Most bad agent outcomes are bad prompts: underspecified goals, missing constraints, no definition of done. sharpen is a prompt-engineering coach that turns a rough request into a precise one before the expensive run starts. It works in Claude Code, Cursor, Codex, and Gemini CLI.
npx skills add arjunlohan/sharpenHow it works
The core idea is that a prompt is a map of unseen territory, so the skill scouts before it asks. It reads the repo to answer its own questions (never interviewing you about things grep can answer), asks only the few questions whose answers change the architecture, then rewrites the prompt with explicit intent, structure, verifiable done-criteria, and a verification step.
The last pass is model tuning. The same task should be prompted differently for different models: some want brief goal-level intent with the why, others want literal, explicitly-scoped instructions. sharpen writes for the model that will actually execute.
Proof, not claims
The showcase shows the skill's work directly: real prompt rewrites with the diffs marked, the same prompt tuned for two different Claude models side by side, and A/B experiments where you can compare the UI built from the rough prompt against the one built from the sharpened prompt: the sharpen showcase.
Why it matters
Prompts are the specs of the agent era, and most of them are written the way bad PRDs were written in 2015. Encoding a repeatable method for making them good, as a skill any agent can load, is the same job as building eval-gated AI products: define quality, make it checkable, ship the judgment.