execution, and namespacing.
prompt-authoring
Guidance for creating effective prompts, chains, and gates using CAGEERF methodology
