Use Nowledge Mem for cross-tool startup context, recall of prior decisions, durable learnings, explicit conversation saving, and connection diagnosis. Use only the connection selected for this DeepChat agent or session.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Use the enabled nowledge-mem-connection MCP tools for the server configured in Plugins >
Nowledge Mem. Keep that destination, returned identity and Space for the whole operation. Never mix results from
different Mem servers or silently retry on another server.
At the beginning of related work, read Context Bundle once (read_context_bundle, source_app
deepchat) for identity, scope, rules and Working Memory. Use read_working_memory only when the
bundle tool is unavailable. An empty Working Memory is a successful empty result.
For continuation, reviews, regressions and prior decisions, search memory_search with a narrow
query and limit 5, then fetch the relevant exact IDs. Search before adding a duplicate. Distinguish
recorded facts from proposals, and keep scope returned by the server. A graph is optional; only
show one when it enforces the same identity and Space as retrieval.
Save a concise durable learning with memory_add or update an existing memory only when it is
useful beyond this conversation. State what was saved. Do not save speculative conclusions as
facts. Save complete conversations only when the user requests it: use DeepChat's conversation
export to Nowledge Mem, which uses the same verified connection as MCP. Do not
claim an export happened without a successful acknowledgement.
For configuration or failed access, direct the user to Plugins > Nowledge Mem. The host verifies
REST and MCP and stores credentials securely. Report the failing connection and operation; do not
switch to localhost after a remote error. Do not ask for keys in chat, invoke unscoped nmem, edit
~/.nowledge-mem/config.json, read local Mem database files, or install another connector. This
plugin uses DeepChat's HTTP client and does not require the CLI.
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Add a DeepChat LLM provider through explicit reviewed source changes. Use when a developer asks Codex to add a provider, provider profile, upstream provider config, model catalog mapping, provider auth behavior, or a special provider adapter in this repository.
日本語の概要は準備中です。原文の説明を表示しています。
Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.
日本語の概要は準備中です。原文の説明を表示しています。
Comprehensive code review assistant that analyzes code quality, security, and best practices
日本語の概要は準備中です。原文の説明を表示しています。
Drive native desktop apps through DeepChat's built-in Computer Use tools. Use when the user asks to operate, inspect, automate, or perform a GUI task in a real desktop application.
日本語の概要は準備中です。原文の説明を表示しています。
Use DeepChat's bundled CLI control plane for model inference, image/video/speech generation, transcription, OCR, artifact inspection, public configuration, Skills, and MCP operations. Activate when a user asks to invoke DeepChat capabilities that are not already exposed as a more specific tool, compare models, run a benchmark, inspect DeepChat runtime state, or manage DeepChat through the CLI.
日本語の概要は準備中です。原文の説明を表示しています。
Help developers build third-party tools that import, inspect, migrate, or analyze DeepChat data. Use when Codex needs to work with DeepChat provider configuration, model configuration, MCP/app settings, sessions, messages, legacy chat data, `agent.db`, `chat.db`, SQLCipher encrypted SQLite, Electron safeStorage wrapped passwords, Tauri importers, or native macOS/Windows/Linux data access.
日本語の概要は準備中です。原文の説明を表示しています。