Plugins

High-reliability Feishu/Lark bridge for DeepSeek Harness: QR one-click auth, card-based commands and intent-confirmation cards, at-least-once zero-loss outbox, media in/out, /doctor session-log ZIP, and a reusable DSH Web GUI that lands sessions in the right workspace.

Usage statistics settings panel: 26-week activity heatmap, daily token trend with a cache hit-rate curve, per-model donut and breakdown, time-range filters, summary cards, one-time historical backfill and lossless rebuild.

Reversible context slimming for DeepSeek Harness: TOON-style lossless tabular re-encoding of uniform JSON arrays, stride-sampled table windows with line numbers, and cross-turn dedup for repeated tool output; every replacement passes a byte-plus-token gate (≤72% bytes, strictly fewer estimated tokens) and the original is spilled to disk behind a `[save-token #id]` notice retrievable in one call via the `save_token_expand` tool, with billed and avoided token metering on a settings dashboard.

Token-cost pipeline for DeepSeek Harness: pressure-aware layered compaction (lossless projection below 40% context pressure, LLM summarization above), per-agent tool allowlisting, long-text-to-image with vision summarization, and a /token-status command tracking pressure, tiers, and saved tokens.

An editable diagram canvas in the DSH sidebar plus two agent tools (diagram_read, diagram_apply) that read and edit the workspace in place, using native .drawio files: the canvas opens and saves them losslessly — cells it does not understand are preserved byte-for-byte — and a file-fingerprint revision powers optimistic locking against concurrent edits.

Automatic context compression for DeepSeek Harness: compresses tool outputs before they reach the model and keeps every lossy compression reversible via CCR retrieval tools.

Codex-style context window auto-management for DeepSeek Harness: a lossless rollover engine with zero summarization model calls, task-oriented handoff anchors, working notes that survive compaction, verbatim recall of archived windows, and a stale-notes gate that downgrades to an explicit warning instead of vouching for outdated notes. One-click composer button plus /smart-compact command, three-band automatic rollover, mandatory anchor sanitization; works out of the box with zero configuration.

Lossless context folding for DeepSeek Harness: the oldest span is replaced by a brief, and the originals stay in the session log.

Auto-resume for interrupted agent sessions: an ordered rule table routes by failure type (rate limit, quota, auth, context overflow, crashed orphan) to automatic backoff retry, model switch, resume after context compaction, or a stop-loss notification, with a visual rule editor and a full activity log.

LLM-free lossless* compaction engine for DeepSeek Harness

Matt Pocock's 25 skills fully translated to Chinese (technical terms kept in English with glosses).

DeepSeek Harness plugin that aggressively saves tokens without affecting task quality: lossless head/tail truncation of tool results (default), grep-result folding, pressure-triggered dsh-compaction-basic delegation, and an LLM-summary CompactionEngine subclass for the aggressive mode. Saves 97.6% of input tokens on an 8-scenario synthetic suite, with all reductions routed through dsh event flow so model-visible = logged stays intact.

Large-image recognition for vision-exp models: lossless 800×800 tile recognition (smart/pipeline/full), local OCR with preprocessing & handwriting routing, optional multi-vendor GPU (DirectML/CUDA/OpenVINO) with auto CPU fallback.

Full-pipeline Chinese translation for the dsh web GUI: live translation of the model reasoning as it streams, in-place Chinese below each English reply, and one-line Chinese summaries for tool outputs and tool-call glosses — a standalone provider request that never enters the session transcript.

Insurance actuary — runs real actuarial calculations (life & P&C reserve valuation under C-ROSS II BEL+RM, profit testing NBV/VNB/IRR, embedded value EV, pricing & loss-triangle reserving), not just explanations.

Decision-and-goal memory with lineage: explicit commands and a model tool record decisions deterministically, a status card re-anchors them across compaction, a lossless event stream with BM25+jieba retrieval backs it, and a scenario-level fidelity regression suite gates releases.

Registers the remove-pdf-background-gray skill for DSH: whitens gray/off-white scan backgrounds in image-based PDFs while preserving resolution, page geometry, and anti-aliased text edges (lossless Flate write-back), via a single Python script (pypdf + Pillow + numpy).

Professional translation for DSH: 18 target languages, 4 styles (formal/casual/technical/literal), glossary-controlled terminology, and whole-file translation (README/docs/subtitles) with Markdown/code-block format preservation; powered by DeepSeek via SiliconFlow.

Host-side macOS notifications for questions, approvals and finished replies — immune to hidden-renderer notification loss, with icon, sound and click-to-raise.

A Genshin Impact Hu Tao (Wangsheng Funeral Parlor) skin for the dsh web GUI - a 14-token ink-black, crimson and amber palette, falling plum petals and spirit butterflies, a plum-blossom brand mark, and a floating petal toggle that tracks the sidebar.

Session Steward for DSH: browse and bulk-prune the official session archive set, and run a four-gate health checkup (log integrity, projection cache, lossless JSON, cold read) that ends in a reversible, backup-first prescription. Never rewrites session logs.

WeChat login watchdog for DSH: surfaces dsh-wechat session loss as a Web UI banner instead of silently dropping queued messages. AI-generated, educational use only.

Approval attention plugin for the DSH Web GUI — audible + visual alerts (chime, tab badge, OS notification, dock) for pending approvals, session completions, job failures, and connection loss.

Registers the remove-pdf-background-gray skill for DSH: whitens gray/off-white scan backgrounds in image-based PDFs while preserving resolution, page geometry, and anti-aliased text edges (lossless Flate write-back), via a single Python script (pypdf + Pillow + numpy).