Plugins

Adds the Miaoshou (Meituan CatPaw) desktop client as a DeepSeek Harness model provider. The 12 models mirror the ones the Miaoshou app picker shows, each carrying its credit-cost multiplier; thinking levels are translated into the upstream modelParams; a read-only panel reports available credits and the current plan. Sign-in is a QR link, so the desktop client does not have to be running.

Right-sidebar GitHub panel with four rising-repository lists — created within 2 days, 7 days, 30 days, and 20k+ stars still pushed — where the first three are ranked by star velocity (stars per day) rather than total stars, so repositories that gained traction fast surface above older ones with more stars. Each row shows the repository name, its own description, language, the stars-per-day rate and up to two topics, and links to the repository. A keyword search box queries the GitHub Search API directly, debounced and IME-aware so a Chinese or Japanese composition costs one request instead of one per keystroke. Reads the public GitHub Search API anonymously: no token, no login, no account, nothing else contacted. Lists cache for 30 minutes and search results for 5; outbound requests go direct first and fall back to a listening local proxy port only if direct fails.

Sidebar leaderboard for DeepSeek Harness (DSH): LMArena blind-test ratings, Artificial Analysis intelligence index, LiveBench 2.0 capability, OpenRouter weekly/monthly usage and market share, price, speed and context — plus a local token-usage dashboard built from your own session logs (tokens, estimated cost, per-model ranking), model detail drawers and CSV export. Daily snapshot, instant open, no API key; locally configured models are highlighted.

Picks each subagent model from the local catalog by task category, unless the caller already named one. Free and quota tiers are ranked together by per-model capability tags; a pricier model is used only when no cheap model is tagged as good at that kind of work. Only providers whose remaining balance is readable, or that are account-backed, enter the candidate pool; the rest stay listed but are not routed. A panel adds per-model manual priority above the automatic order plus allow/revoke controls.

K-ON! themed tea-table widget: click it to see the DeepSeek balance, today's spend and peak-hour state; each finished turn pops a cost bubble; double-click shows zh/ja character lines.

Live CNY cost of the current session under the composer, priced per assistant message by the model that produced it and the peak or off-peak window of its own timestamp, with a Settings page that edits the six prices of every model and marks a model with no rate card with a leading tilde because it is costed from the Flash rates.

Project-level usage hub for DSH: per-project token, time and cost, with half-hour swimlanes, 26-week activity heatmaps and provider balance alerts; unpriceable usage is flagged rather than estimated.

多模型圆桌辩论插件：让 DSH 里已配置的多个 AI 带着各自人设围绕同一话题多轮互喷，共享上下文，裁判按六维（攻击性/逻辑/人设还原度/趣味度/策略/深度）加权打分，性价比另用 dsh-cost-meter 真实账本单列，输出 Markdown 战报与 docx。Multi-model roundtable debate plugin for DeepSeek Harness.

Answers real-life decisions from a 765-entry Chinese life guide: looks up entries first, ranks by cost and benefit, and cites section and item.

Manage MCP servers and skills in DeepSeek Harness, and hide tool groups from the next prompt with token-cost estimates.

Context governance for DSH: folds oversized tool results, collapses the tool surface and keeps the prompt cache warm, so long sessions stop getting more expensive.

Per-turn tool cost line in the chat stream: after each turn a collapsible row under the reply lists which tools ran, how many times, and how many tokens each tool injected into the context (input plus output), expandable to per-tool detail.

Shows your DeepSeek balance and what the current session has cost in a corner overlay, with budget alerts, peak-hour warnings and opt-in official price sync.

Finds wasted GitHub Actions runner time and proposes small, reviewable workflow patches, with explicit approval before any edit and a DSH settings page for analysis rules, history limits, storage, and cost estimation.

Always-on quotes strip pinned to the top of the DSH window: watchlist, holdings with share counts and cost price, P&L, candlestick and intraday charts, 5-level order book, trade ledger, price alerts, and streamed AI review of the selected symbol.

Long-term memory scoped to the DSH workspace. A session opens with a one-line-per-memory index - bodies are fetched on demand by the `memory` tool, so the same character budget holds about three times as many entries - and when a turn ends a purely local search over the last user message pulls the matching memories into the next briefing at no model cost. Optional auto-capture distils a stretch of conversation into structured memories; in draft mode they wait in an approval queue instead of landing directly. A portrait layer keeps a workspace and a global picture, rewritten from the journal and the records, with the same draft-or-direct choice. Settings → Memory holds the archive, list and timeline views, a health check (duplicate clusters, stale entries, broken links), the supersede version chain with rollback, and a read-only scan that imports existing WorkBuddy / Qoder memory files.

Registers an apiCost session projection that estimates DeepSeek spend from reported token usage at published peak / off-peak rates, in CNY and USD, over self, delegation-tree or verified-team scopes: a composer-dock pill and its detail panel, the /cost command, a session_cost tool, and GET routes for the view and the current rate card. A panel switch parks model requests at the llm/stream waterfall while peak rates apply and dispatches them unchanged once the off-peak window opens, so the call is billed at the off-peak rate with no failure reported to the session.

Shows what each completed turn cost in CNY on its own row under the turn, with a click-to-open breakdown naming the tokens per category, the unit rate, the matched price rule and the billing instant; per-model rates are edited on a settings page inside DSH — four token categories plus weekly recurring peak/valley rules with arbitrary multipliers and absolute time-window promo prices that override them. 0.2.3 adds account awareness: it reads the balance or remaining quota of 14 providers (DeepSeek, OpenRouter, Zhipu GLM, Moonshot, MiniMax, Kimi Coding, OpenAI-compatible relays and more) and shows today / this month / this session spend next to it; spend is accumulated in a local ledger (atomic writes, one book per API key, 1-24 month retention, JSON export), and a monthly calendar lets any day be corrected by hand - including days with no balance observation - with an optional note. Usage and rates are computed locally from what the provider reported; balance is read straight from the provider API with your own key, and nothing is sent to a third party.

Composer-dock badge showing DeepSeek peak/off-peak pricing on a Beijing-time calendar whose China holiday schedule is fetched online at runtime (off-peak costs half the peak rate); auto-hides for non-DeepSeek models and cycles seven readouts on click.

Long sessions get expensive, and the platform compacts the context on its own. This plugin adds an occupancy badge (hover to see how it is computed), writes a handover draft into HANDOVER.md from 45% — files changed, money spent, what recent turns were about — and reminds you once at 60% to wrap up. On compaction it records the reading from just before, so you can check it later. No model calls, nothing added to the conversation; it does not prevent compaction.

Context fullness bands, the per-session compaction threshold derived from the model window, per-step cost computed from cache read and cache write rates, cache-hit, DeepSeek balance with the peak/off-peak tariff, and a one-click handoff. It never calls a model and spends no tokens.

Manage skills and MCP servers from one DSH settings panel. Skills are read from disk: browse, search, preview the body, create, rename and delete, with a backup written before every removal. Global MCP servers are the real cordis.patch.yml loader rows; per-project entries mount only for sessions in that project. Each expanded card lists the tools that server actually registered, and a per-server allow/deny rule keeps only the ones you need — a blocked tool never enters the tool registry, so it costs no context on every turn. Every MCP card carries a connection status tag (connected with a tool count, retrying, gave up, activation failed) plus a one-click reconnect, so a server that fails at startup is visible instead of silently missing its tools. Imports existing configs from 26 other MCP clients.

English edition of Weiwen's Law (KISS's Law): the first and only domain-agnostic white-box causal adjudication middleware for AI agents. Unlike domain-specific causal-inference tooling (statistics/economics/ML) or evidence-gated agent firewalls, it adjudicates *every* tool call along a deterministic structural causal chain (R->S->D->H->M) before execution - across any domain, with no training or per-domain tuning. It blocks destructive and credential-file operations, escalates repeated boundary violations, cuts faulting links until verified-fixed, and gives risk verdicts on un-auditable execution; runs fully local with zero API cost.

Content-degradation detection (thinking loops, output repetition) for DeepSeek Harness, with a continue-vs-handoff cost verdict and zero-model handoff documents.