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nowledge-mem
Use Nowledge Mem from Kimi Code for startup context, memory search, durable saves, thread search, and Kimi Code transcript import.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
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Use Nowledge Mem from Kimi Code for startup context, memory search, durable saves, thread search, and Kimi Code transcript import.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
Use Nowledge Mem from WorkBuddy or CodeBuddy for startup context, memory search, durable saves, thread search, and WorkBuddy/CodeBuddy transcript import.
Read your daily Working Memory briefing to understand current context. Contains active focus areas, priorities, unresolved flags, and recent knowledge changes. Load this automatically at the beginning of sessions for cross-tool continuity.
Search memory store when past insights would improve response. Recognize when user's stored breakthroughs, decisions, or solutions are relevant. Search proactively based on context, not just explicit requests.
Check Nowledge Mem setup, detect your agent, and guide native connector setup. Use when the user asks about setup, configuration, or when memory tools aren't working as expected.
Save decisions, insights, preferences, and procedures as durable memories. Fires when the conversation produces knowledge worth keeping across sessions.
Load today's Working Memory briefing at session start. Shows your current focus areas, priorities, and recent knowledge changes across all AI tools.
| name | nowledge-mem |
| description | Use Nowledge Mem from Kimi Code for startup context, memory search, durable saves, thread search, and Kimi Code transcript import. |
Nowledge Mem is the user's cross-tool memory. Use it to start with the right context, recall prior work, save durable decisions, and make Kimi Code sessions searchable from other AI tools.
At the beginning of a meaningful session, or when resuming work, read Context Bundle if the Nowledge Mem MCP server is connected. It includes owner context, AI Identity, active rules, active space, and Working Memory.
If MCP is not connected, use the CLI fallback:
nmem --json context --source-app kimi-code
If that fails on an older nmem, use:
nmem --json wm read
Do not read both Context Bundle and Working Memory unless the user asks. Summarize only the parts relevant to the current task.
If nmem exists but rejects a Kimi Code command, flag, or MCP host helper, treat it as an outdated CLI rather than a broken Mem server. Check nmem --version, refresh the CLI from the same source, then retry. For the desktop-bundled CLI, ask the user to open Mem and run Settings -> Preferences -> Developer Tools -> Install bundled CLI. For standalone installs, use python3 -m pip install --user --upgrade nmem-cli or pipx upgrade nmem-cli.
Search memory when the user references prior work, asks for rationale, resumes a named project, investigates a regression, or asks about something that may already have been decided.
Prefer MCP when available:
memory_search for durable decisions, preferences, procedures, and learnings.thread_search when the user asks about prior conversations.thread_fetch_messages only after a thread result is relevant.CLI fallback:
nmem --json m search "what to look up"
nmem --json t search "conversation to find" --source kimi-code -n 5
For broad browsing across memories, threads, wiki pages, and artifacts, use the Knowledge Filesystem through MCP mem_fs when available, or:
nmem fs recall "topic" --in /memories -k 5
nmem fs grep "exact phrase" /threads
When a meaningful decision, reusable procedure, user preference, correction, or non-obvious lesson appears, save it. Search first to avoid duplicates.
Prefer MCP:
memory_search for an existing memory.memory_update if the existing memory should evolve.memory_add for a new durable memory.CLI fallback:
nmem --json m search "existing concept"
nmem --json m add "content" -t "Title" --unit-type decision -l "label" -s kimi-code -i 0.8
Use one strong memory instead of several weak notes.
Real thread sync is local to the machine where Kimi Code stores its session files. MCP is not the transcript-import layer.
If the user explicitly asks to save or import Kimi Code conversations, use:
nmem --json t sync --from kimi-code --session-id <session-id> --apply
To backfill older Kimi Code sessions, preview first:
nmem t sync --from kimi-code --limit 20
Then import:
nmem t sync --from kimi-code --apply
This works for local and remote Nowledge Mem because nmem reads local Kimi Code files and uploads normalized threads to the configured Mem server.
When setup seems broken or the user asks whether Mem is connected:
nmem --json status
If nmem --json status works but Kimi-specific commands fail, do not keep using the old CLI. Upgrade it first, then rerun the failed command.
If the desktop app is on the same machine, nmem usually comes from the app. If Kimi Code runs on another machine, install the standalone CLI:
python3 -m pip install --user nmem-cli
If the host process has NMEM_AGENT_ID, NMEM_HOST_AGENT_ID, or NMEM_SPACE, let nmem use those environment variables. Do not treat source_app=kimi-code as an AI Identity; it is only provenance.
For personal Kimi Code behavior, use Kimi's own AGENTS.md surface under $KIMI_CODE_HOME/AGENTS.md or the project instructions. Do not edit installed plugin files under $KIMI_CODE_HOME/plugins/managed/.