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Optimize a raw instruction into a professional prompt through the harness llm service — four-section (Role / Task / Context / Format) or a headerless plain-text style. Situation-aware (1.3.0+): the instruction and conversation are parsed into role / task / goal profiles, so the role stays on-task and goals/constraints are preserved (goal-alignment retry, per-session goal carry-over); roles follow an identity + capability + behavior formula with per-task-type writing tips (1.3.3-1.3.5). Context-aware by default; long outputs resume with auto-expanded budgets; validated results are cached (identical requests cost zero model calls); stream early-stop and an optional fast profile cut latency (1.3.6). Composer ✨ one-click optimize / undo / cancel with a token-cost hint; prompt_optimize tool (optimize + iterate), auto-optimize hook, auto-detected zh/en role document.
Inside DeepSeek Harness, with dsh-market
dsh plugin --profile web add dshmarket
Or from the command line
dsh plugin --profile web add oss-prompt-optimizer
Installing runs third-party code with your own permissions — it can read your files, use your credentials and reach the network. Review the source first, and pin a commit (github:owner/repo#sha) when you can.