Plugins

Data quality checking for DeepSeek Harness — profiling, cleaning, and verification pipelines with structured reports.

One-click optimization of a raw instruction into a professional prompt: three output styles, situation-aware role/task/goal profiling, multi-sample selection scored by a model judge, self-iterating learning (session memory + smart defaults + user overrides), /template for 22 sub-scenes with zero model calls, input-box ✨ one-click optimize/undo, prompt_optimize tool, auto-optimize hook, a runtime status page, a settings panel (dsh 0.2.0) and auto-detected zh/en documents.

Performance profiling and LLM cache diagnostics for DeepSeek Harness — context engineering, latency profiling, and cache behavior reports.

Security audit plugin for DeepSeek Harness: static permission profiling and a runtime sentinel for third-party plugins

Data cleaning and data enrichment for CSV/XLSX/JSON enterprise lists in DeepSeek Harness, including spreadsheet cleaning, deduplication, profiling, optional Qichacha MCP and exports.

Per-model capability profiling for DSH: turns local session history into per-model dashboards of session counts, tool usage, error rates, and top error signatures, with time-range filters and multi-model comparison.

Per-answer timing breakdown: splits each reply into model, tool and other time, with first-token, decode and per-tool totals, and keeps a local JSONL ledger. Needs Node 24 or newer in the DSH runtime.

Data analysis toolkit: CSV/TSV/JSON profiling, IQR and z-score anomaly detection, structured summarization with quality issues, and code-free filter/group/aggregate queries.

Request Context Profiler for DeepSeek Harness — see what changed between model requests, and how cache reuse changed with it.

查应用里有没有展示 ICP/APP 备案号、隐私政策入口、运营者信息 —— 都是法定义务

Collects and redacts DSH runtime diagnostics before filing structured issues in GitHub or Gitea.

Algorithm learning toolkit syncing Codeforces and AtCoder submissions, profiling weaknesses, generating AI training plans, and tracking spaced-repetition reviews across a 114-lesson algorithm curriculum.