Plugins

Four read-only tools over a WeKnora knowledge base: list knowledge bases, hybrid passage search, reassemble one document's chunks in order, and WeKnora's own cited RAG or ReAct-agent answer with a resumable session id.

WeKnora knowledge retrieval tools for DeepSeek Harness (dsh): semantic search, document reading and RAG/agent answers over your own knowledge bases.

Config-only bundle that mounts Ouroboros through the DSH MCP client, exposing 36 interview, Seed, execution, evaluation, and evolution workflow tools in DSH.

An agent preset for evidence-checked research: every step advances only through system-verified observations, L3+ claims go to an independent evaluator, and the session folds into a replayable ledger that grows into a browsable domain ontology. Requires DSH >=0.1.7-alpha.1.

千星沙箱模拟器：Lua 驱动的 2D 奇域外置沙箱 ，支持 DeepSeek Harness、Web 和 MCP

Quantitative R&D toolkit for DeepSeek Harness — 59 tools across six domains covering market data, indicators, factor evaluation, walk-forward ML validation, risk (VaR/CVaR/drawdown/Beta with Kupiec POF), options, bonds, FICC and fund simulation, with an end-to-end PDAT→PET research pipeline.

Controls your real Chrome from DSH over a local WebSocket bridge: tab management, navigation, DOM snapshots, page JS evaluation, clicks, typing, screenshots, waits and dialog handling behind a Settings toggle.

Controls your real Chrome from DSH over a local WebSocket bridge: tab management, navigation, DOM snapshots, page JS evaluation, clicks, typing, screenshots, waits and dialog handling behind a Settings toggle.

Local-first A-share research workbench for DeepSeek Harness: market dashboards, watchlists, valuation, four investor agents, versioned reports, and continuous post-report chat.

Host-side DSH evaluation control plugin: injects one evaluation trial's variables, budget limits, session root and fork lineage into the agent process, routes its model calls through a host broker under a gateway lease, and writes a schema-versioned bundle descriptor for evidence attribution.

Rewrites outgoing /chat/completions requests with an evolved chain-of-thought scaffold (nine-dimension genome, elitist selection with fragment crossover, one deterministic re-evaluation pass), scores answers against a fixed 20-objective baseline, and appends one line per rewrite to live-requests.jsonl in the plugin data root (default <cwd>/.aecot, overridable with AECOT_ROOT).

OMK — Observe. Measure. Know. Evidence-backed knowledge changes for AI applications.

Evaluation harness for DSH plugins: YAML cases drive real headless agent runs, assert on tool calls, args, results and token usage, with a baseline gate for CI regression.

Persona engine for DeepSeek Harness: one config.json drives the AI's self-name (per model, per tier), how it addresses you, relationship stance, character text, per-item work contracts with individual toggles, thinking-chain language, appearance and reply tone (both opt-in and per-model overridable), and long-term memory gated by human confirmation - the AI can only append proposed candidates, a human confirm line flips them into effect, and the log is physically append-only and replayable. Confirmed entries are injected as a quoted data-not-instructions block; entries tagged with a kind can instead be routed to arbitrary files on confirmation and never reach the prompt. Three prompt sections re-evaluate from disk on every assembly, so edits apply on the next step with no restart. Persona presets are shareable JSON snapshots (imports and exports SillyTavern character cards, v2/v3 read, v2 written) and deliberately never carry memory. Zero runtime dependencies, no network, no shell; any error degrades to an empty section rather than a broken session. Ships blank - installing changes nothing until you write something.

Self-evolving team memory as plain Markdown in git: a native Cordis plugin that registers 38 bare-name memory tools plus a prompt-signals section re-evaluated at every assembly, with RPE reinforcement, decay and sleep consolidation; shares one data repo with its Claude Code (MCP) and OpenClaw hosts; verified against DSH 0.1.0-rc.6.

Self-evolving team memory as plain Markdown in git: a native Cordis plugin that registers 38 bare-name memory tools plus a prompt-signals section re-evaluated at every assembly, with RPE reinforcement, decay and sleep consolidation; shares one data repo with its Claude Code (MCP) and OpenClaw hosts; verified against DSH 0.1.0-rc.6.

Safe math expression evaluator, zero-dependency recursive-descent parser.

Evidence-first self-evolution controller: bounded Cordis candidate generation, one-shot real-Loader admission, Harbor evaluation, and a crash-resumable journaled lineage.

A visible Chrome window per DSH session: 16 chrome_* tools (open/status/close/navigate/tabs/snapshot/screenshot/click/click_at/fill/type/press_key/hover/scroll/evaluate/wait), a Chrome tab in the Web GUI with a live screencast view (screenshot-heartbeat fallback for idle pages), tab management, window controls and a screenshot history.

Data asset valuation plugin with cost-based (replacement cost) and income-based valuation.

Drives a locally installed Moli headless browser from DeepSeek Harness: rendered fetch, JavaScript evaluation, Markdown, semantic trees, screenshots and PDFs. A CSS selector returns one container rather than the whole page (measured 40-80x fewer tokens on listing pages) and scroll triggers lazy loading. Installs Moli when it is missing; verified with a real engine on Windows, Linux and macOS.

Lua LSP configuration plugin for DeepSeek Harness using lua-language-server.

Finance data toolkit for DSH agents: number and currency formatting (incl. Chinese wan/yi units), return and CAGR math, valuation and profitability ratios, PV/FV, risk metrics, and a data-quality checklist.

Proactive multi-model scheduling plugin: multi-source signal intake with dedup and urgency ranking, a four-way execute/defer/dismiss/ask-user decision engine, DAG plan generation with parallel model execution, quality reflection with automatic retry and model switch, long-term memory for task patterns and lessons, and strategy evolution via genetic algorithms with sandboxed evaluation and canary rollout.