prompt-optimizer
SolidUse when asked to improve, optimize, rewrite, tune, or port a prompt, skill, or tool description, to build prompt evals, or to audit prompt text for dated instructions. Optimize mode returns a shorter prompt validated on holdout cases; audit mode returns a confidence-ordered findings report and a proposed diff, and applies nothing.
Install
Quality Score: 82/100
Skill Content
Details
- Author
- OutlineDriven
- Repository
- OutlineDriven/odin-claude-plugin
- Created
- 10 months ago
- Last Updated
- yesterday
- Language
- Python
- License
- Apache-2.0
Integrates with
Similar Skills
Semantically similar based on skill content — not just same category
dhpk-prompt-optimize
Rewrite a raw task prompt into a model-aware, effort-calibrated version before you run it. Use when: the user asks to "optimize this prompt", "improve this prompt for Claude", "what effort should I use", "make this a good API prompt/template", or pastes a rough instruction before a big Claude Code or API task. Detects the target model (current session or a named one), classifies task complexity, recommends an effort level (low/medium/high/xhigh/max, plus the dhpk agent-frontmatter/Claude Code equivalent), asks up to 4 AskUserQuestion questions only for missing required info, then applies model-specific behavioral rewrites. Not for: generic few-shot/CoT/template technique coaching with no model or effort selection (use prompt-engineering-patterns), or auditing token/cache budget (use dhpk-harness-budget). Output: optimized prompt block + effort recommendation with rationale + bullet list of rewrites applied.
prompt-optimizer
Optimize LLM prompt templates against an eval harness. Works with autoresearch for overnight prompt optimization. For projects that call LLMs (try-on prompts, chatProxy, MCP tools).
optimize-prompts
Use this to improve a prompt systematically instead of hand-tweaking it by feel. Trigger on "optimize my prompt", "make this prompt better", "the prompt isn't working well", "auto-tune my prompt", "few-shot example selection", or when prompt quality has plateaued. Optimize against an eval set with a method, and let the numbers pick the winner.