Plugins

ShiGuang Memory Engine (SGME) bridge: multi-agent shared long-term memory via HTTP — L0/L1/L1.5/L2 distillation, scenario-based injection, unified search, and proactive care signals (memory_search / wiki_search / signal_pull / signal_claim / signal_ack), installable as `dsh-sgme`.

Team knowledge base for offline knowledge sharing (FDE box scenario): browse, full-text search and refinement entry points; raw material is queued automatically and distilled into wiki pages by a serial bot session (Karpathy LLM Wiki pattern: immutable raw, two-step refinement, log stream, monthly lint).

Learning Mode agent preset: a coding agent that teaches while coding — concrete scenario-grounded explanations, Socratic guidance, and TODO(你) practice blanks, modeled on Claude Code's Learning output style; installable via dsh plugin add (dsh-learning-mode on npm).

AI interview practice for DeepSeek Harness Web: generates questions across four modes (concept review, mock interview, scenario, LeetCode Hot 100), scores answers, and provides explanations.

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.

AI can write resume text, but templates may still look wrong, pages may overflow, whitespace may remain, and small edits can destabilize the layout. dsh-resume provides an end-to-end DSH workflow for truthful content refinement, template selection, A4 layout tuning, visual review, and tailored resume versions for different job-search scenarios.

Data asset registration helper: runs a disallowed-scenario and ownership-dispute pre-check, matches a registration agency by data type and region, and returns a registration checklist with the triple-audit standard and seven-step flow.

Learning Mode agent preset: a coding agent that teaches while coding — concrete scenario-grounded explanations, Socratic guidance, and TODO(你) practice blanks, modeled on Claude Code's Learning output style; installable via dsh plugin add (dsh-learning-mode on npm).

Layered long-term memory for DeepSeek Harness: each turn injects a persona frame plus a relevance-ranked memory index while full transcripts stay on disk and are expanded on demand, so the vault never floods the context window. Facts are slot-keyed, so a changed mind retires the old value instead of recalling two contradictory answers. The persona frame is derived from memory by the model itself, behind three anti-pollution gates (rejecting development chatter, blocking echoes of its own injection, and merging restatements by shared content words). Periodic consolidation merges near-duplicates, rolls long topics into digests, and archives low-value old entries by tagging only — raw text is never deleted. The append-only store is compacted in place (measured 4.7 MB to 355 KB). Zero third-party dependencies, no build step. Note: per-turn injection inevitably lowers prompt-cache hit rates, so this plugin is not recommended for work scenarios.

Audible feedback for the DSH web interface: button presses are classified by semantics (normal, primary, toggle, send, close, danger, link) and the current session's task transitions (started, finished, failed) plus any pending interaction play a cue, each scenario with its own cue picker and switch on a settings page. All 78 cues and 12 sound packs are synthesised in the browser with the Web Audio API, so the package ships no audio files and makes no network request. A community port of dsh-plugin-uisfx to DSH 0.2.0, with the ported changes recorded in PROVENANCE.md.

Bundles five TIA Portal skills (Siemens PLC docs lookup, ladder-logic generation, SimaticML-to-PNG rendering, scan-cycle simulation with PASS/FAIL scenarios, and a TiaLink gateway skill for driving a real TIA Portal project) and wires five MCP endpoints. Requires TiaLink (paid; Windows + TIA Portal V19/V20 + Openness) for real-project work, API keys for the four cloud services (100 free calls each, then paid; rendering is billed per call even on failure), Node 22+, and network access; program snippets are uploaded to tia.hubwiz.com.

Five-layer intent recognition pipeline (rules, pgvector retrieval, Jev scoring, fine-tuned arbitration, LLM fallback) with scenario management UI, PostgreSQL storage and distributable API services.

场景配置插件：不同插件组合即不同场景，统一对插件开启关闭（配置页自动发现已安装插件并按场景热切换）；模型由用户在会话窗口按需选取

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.

Read-only assessment of whether a Dameng DM8 database can be recovered and to which point (LSN or time), across 16 recovery scenarios, with an HTML report. Needs Java 8+ and a Dameng JDBC driver jar (DmJdbcDriver8.jar) that you provide yourself.

Live token cost for every chat in the DSH Web UI: the chat, its subagents and the whole session tree, priced from a bundled models.dev snapshot of seven providers and 139 priced models, with cache read and write rates wherever the provider publishes them and DeepSeek from its own peak and off-peak table. Every finished answer carries its own price in the transcript, and a chat that is not currently open is priced from the log instead of disappearing. Spend is costed per interval from an append-only JSONL ledger in the project folder, so logged work keeps the tariff it was billed at. On top of it: budget-aware work plans packed against a money limit, routing scenarios that show which models are worth connecting, a generated plan document in English, Chinese or Russian, and a warning when spend belongs to no plan unit.

Spanish practice panel for DeepSeek Harness: six locally graded drill types (verb conjugation across 11 tenses, noun gender, plurals, vocabulary recognition, sentence cloze, ser/estar and subjunctive grammar points) plus a model-graded scenario-writing tab, 8-level spaced repetition, and four agent tools. Answers are computed and graded inside the plugin, so the model is only used for teaching and explanations.

Yijing-based decision review plugin for DSH: six-layer weighted aggregation engine (384 yao to taiji) with strong-association veto rules, pure JS with zero solver dependencies. Free tier is capped at 9 runs per day and activation codes raise it; the stock-trend scenario pulls live quotes from Tencent for the stock codes in the request.

Mounts the tsm-core-dlr semantic service (LanceDB vectors + graph) as MCP tools and ships the scenario-independent DLR/TSM modeling skill, a service-status overlay and the agent rules; the service is a hard dependency and must be running before the bundle activates.

Skill dossier + work report for DSH: browse, search and archive each skill (direction, scope, boundaries, scenarios), review or delete it, and roll daily briefs into daily, weekly and monthly reports.

An agent preset that turns a one-line idea into requirements, tech plan and maintenance baseline — every step is restated and confirmed by you before proceeding. No scenario presets: 73 atomic skills (6 master skills + a verify dispatcher + 66 sub-skills) load by context.

Routes dsh requests by conversation scenario and retries with fallback models when requests fail.

Scenario bundles of dsh plugins (插件界的 dotfiles): a recipe tool that lists, searches, applies and composes ready-made plugin environments with ordered install sequences.

Beginner starter pack for DeepSeek Harness: welcome flow, 0→1 learning path, scenario-based plugin recommendations and a self-check checklist, paired with the dsh-handbook-zh Chinese tutorial repo.