ztl34245881-commits/dsh-task-planner

README excerpt

dsh-task-planner Task planning with experience muscle-memory for DeepSeek Harness ( dsh ). Give a task → the agent recalls past similar solutions (condition reflex), evaluates whether they fit, and produces a dynamic plan matched against its capabilities — never hard-coded combos . Every plan auto-drafts a lesson into the experience library; when the task closes, the agent updates the outcome. The more you work, the smarter the reflex. Features - 🧠 Experience library ( task memory save/recall/list ): persistent lessons as plain Markdown with signature keywords. Recall uses a 2–3-char sliding-window tokenizer, so "weekly report" still hits a "daily report" lesson. - ⚡ Condition-reflex planning ( plan task ): recall → LLM evaluates fit (reuse & improve, or explain why not and plan fresh) → decomposed steps with capability matching → risks → next actions. - 🤖 LLM-driven, not rule-driven : the model decides what to use per task; the plugin only supplies context (past experiences + optional capability catalog). - ✍️ De-AI deliverable standard : any textual output step (docs/sheets/slides/copy/scripts) must include a humanize-then-review pass before delivery. - 🗂️ Auto-persist : plan …

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